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List of awesome university courses for learning Computer Science
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* Added course on principles of programming languages Added 'Principles of Programming Languages' course from Matteo Pradella at Politecnico di Milano * Removing "courses" from the post Coauthoredby: Peter Thaleikis <spekulatius@users.noreply.github.com> 


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README.md 
Awesome CS Courses
Introduction
There are a lot of hidden treasures lying within university pages scattered across the internet. This list is an attempt to bring to light those awesome CS courses which make their highquality materials, i.e. assignments, lectures, notes, readings and examinations, available online for free.
Table of Contents
 Algorithms
 Artificial Intelligence
 Computer Graphics
 CS Theory
 Introduction to CS
 Machine Learning
 Misc
 Programming Languages / Compilers
 Security
 Systems
 Statistics / Regression
Legend
  Lecture Videos
  Lecture Notes
  Assignments / Labs
  Readings
Courses
Systems
 CS 61C Great Ideas in Computer Architecture (Machine Structures) UC Berkeley
 The subjects covered in this course include: C and assembly language programming, translation of highlevel programs into machine language, computer organization, caches, performance measurement, parallelism, CPU design, warehousescale computing, and related topics.
 Lecture Videos
 Lecture Notes
 Resources
 Old Exams
 CS 107 Computer Organization & Systems Stanford University
 CS107 is the third course in Stanford's introductory programming sequence. The course will work from the C programming language down to the microprocessor to demystify the machine. With a complete understanding of how computer systems execute programs and manipulate data, you will become a more effective programmer, especially in dealing with issues of debugging, performance, portability, and robustness.
 Lecture Videos
 Assignments
 CS 140 Operating Systems Stanford University
 This class introduces the basic facilities provided in modern operating systems. The course divides into three major sections. The first part of the course discusses concurrency. The second part of the course addresses the problem of memory management. The third major part of the course concerns file systems.
 Lecture Notes
 Assignments
 6.004 Computation Structures MIT
 Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lowerlevel elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures. 4 Engineering Design Points. 6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops of series of building blocks logic gates, combinational and sequential circuits, finitestate machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples.
 Youtube Playlist
 Lecture Notes
 LabsAssignments
 CS 162 Operating Systems and Systems Programming UC Berkeley
 The purpose of this course is to teach the design of operating systems and operating systems concepts that appear in other advanced systems. Topics we will cover include concepts of operating systems, systems programming, networked and distributed systems, and storage systems, including multipleprogram systems (processes, interprocess communication, and synchronization), memory allocation (segmentation, paging), resource allocation and scheduling, file systems, basic networking (sockets, layering, APIs, reliability), transactions, security, and privacy.
 Operating Systems course by the Chair of EECS, UC Berkeley David Culler
 Lecture Videos Spring 2015 lectures
 Lecture Notes Spring 2015 lectures
 The purpose of this course is to teach the design of operating systems and operating systems concepts that appear in other advanced systems. Topics we will cover include concepts of operating systems, systems programming, networked and distributed systems, and storage systems, including multipleprogram systems (processes, interprocess communication, and synchronization), memory allocation (segmentation, paging), resource allocation and scheduling, file systems, basic networking (sockets, layering, APIs, reliability), transactions, security, and privacy.
 CS 168 Introduction to the Internet: Architecture and Protocols UC Berkeley
 This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
 Lecture Notes & Assignments
 Discussion Notes
 CS 179 GPU Programming Caltech
 This course will cover programming techniques for the GPU. The course will introduce NVIDIA's parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
 Assignments
 Lecture Notes
 CS 186 Introduction to Database Systems UC Berkeley
 In the project assignments in CS186, you will write a basic database management system called SimpleDB. For this project, you will focus on implementing the core modules required to access stored data on disk; in future projects, you will add support for various query processing operators, as well as transactions, locking, and concurrent queries.
 Lecture Videos
 Lecture Notes
 Projects
 CS 241 Systems Programming (Spring 2016) Univ of Illinois, UrbanaChampaign
 System programming refers to writing code that tasks advantage of operating system support for programmers. This course is designed to introduce you to system programming. By the end of this course, you should be proficient at writing programs that take full advantage of operating system support. To be concrete, we need to fix an operating system and we need to choose a programming language for writing programs. We chose the C language running on a Linux/UNIX operating system (which implements the POSIX standard interface between the programmer and the OS).
 Assignments
 Labs
 Github Page
 Crowd Sourced Book
 CS 425 Distributed Systems Univ of Illinois, UrbanaChampaign
 Brilliant set of lectures and reading material covering fundamental concepts in distributed systems such as Vector clocks, Consensus and Paxos. This is the 2016 version by Prof Indranil Gupta.
 Lectures
 Assignments
 CS 452 RealTime Programming University of Waterloo
 Write a realtime OS microkernel in C, and application code to operate a model train set in response to realtime sensor information. The communication with the train set runs at 2400 baud so it takes about 61 milliseconds to ask all of the sensors for data about the train's possible location. This makes it particularly challenging because a train can move about 3 centimeters in that time. One of the most challenging and timeconsuming courses at the University of Waterloo.
 Assignments
 Lecture notes
 CS 2043 Unix Tools & Scripting Cornell University
 UNIXlike systems are increasingly being used on personal computers, mobile phones, web servers, and many other systems. They represent a wonderful family of programming environments useful both to computer scientists and to people in many other fields, such as computational biology and computational linguistics, in which data is naturally represented by strings. This course provides an intensive training to develop skills in Unix command line tools and scripting that enable the accomplishment and automation of large and challenging computing tasks. The syllabus takes students from shell basics and piping, to regularexpression processing tools, to shell scripting and Python.
 Syllabus
 Lectures
 Assignments
 CS 3410 Computer System Organization and Programming Cornell University
 CS3410 provides an introduction to computer organization, systems programming and the hardware/software interface. Topics include instruction sets, computer arithmetic, datapath design, data formats, addressing modes, memory hierarchies including caches and virtual memory, I/O devices, busbased I/O systems, and multicore architectures. Students learn assembly language programming and design a pipelined RISC processor.
 Lectures
 Assignments
 CS 4410 Operating Systems Cornell University
 CS 4410 covers systems programming and introductory operating system design and implementation. We will cover the basics of operating systems, namely structure, concurrency, scheduling, synchronization, memory management, filesystems, security and networking. The course is open to any undergraduate who has mastered the material in CS3410/ECE3140.
 Syllabus
 Lectures
 CS 4414 Operating Systems University of Virginia
 A course (that) covers topics including: Analysis process communication and synchronization; resource management; virtual memory management algorithms; file systems; and networking and distributed systems. The primary goal of this course is to improve your ability to build scalable, robust and secure computing systems. It focuses on doing that by understanding what underlies the core abstractions of modern computer systems.
 Syllabus
 Lectures
 CS 5412 Cloud Computing Cornell University
 Taught by one of the stalwarts of this field, Prof Ken Birman, this course has a fantastic set of slides that one can go through. The Prof's book is also a gem and recommended as a must read in Google's tutorial on Distributed System Design
 Slides
 CSCE 3613 Operating Systems University of Arkansas (Fayetteville)  An introduction to operating systems including topics in system structures, process management, storage management, files, distributed systems, and case studies.
 CSCIUA.0202: Operating Systems (Undergrad) Operating Systems NYU
 NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process scheduling, file system, ect. It also includes some recommended reading materials. What's more, there are a series of handson lab materials, helping you easily understand OS.
 Assignments
 Lectures
 Old Exams
 CSCI 360 Computer Architecture 3 CUNY Hunter College
 A course that covers cache design, buses, memory hierarchies, processorperipheral interfaces, and multiprocessors, including GPUs.
 CSCI 493.66 UNIX System Programming (formerly UNIX Tools) CUNY Hunter College
 A course that is mostly about writing programs against the UNIX API, covering all of the basic parts of the kernel interface and libraries, including files, processes, terminal control, signals, and threading.
 CSCI 493.75 Parallel Computing CUNY Hunter College
 The course is an introduction to parallel algorithms and parallel programming in C and C++, using the Message Passing Interface (MPI) and the OpenMP application programming interface. It also includes a brief introduction to parallel architectures and interconnection networks. It is both theoretical and practical, including material on design methodology, performance analysis, and mathematical concepts, as well as details on programming using MPI and OpenMP.
 Hack the Kernel Introduction to Operating Systems SUNY University at Buffalo, NY
 This course is an introduction to operating system design and implementation. We study operating systems because they are examples of mature and elegant solutions to a difficult design problem: how to safely and efficiently share system resources and provide abstractions useful to applications.
 For the processor, memory, and disks, we discuss how the operating system allocates each resource and explore the design and implementation of related abstractions. We also establish techniques for testing and improving system performance and introduce the idea of hardware virtualization. Programming assignments provide handson experience with implementing core operating system components in a realistic development environment. Course by Dr.Geoffrey Challen
 Syllabus
 Slides
 Video lectures
 Assignments
 Old Exams
 ECE 459 Programming for Performance University of Waterloo
 Learn techniques for profiling, rearchitecting, and implementing software systems that can handle industrialsized inputs, and to design and build critical software infrastructure. Learn performance optimization through parallelization, multithreading, async I/O, vectorization and GPU programming, and distributed computing.
 Lecture slides
 ECGR4101/5101 Embedded Systems using the Renesas RX63N Processor University of North Carolina at Charlotte
 Introduction to designing microcontrollerbased embedded computer systems using assembly and C programs. Examination of Realtime Operating Systems and their impact on performance. Computer engineering applications will be emphasized.
 The goal of this course is to solidify and build upon a student’s knowledge of computer organization by presenting handson experience with microcontrollers. Students will also examine a few sensors that are used in commercial and medical products and learn how to interface them in a microcontroller system.
 Lecture Videos
 Lecture Notes
 PODC Principles of Distributed Computing ETHZurich
 Explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing in an easytoread set of lecture notes, combined with complete exercises and solutions.
 Book
 Assignments and Solutions
 SPAC Parallelism and Concurrency Univ of Washington
 Technically not a course nevertheless an awesome collection of materials used by Prof Dan Grossman to teach parallelism and concurrency concepts to sophomores at UWash
 6.824 Distributed Systems MIT
 MIT's graduatelevel DS course with a focus on fault tolerance, replication, and consistency, all taught via awesome lab assignments in Golang!
 Assignments  Just do
git clone git://g.csail.mit.edu/6.824golabs2014 6.824
 Readings
 6.828 Operating Systems MIT
 MIT's operating systems course focusing on the fundamentals of OS design including booting, memory management, environments, file systems, multitasking, and more. In a series of lab assignments, you will build JOS, an OS exokernel written in C.
 Assignments
 Lectures
 Videos Note: These are student recorded cam videos of the 2011 course. The videos explain a lot of concepts required for the labs and assignments.
 CSEP 552 Distributed Systems University of Washington
 CSEP552 is a graduate course on distributed systems. Distributed systems have become central to many aspects of how computers are used, from web applications to ecommerce to content distribution. This course will cover abstractions and implementation techniques for the construction of distributed systems, including client server computing, the web, cloud computing, peertopeer systems, and distributed storage systems. Topics will include remote procedure call, maintaining consistency of distributed state, fault tolerance, high availability, and other topics. As we believe the best way to learn the material is to build it, there will be a series of handson programming projects.
 Lectures of a previous session are available to watch.
 15213 Introduction to Computer Systems (ICS) CarnegieMellon University
 The ICS course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systemslevel issues is required. Topics covered include: machinelevel code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation.
 This is the musthave course for everyone in CMU who wants to learn some computer science no matter what major are you in. Because it's CMU (The course number is as same as the zip code of CMU)!
 Lecture Notes
 Videos
 Assignments
 15319/619 Cloud Computing (ICS) CarnegieMellon University
 This projectbased online course focuses on skill building across various aspects of cloud computing. We cover conceptual topics and provide handson experience through projects utilizing public cloud infrastructures (Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP)). The adoption of cloud computing services continues to grow across a variety of organizations and in many domains. Simply, cloud computing is the delivery of computing as a service over a network, whereby distributed resources and services are rented, rather than owned, by an end user as a utility.
 For the projects, students will work with Amazon Web Services, Microsoft Azure and Google Cloud Platform, use them to rent and provision compute resources and then program and deploy applications that run on these resources. Students will develop and evaluate virtual machine (VM) and container scaling, elasticity and load balancing solutions. In addition, students will work with cloud storage systems and learn to develop different applications using batch, iterative and stream processing frameworks. 15619 students will have to complete an extra project which entails designing and implementing a complete webservice solution for querying big data. For the extra project, the student teams are evaluated based on the cost and performance of their web service.
 Lecture Notes
 Videos
 15410 Operating System Design and Implementation CarnegieMellon University
 This awesome course covers some fundamental topics of operating system, such as process model, virtual memory concurrency, threads, locking, interrupts, preemptibility, race conditions. It also covers introductory understanding of typical OS topics (file systems, transactions, scheduling, security concepts and technologies, memory consistency)Parallel programming models / frameworks.
 Lecture Notes
 Assignments
 15418 Parallel Computer Architecture and Programming CarnegieMellon University
 The goal of this course is to provide a deep understanding of the fundamental principles and engineering tradeoffs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design.
 Assignments
 Lecture Notes
 Lecture Videos
 Readings
 15440 Distributed Systems CarnegieMellon University
 Introduction to distributed systems with a focus on teaching concepts via projects implemented in the Go programming language.
 Assignments
 15721 Database Systems CarnegieMellon University
 This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both highperformance transaction processing systems (OLTP) and largescale analytical systems (OLAP). The class will stress both efficiency and correctness of the implementation of these ideas. All class projects will be in the context of a real inmemory, multicore database system. The course is appropriate for graduate students in software systems and for advanced undergraduates with strong systems programming skills.
 Assignments
 Lecture Videos
 Readings
 15445/645 Database Systems CarnegieMellon University
 This course covers fundamental topics of DBMS, compared to CMU 15721.
 Assignments
 Lecture Videos
 Readings
 15749 Engineering Distributed Systems CarnegieMellon University
 A project focused course on Distributed Systems with an awesome list of readings
 Readings
 18447 Introduction to Computer Architecture CMU
 Very comprehensive material on Computer Architecture  definitely more than just "introduction". Online material is very userfriendly, even the recitation videos available online. This is the Spring'15 version by Prof. Onur Mutlu
 Lectures and Recitation
 Homeworks 7 HWs with answer set as well
 Readings
Programming Languages / Compilers
 CS 75 Principles of Compiler Design Swathmore College
 Modelled after the influential paper on incremental approach to compiler design, this course teaches how to build a compiler in OCaml
 Course on Github
 Notes
 CS 91 Introduction to Programming Languages Swathmore College
 CIS 194 Introduction to Haskell Penn Engineering
 Explore the joys of functional programming, using Haskell as a vehicle. The aim of the course will be to allow you to use Haskell to easily and conveniently write practical programs.
 Previous semester also available, with more exercises
 CIS 198 Rust Programming UPenn
 This course covers what makes Rust so unique and applies it to practical systems programming problems. Topics covered include traits and generics; memory safety (move semantics, borrowing, and lifetimes); Rust’s rich macro system; closures; and concurrency.
 Assignments
 CMSC 430 Introduction to Compilers Univ of Maryland
 The goal of CMSC 430 is to arm students with the ability to design, implement, and extend a programming language. Throughout the course, students will design and implement several related languages, and will explore parsing, syntax querying, dataflow analysis, compilation to bytecode, type systems, and language interoperation.
 Lecture Notes
 Assignments
 Corsopl Principles of Programming Languages Politecnico di Milano  Lecture Notes  Readings
 This course gives an overview of Scheme, Haskell and Erlang while analyzing the languages' similarities and differences. As a result, many important/prevalent concepts and how they manifest differently in different programming languages are explained. Among these are (purely) functional programming, object orientation, type inference and evaluation order.
 Thorough introduction to functional programming with no preknowledge in any functional language required.
 COS 326 Functional Programming Princeton University
 Covers functional programming concepts like closures, tailcall recursion & parallelism using the OCaml programming language
 Lectures
 Assignments
 CS 143 Compiler construction Stanford University
 CS143 is a Stanford's course in the practical and theoretical aspects of compiler construction.
 Home
 Syllabus
 Lectures
 Assignments
 CS143  2011
 CS 164 Hack your language! UC Berkeley
 Introduction to programming languages by designing and implementing domainspecific languages.
 Lecture Videos
 Code for Assignments
 CS 173 Programming Languages Brown University
 Course by Prof. Krishnamurthi (author of HtDP) and numerous other awesome books on programming languages. Uses a custom designed Pyret programming language to teach the concepts. There was an online class hosted in 2012, which includes all lecture videos for you to enjoy.
 Videos
 Assignments
 CS 223 Purely Functional Data Structures In Elm University of Chicago
 This course teaches functional reactive programming and purely functional data structures based on Chris Okazaki's book and using the Elm programming language.
 Lectures
 Assignments
 CS 240h Functional Systems in Haskell Stanford University
 Building software systems in Haskell
 Lecture Slides
 3 Assignments: Lab1, Lab2, Lab3
 CS 421 Programming Languages and Compilers Univ of Illinois, UrbanaChampaign Course that uses OCaml to teach functional programming and programming language design.
 CS 3110 Data Structures and Functional Programming Cornell University
 Another course that uses OCaml to teach alternative programming paradigms, especially functional and concurrent programming.
 Lecture Slides
 Assignments
 CS 4120 Introduction to Compilers Cornell University
 An introduction to the specification and implementation of modern compilers. Topics covered include lexical scanning, parsing, type checking, code generation and translation, an introduction to optimization, and compiletime and runtime support for modern programming languages. As part of the course, students build a working compiler for an objectoriented language.
 Syllabus
 Lectures
 Assignments
 CS 4400 Programming Languages Northeastern University
 This is a course on the study, design, and implementation of programming languages.
 The course works at two simultaneous levels: first, we will use a programming language that can demonstrate a wide variety of programming paradigms. Second, using this language, we will learn about the mechanics behind programming languages by implementing our own language(s). The two level approach usually means that we will often see how to use a certain feature, and continue by implementing it.
 Syllabus
 Lecture Notes/Resources
 CS 4610 Programming Languages and Compilers University of Virginia
 Course that uses OCaml to teach functional programming and programming language design. Each assignment is a part of an interpreter and compiler for an objectoriented language similar to Java, and you are required to use a different language for each assignment (i.e., choose 4 from Python, JS, OCaml, Haskell, Ruby).
 Lecture Notes
 Assignments
 CS 5114 Network Programming Languages Cornell University
 This course provides an introduction to the languages used to program computer networks. It will examine recent proposals based on logic, functional, and distributed languages, as well as tools for establishing correctness using automatic solvers, model checkers, and proof assistants.
 Syllabus
 Lectures
 CS 5142 Scripting Languages Cornell University
 Perl, PHP, JavaScript, VisualBasic  they are oftenrequested skills for employment, but most of us do not have the time to find out what they are all about. In this course, you learn how to use scripting languages for rapid prototyping, web programming, data processing, and application extension. Besides covering traditional programming languages concepts as they apply to scripting (e.g., dynamic typing and scoping), this course looks at new concepts rarely found in traditional languages (e.g., string interpolation, hashes, and polylingual code). Through a series of small projects, you use different languages to achieve programming tasks that highlight the strengths and weaknesses of scripting. As a side effect, you practice teaching yourself new languages.
 Syllabus
 Lectures
 Assignments
 CS 5470 Compilers University of Utah
 If you're a fan of Prof Matt's writing on his fantastic blog you ought to give this a shot. The course covers the design and implementation of compilers, and it explores related topics such as interpreters, virtual machines and runtime systems. Aside from the Prof's witty take on cheating the page has tons of interesting links on programming languages, parsing and compilers.
 Lecture Notes
 Projects
 CS 6118 Types and Semantics Cornell University
 Types and Semantics is about designing and understand programming languages, whether they be domain specific or general purpose. The goal of this class is to provide a variety of tools for designing custom (programming) languages for whatever task is at hand. Part of that will be a variety of insights on how languages work along with experiences from working with academics and industry on creating new languages such as Ceylon and Kotlin. The class focuses on types and semantics and the interplay between them. This means category theory and constructive type theory (e.g. Coq and richer variations) are ancillary topics of the class. The class also covers unconventional semantic domains such as classical linear type theory in order to both break students from conventional thinking and to provide powerful targets capable of formalizing thinks like networking protocols, resourcesensitive computation, and concurrency constructs. The class project is to design and formalize a (programming) language for a purpose of the student's choosing, and assignments are designed to ensure students have had a chance to practice applying the techniques learned in class before culminating these skills in the class project.
 Syllabus
 Lectures
 CSC 253 CPython internals: A tenhour codewalk through the Python interpreter source code University of Rochester
 Nine lectures walking through the internals of CPython, the canonical Python interpreter implemented in C. They were from the Dynamic Languages and Software Development course taught in Fall 2014 at the University of Rochester.
 CSE 341 Programming Languages University of Washington
 Covers nonimperative paradigms and languages such as Ruby, Racket, and ML and the fundamentals of programming languages.
 Lectures and Videos
 Assignments and Tests
 CSE P 501 Compiler Construction University of Washington
 Teaches understanding of how a modern compiler is structured and the major algorithms that are used to translate code from highlevel to machine language. The best way to do this is to actually build a working compiler, so there will be a significant project to implement one that translates programs written in a core subset of Java into executable x86 assembly language. The compilers themselves will use scanner and parser generator tools and the default implementation language is Java.
 Lectures
 Assignments, Tests, and Solutions
 DMFP Discrete Mathematics and Functional Programming Wheaton College
 A course that teaches discrete maths concepts with functional programming
 Lecture Videos
 Assignments
 PCPP Practical Concurrent and Parallel Programming IT University of Copenhagen
 In this MSc course you learn how to write correct and efficient concurrent and parallel software, primarily using Java, on standard sharedmemory multicore hardware.
 The course covers basic mechanisms such as threads, locks and shared memory as well as more advanced mechanisms such as parallel streams for bulk data, transactional memory, message passing, and lockfree data structures with compareandswap.
 It covers concepts such as atomicity, safety, liveness and deadlock.
 It covers how to measure and understand performance and scalability of parallel programs.
 It covers tools and methods to find bugs in concurrent programs.
 6.945 Adventures in Advanced Symbolic Programming MIT
 Taught by Gerald Sussman of SICP fame, this class deals with concepts and techniques for the design an implementation of large software systems that can be adapted to uses not anticipated by the designer. Applications include compilers, computeralgebra systems, deductive systems, and some artificial intelligence applications.
 Assignments: Extensive programming assignments, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or other "functional" language.
 Readings
 CS 696 Functional Design and Programming San Diego State University
 Covers functional programming basis using Clojure.
 Topics include testing, functional programming, immutable collections and concurrency.
 Also includes assignments covering Clojurescript, [Reagent](Reagent Github) etc.
 L28 Advanced Functional Programming University of Cambridge
 This module aims to teach students how to use the features of modern typed functional programming languages (e.g. OCaml, Haskell) to design and implement libraries and DSLs. It aims to demonstrate how such techniques can improve both correctness and efficiency. Students wishing to take the module should have some experience of a typed functional programming language and an understanding of type inference.
 This particular session was taught by a prominent OCaml programmer, open Source contributor & author of real world OCaml  Dr Anil Madhavapeddy.
Algorithms
 CS 61B Data Structures UC Berkeley
 In this course, you will study advanced programming techniques including data structures, encapsulation, abstract data types, interfaces, and algorithms for sorting and searching, and you will get a taste of “software engineering”—the design and implementation of large programs.
 Full Lecture Materials Lecture of Spring 2016. This website contains full matrials including video links, labs, homeworks, projects. Very good for selflearner. Also a good start for Java. And it includes some other useful resources for Java Documentation, Data Structure Resources, Git/GitHub and Java Development Resources. Resources
 Labs The link to labs and projects is included in the website.
 Lecture Videos
 CS 97SI Introduction to Competitive Programming Stanford University
 Fantastic repository of theory and practice problems across various topics for students who are interested to participate in ACMICPC.
 Lectures and Assignments
 CS 224 Advanced Algorithms Harvard University
 CS 224 is an advanced course in algorithm design, and topics we will cover include the word RAM model, data structures, amortization, online algorithms, linear programming, semidefinite programming, approximation algorithms, hashing, randomized algorithms, fast exponential time algorithms, graph algorithms, and computational geometry.
 Lecture Videos (Youtube)
 Assignments
 CS 261 A Second Course in Algorithms Stanford University
 Algorithms for network optimization: maxflow, mincost flow, matching, assignment, and mincut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NPcomplete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms.
 Lecture Notes, Videos & Assignments (Youtube)
 CS 473/573 Fundamental Algorithms Univ of Illinois, UrbanaChampaign
 Algorithms class covering recursion, randomization, amortization, graph algorithms, network flows and hardness. The lecture notes by Prof. Erikson are comprehensive enough to be a book by themselves. Highly recommended!
 Lecture Notes
 Labs and Exams
 CS 2150 Program & Data Representation University of Virginia
 This data structures course introduces C++, linkedlists, stacks, queues, trees, numerical representation, hash tables, priority queues, heaps, huffman coding, graphs, and x86 assembly.
 Lectures
 Assignments
 CS 4820 Introduction to Analysis of Algorithms Cornell University
 This course develops techniques used in the design and analysis of algorithms, with an emphasis on problems arising in computing applications. Example applications are drawn from systems and networks, artificial intelligence, computer vision, data mining, and computational biology. This course covers four major algorithm design techniques (greedy algorithms, divide and conquer, dynamic programming, and network flow), computability theory focusing on undecidability, computational complexity focusing on NPcompleteness, and algorithmic techniques for intractable problems, including identification of structured special cases, approximation algorithms, and local search heuristics.
 Lectures
 Syllabus
 CSCI 104 Data Structures and Object Oriented Design University of Southern California (USC)
 CSCI 135 Software Design and Analysis I
CUNY Hunter College
 It is currently an intensive introduction to program development and problem solving. Its emphasis is on the process of designing, implementing, and evaluating smallscale programs. It is not supposed to be a C++ programming course, although much of the course is spent on the details of C++. C++ is an extremely large and complex programming language with many features that interact in unexpected ways. One does not need to know even half of the language to use it well.
 Lectures and Assignments
 CSCI 235 Software Design and Analysis II CUNY Hunter College
 Introduces algorithms for a few common problems such as sorting. Practically speaking, it furthers the students' programming skills with topics such as recursion, pointers, and exception handling, and provides a chance to improve software engineering skills and to give the students practical experience for more productive programming.
 Lectures and Assignments
 CSCI 335 Software Design and Analysis III
CUNY Hunter College
 This includes the introduction of hashes, heaps, various forms of trees, and graphs. It also revisits recursion and the sorting problem from a higher perspective than was presented in the prequels. On top of this, it is intended to introduce methods of algorithmic analysis.
 Lectures and Assignments
 CSE 331 Software Design and Implementation University of Washington
 Explores concepts and techniques for design and construction of reliable and maintainable software systems in modern highlevel languages; program structure and design; programcorrectness approaches, including testing.
 Lectures, Assignments, and Exams
 CSE 373 Analysis of Algorithms Stony Brook University
 Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal book has been touted by many to be best for getting that job in Google. In addition, he's also wellknown for tutoring students in competitive programming competitions. If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course.
 Lecture Videos
 ECS 122A Algorithm Design and Analysis UC Davis
 Taught by Dan Gusfield in 2010, this course is an undergraduate introduction to algorithm design and analysis. It features traditional topics, such as Big Oh notation, as well as an importance on implementing specific algorithms. Also featured are sorting (in linear time), graph algorithms, depthfirst search, string matching, dynamic programming, NPcompleteness, approximation, and randomization.
 Syllabus
 Lecture Videos
 Assignments
 ECS 222A Graduate Level Algorithm Design and Analysis UC Davis
 This is the graduate level complement to the ECS 122A undergraduate algorithms course by Dan Gusfield in 2011. It assumes an undergrad course has already been taken in algorithms, and, while going over some undergraduate algorithms topics, focuses more on increasingly complex and advanced algorithms.
 Lecture Videos
 Syllabus
 Assignments
 6.INT Hacking a Google Interview MIT
 This course taught in the MIT Independent Activities Period in 2009 goes over common solution to common interview questions for software engineer interviews at highly selective companies like Apple, Google, and Facebook. They cover time complexity, hash tables, binary search trees, and other common algorithm topics you should have already covered in a different course, but goes more in depth on things you wouldn't otherwise learn in class like bitwise logic and problem solving tricks.
 Handouts
 Topics Covered
 6.006 Introduction to Algorithms MIT
 This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
 Lecture Videos
 Assignments
 Readings
 Resources
 Old Exams
 6.046J/18.410J Design and Analysis of Algorithms MIT
 This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divideandconquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography. This course assumes that students know how to analyze simple algorithms and data structures from having taken 6.006. It introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms.
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 Lecture Notes
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 Old Exams
 6.851 Advanced Data Structures MIT
 This is an advanced DS course, you must be done with the Advanced Algorithms course before attempting this one.
 Lectures Contains videos from sp2012 version, but there isn't much difference.
 Assignments contains the calendar as well.
 6.854/18.415J Advanced Algorithms MIT
 Advanced course in algorithms by Dr. David Karger covering topics such as amortization, randomization, fingerprinting, wordlevel parallelism, bit scaling, dynamic programming, network flow, linear programming, fixedparameter algorithms, and approximation algorithms.
 Register on NB to access the problem set and lectures.
 6.854J/18.415J Advanced Algorithms MIT
 This course is a firstyear graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, wordlevel parallelism, bit scaling, dynamic programming, network flow, linear programming, fixedparameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures. The need for efficient algorithms arises in nearly every area of computer science. But the type of problem to be solved, the notion of what algorithms are "efficient,'' and even the model of computation can vary widely from area to area. In this second class in algorithms, we will survey many of the techniques that apply broadly in the design of efficient algorithms, and study their application in a wide range of application domains and computational models. The goal is for the class to be broad rather than deep. Our plan is to touch upon the following areas. This is a tentative list of topics that might be covered in the class; we will select material adaptively based on the background, interests, and rate of progress of the students.
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 15451/651 Algorithms Carnegie Mellon University
 The required algorithms class that go in depth into all basic algorithms and the proofs behind them. This is one of the heavier algorithms curriculums on this page. Taught by Avrim Blum and Manuel Blum who has a Turing Award due to his contributions to algorithms. Course link includes a very comprehensive set of reference notes by Avrim Blum.
 16s4102 Algorithms University of Virginia
CS Theory
 CIS 500 Software Foundations University of Pennsylvania
 An introduction to formal verification of software using the Coq proof assistant. Topics include basic concepts of logic, computerassisted theorem proving, functional programming, operational semantics, Hoare logic, and static type systems.
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 CS 103 Mathematical Foundations of Computing Stanford University
 CS103 is a first course in discrete math, computability theory, and complexity theory. In this course, we'll probe the limits of computer power, explore why some problems are harder to solve than others, and see how to reason with mathematical certainty.
 Links to all lectures notes and assignments are directly on the course page
 CS 173 Discrete Structures Univ of Illinois UrbanaChampaign
 This course is an introduction to the theoretical side of computer science. In it, you will learn how to construct proofs, read and write literate formal mathematics, get a quick introduction to key theory topics and become familiar with a range of standard mathematics concepts commonly used in computer science.
 Textbook Written by the professor. Includes Instructor's Guide.
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 CS 276 Foundations of Cryptography UC Berkeley
 This course discusses the complexitytheory foundations of modern cryptography, and looks at recent results in the field such as Fully Homomorphic Encryption, Indistinguishability Obfuscation, MPC and so on.
 CS 278 Complexity Theory UC Berkeley
 A graduate level course on complexity theory that introduces P vs NP, the power of randomness, averagecase complexity, hardness of approximation, and so on.
 CS 374 Algorithms & Models of Computation (Fall 2014) University of Illinois UrbanaChampaign
 CS 498 section 374 (unofficially "CS 374") covers fundamental tools and techniques from theoretical computer science, including design and analysis of algorithms, formal languages and automata, computability, and complexity. Specific topics include regular and contextfree languages, finitestate automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth and breadthfirst search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NPcompleteness. The course also has a strong focus on clear technical communication.
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 CS 3110 Data Structures and Functional Programming Cornell University
 CS 3110 (formerly CS 312) is the third programming course in the Computer Science curriculum, following CS 1110/1112 and CS 2110. The goal of the course is to help students become excellent programmers and software designers who can design and implement software that is elegant, efficient, and correct, and whose code can be maintained and reused.
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 CS 3220 Introduction to Scientific Computing Cornell University
 In this onesemester survey course, we introduce numerical methods for solving linear and nonlinear equations, interpolating data, computing integrals, and solving differential equations, and we describe how to use these tools wisely (we hope!) when solving scientific problems.
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 CS 4300 Information Retrieval Cornell University
 Studies the methods used to search for and discover information in largescale systems. The emphasis is on information retrieval applied to textual materials, but there is some discussion of other formats.The course includes techniques for searching, browsing, and filtering information and the use of classification systems and thesauruses. The techniques are illustrated with examples from web searching and digital libraries.
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 CS 4810 Introduction to Theory of Computing Cornell University
 This undergraduate course provides a broad introduction to the mathematical foundations of computer science. We will examine basic computational models, especially Turing machines. The goal is to understand what problems can or cannot be solved in these models.
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 CS 6810 Theory of Computing Cornell University
 This graduate course gives a broad introduction to complexity theory, including classical results and recent developments. Complexity theory aims to understand the power of efficient computation (when computational resources like time and space are limited). Many compelling conceptual questions arise in this context. Most of these questions are (surprisingly?) difficult and far from being resolved. Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult). We will learn about these advances in this course. A theme will be combinatorial constructions with randomlike properties, e.g., expander graphs and errorcorrecting codes. Some examples:
 Is finding a solution inherently more difficult than verifying it?
 Do more computational resources mean more computing power?
 Is it easier to find approximate solutions than exact ones?
 Are randomized algorithms more powerful than deterministic ones?
 Is it easier to solve problems in the average case than in the worst case?
 Are quantum computers more powerful than classical ones?
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 This graduate course gives a broad introduction to complexity theory, including classical results and recent developments. Complexity theory aims to understand the power of efficient computation (when computational resources like time and space are limited). Many compelling conceptual questions arise in this context. Most of these questions are (surprisingly?) difficult and far from being resolved. Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult). We will learn about these advances in this course. A theme will be combinatorial constructions with randomlike properties, e.g., expander graphs and errorcorrecting codes. Some examples:
 CSCE 3193 Programming Paradigms University of Arkansas (Fayetteville)
 Programming in different paradigms with emphasis on object oriented programming, network programming and functional programming. Survey of programming languages, event driven programming, concurrency, software validation.
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 6.045 Great Ideas in Theoretical Computer Science MIT
 This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NPcompleteness, the power of randomness, cryptography and oneway functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.
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Introduction to CS
 CS 10 The Beauty and Joy of Computing UC Berkeley
 CS10 is UCB's introductory computer science class, taught using the beginners' draganddrop language. Students learn about history, social implications, great principles, and future of computing. They also learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests.
 Snap*!* (based on Scratch by MIT).
 Curriculum
 CS 50 Introduction to Computer Science Harvard University
 CS50x is Harvard College's introduction to the intellectual enterprises of computer science and the art of programming for majors and nonmajors alike, with or without prior programming experience. An entrylevel course taught by David J. Malan.
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 The course can also be taken from edX.
 The course's OpenCourseware resides here
 CS 61A Structure and Interpretation of Computer Programs [Python] UC Berkeley
 In CS 61A, we are interested in teaching you about programming, not about how to use one particular programming language. We consider a series of techniques for controlling program complexity, such as functional programming, data abstraction, and objectoriented programming. Mastery of a particular programming language is a very useful side effect of studying these general techniques. However, our hope is that once you have learned the essence of programming, you will find that picking up a new programming language is but a few days' work.
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 CS 61AS Structure & Interpretation of Computer Programs [Racket] UC Berkeley
 A selfpaced version of the CS61 Course but in Racket / Scheme. 61AS is a great introductory course that will ease you into all the amazing concepts that future CS courses will cover, so remember to keep an open mind, have fun, and always respect the data abstraction
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 CS 101 Computer Science 101 Stanford University
 CS101 teaches the essential ideas of Computer Science for a zeropriorexperience audience. Participants play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers.
 Lectures videos will available for free after registration.
 CS 106A Programming Methodology Stanford University
 This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: objectoriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widelyused Java programming language along with good software engineering principles.
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 All materials in a zip file
 CS 106B Programming Abstractions Stanford University
 This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java.
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 All materials in a zip file
 CS 107 Programming Paradigms Stanford University
 Topics: Advanced memory management features of C and C++; the differences between imperative and objectoriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++)
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 CS 109 Programming Practice Using Scala KAIST
 This course introduces basic concepts of programming and computer science, such as dynamic and static typing, dynamic memory allocation, objects and methods, binary representation of numbers, using an editor and compiler from the command line, running programs with arguments from the command line, using libraries, and the use of basic data structures such as arrays, lists, sets, and maps. We will use Scala for this course.
 [Lectures] (http://otfried.org/courses/cs109/index.html)
 [Assignments] (http://otfried.org/courses/cs109/index.html)
 CS 1109 Fundamental Programming Concepts Cornell University
 This course provides an introduction to programming and problem solving using a highlevel programming language. It is designed to increase your knowledge level to comfortably continue to courses CS111x. Our focus will be on generic programming concepts: variables, expressions, control structures, loops, arrays, functions, pseudocode and algorithms. You will learn how to analyze problems and convert your ideas into solutions interpretable by computers. We will use MATLAB; because it provides a productive environment, and it is widely used by all engineering communities.
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 CS 1110 Introduction to Computing Using Python Cornell University
 Programming and problem solving using Python. Emphasizes principles of software development, style, and testing. Topics include procedures and functions, iteration, recursion, arrays and vectors, strings, an operational model of procedure and function calls, algorithms, exceptions, objectoriented programming, and GUIs (graphical user interfaces). Weekly labs provide guided practice on the computer, with staff present to help. Assignments use graphics and GUIs to help develop fluency and understanding.
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 CS 1112 Introduction to Computing Using Matlab Cornell University
 Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, objectoriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
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 CS 1115 Introduction to Computational Science and Engineering Using Matlab Graphical User Interfaces Cornell University
 Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, objectoriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
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 CS 1130 Transition to OO Programming Cornell University
 Introduction to objectoriented concepts using Java. Assumes programming knowledge in a language like MATLAB, C, C++, or Fortran. Students who have learned Java but were not exposed heavily to OO programming are welcome.
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 CS 1133 Transition to Python Cornell University
 Introduction to the Python programming language. Covers the basic programming constructs of Python, including assignment, conditionals, iteration, functions, objectoriented design, arrays, and vectorized computation. Assumes programming knowledge in a language like Java, Matlab, C, C++, or Fortran.
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 CS 14102 and CS242020 Computer Science I and II for Hackers University of Utah
 An intro course in the spirit of SICP designed by Professor Matthew Flatt (one of the lead designers of Racket and author of HtDP). Mostly Racket and C, and a bit of Java, with explanations on how high level functional programming concepts relate to the design of OOP programs. Do this one before SICP if SICP is a bit too much...
 Lectures and Assignments 1
 Lectures and Assignments 2
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 Racket Language
 CS 2110 ObjectOriented Programming and Data Structures Cornell University
 CS 2110 is an intermediatelevel programming course and an introduction to computer science. Topics include program design and development, debugging and testing, objectoriented programming, proofs of correctness, complexity analysis, recursion, commonly used data structures, graph algorithms, and abstract data types. Java is the principal programming language. The course syllabus can easily be extracted by looking at the link to lectures.
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 CS 4302 Web Information Systems Cornell University
 This course will introduce you to technologies for building datacentric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining crosscutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semesterlong project in which the students demonstrate their expertise in building datacentric Web information systems.
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 CSCE 2004 Programming Foundations I University of Arkansas (Fayetteville)
 Introductory course for students majoring in computer science or computer engineering. Software development process: problem specification, program design, implementation, testing and documentation. Programming topics: data representation, conditional and iterative statements, functions, arrays, strings, file I/O, and classes. Using C++ in a UNIX environment.
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 CSCI E1 Understanding Computers and the Internet Harvard University Extension College
 This course is all about understanding: understanding what's going on inside your computer when you flip on the switch, why tech support has you constantly rebooting your computer, how everything you do on the Internet can be watched by others, and how your computer can become infected with a worm just by being turned on. Designed for students who use computers and the Internet every day but don't fully understand how it all works, this course fills in the gaps. Through lectures on hardware, software, the Internet, multimedia, security, privacy, website development, programming, and more, this course "takes the hood off" of computers and the Internet so that students understand how it all works and why. Through discussions of current events, students are exposed also to the latest technologies.
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 CSforall CS for All Harvey Mudd College
 This book (and course) takes a unique approach to “Intro CS.” In a nutshell, our objective is to provide an introduction to computer science as an intellectually rich and vibrant field rather than focusing exclusively on computer programming. While programming is certainly an important and pervasive element of our approach, we emphasize concepts and problemsolving over syntax and programming language features.
 Lectures and Other resources
 6.001 Structure and Interpretation of Computer Programs MIT
 Teaches bigpicture computing concepts using the Scheme programming language. Students will implement programs in a variety of different programming paradigms (functional, objectoriented, logical). Heavy emphasis on function composition, codeasdata, control abstraction with continuations, and syntactic abstraction through macros. An excellent course if you are looking to build a mental framework on which to hang your programming knowledge.
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 IDE
 6.005 Software Construction, Fall 2016 MIT
 This course introduces fundamental principles and techniques of software development. Students learn how to write software that is safe from bugs, easy to understand, and ready for change. Topics include specifications and invariants; testing, testcase generation, and coverage; state machines; abstract data types and representation independence; design patterns for objectoriented programming; concurrent programming, including message passing and shared concurrency, and defending against races and deadlock; and functional programming with immutable data and higherorder functions.
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Machine Learning
 DEEPNLP Deep Learning for Natural Language Processing University of Oxford
 This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. This course is organised by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.
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 Assignments are available on the organisation page titled as "practicals"
 CS20si Tensorflow for Deep Learning Research Stanford University
 This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
 Assignments available on Github.
 COMS 4771 Machine Learning Columbia University
 Course taught by Tony Jebara introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods.
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 CS 109 Data Science Harvard University
 Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.
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 2014 Lectures
 2013 Lectures (slightly better)
 CS 156 Learning from Data Caltech
 This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
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 CS 189 Introduction To Machine Learning UC Berkeley
 CS 224d Deep Learning for Natural Language Processing Stanford University
 Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single endtoend model and do not require traditional, taskspecific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cuttingedge research in deep learning applied to NLP.
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 CS 229r Algorithms for Big Data Harvard University
 Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.
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 CS 231n Convolutional Neural Networks for Visual Recognition Stanford University
 Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and selfdriving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning endtoend models for these tasks, particularly image classification. During the 10week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cuttingedge research in computer vision.
 Lecture Notes
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 Github Page
 CS 287 Advanced Robotics UC Berkeley
 The course introduces the math and algorithms underneath stateoftheart robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimizationtwo areas with wide applicability in modern Artificial Intelligence. An intended sideeffect of the course is to generally strengthen your expertise in these two areas.
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 CS 395T Statistical and Discrete Methods for Scientific Computing University of Texas
 Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++. Topics covered include probability theory and Bayesian inference; univariate distributions; Central Limit Theorem; generation of random deviates; tail (pvalue) tests; multiple hypothesis correction; empirical distributions; model fitting; error estimation; contingency tables; multivariate normal distributions; phylogenetic clustering; Gaussian mixture models; EM methods; maximum likelihood estimation; Markov Chain Monte Carlo; principal component analysis; dynamic programming; hidden Markov models; performance measures for classifiers; support vector machines; Wiener filtering; wavelets; multidimensional interpolation; information theory.
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 CS 4780 Machine Learning Cornell University
 This course will introduce you to technologies for building datacentric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining crosscutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semesterlong project in which the students demonstrate their expertise in building datacentric Web information systems.
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 CS 4786 Machine Learning for Data Science Cornell University
 An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
 Dimensionality reduction, such as principal component analysis (PCA) and the singular value decomposition (SVD), canonical correlation analysis (CCA), independent component analysis (ICA), compressed sensing, random projection, the information bottleneck. (We expect to cover some, but probably not all, of these topics).
 Clustering, such as kmeans, Gaussian mixture models, the expectationmaximization (EM) algorithm, linkbased clustering. (We do not expect to cover hierarchical or spectral clustering.).
 Probabilisticmodeling topics such as graphical models, latentvariable models, inference (e.g., belief propagation), parameter learning.
 Regression will be covered if time permits.
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 Lectures
 An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
 CVX 101 Convex Optimization Stanford University
 The course concentrates on recognizing and solving convex optimization problems that arise in applications. Topics addressed include the following. Convex sets, functions, and optimization problems. Basics of convex analysis. Leastsquares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.  Textbook  Lectures and Assignments
 DSGA 1008 Deep Learning New York University
 This increasingly popular course is taught through the Data Science Center at NYU. Originally introduced by Yann Lecun, it is now led by Zaid Harchaoui, although Prof. Lecun is rumored to still stop by from time to time. It covers the theory, technique, and tricks that are used to achieve very high accuracy for machine learning tasks in computer vision and natural language processing. The assignments are in Lua and hosted on Kaggle.
 Course Page
 Recorded Lectures
 EECS E6893 & EECS E6895 Big Data Analytics & Advanced Big Data Analytics Columbia University
 Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
 Taught by Dr. ChingYung Lin
 Course Site
 Assignments  Assignments are present in the Course Slides
 EECS E6894 Deep Learning for Computer Vision and Natural Language Processing Columbia University
 This graduate level research class focuses on deep learning techniques for vision and natural language processing problems. It gives an overview of the various deep learning models and techniques, and surveys recent advances in the related fields. This course uses Theano as the main programming tool. GPU programming experiences are preferred although not required. Frequent paper presentations and a heavy programming workload are expected.
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 EE103 Introduction to Matrix Methods Stanford University
 The course covers the basics of matrices and vectors, solving linear equations, leastsquares methods, and many applications. It'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. EE103 is based on a book that Stephen Boyd and Lieven Vandenberghe are currently writing. Students will use a new language called Julia to do computations with matrices and vectors.
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 Fast.ai Introduction to Machine Learning for Coders Fast.ai / University of San Francisco
 There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.
 Lecture Videos
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 Jupyter Notebooks
 Info 290 Analyzing Big Data with Twitter UC Berkeley school of information
 In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cuttingedge algorithms and software tools for data analytics as applied to Twitter's data. Topics include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, realtime search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
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 Previous Years coursepage
 Machine Learning: 20142015 University of Oxford
 The course focusses on neural networks and uses the Torch deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, backpropagation, convolutional neural networks, maxmargin learning, siamese networks, recurrent neural networks, LSTMs, handwriting with recurrent neural networks, variational autoencoders and image generation and reinforcement learning
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 Source code
 StatLearning Intro to Statistical Learning Stanford University
 This is an introductorylevel course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines.
 The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R which is a more approachable version of the Elements of Statistical Learning (or ESL) book.
 10601 Machine Learning Carnegie Mellon University
 This course covers the theory and practical algorithms for machine learning from a variety of perspectives. It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, marginbased learning, and Occam's Razor. Short programming assignments include handson experiments with various learning algorithms. This course is designed to give a graduatelevel student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
 Taught by one of the leading experts on Machine Learning  Tom Mitchell
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 Project Ideas and Datasets
 10708 Probabilistic Graphical Models Carnegie Mellon University
 Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides a unified view for this wide range of problems, enabling efficient inference, decisionmaking and learning in problems with a very large number of attributes and huge datasets. This graduatelevel course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
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 11785 Deep Learning Carnegie Mellon University
 The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. Lectures and Reading Notes are available on the page.
 CS246 Mining Massive Data Sets Stanford University
 The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data.
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 CS276 Information Retrieval and Web Search Stanford University
 Basic and advanced techniques for textbased information systems: efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; Web search including crawling, linkbased algorithms, and Web metadata; text/Web clustering, classification; text mining.
 Lecture notes
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 Practical_RL Reinforcement Learning in the Wild Yandex SDA
 A course on reinforcement learning in the wild. Taught oncampus in HSE and Yandex SDA (russian) and maintained to be friendly to online students (both english and russian).
 Syllabus
 UvA DEEP LEARNING UvA Deep Learning Course University of Amsterdam
 This course is taught in the MSc program in Artificial Intelligence of the University of Amsterdam. In this course we study the theory of deep learning, namely of modern, multilayered neural networks trained on big data. The course focuses particularly on computer vision and language modelling, which are perhaps two of the most recognizable and impressive applications of the deep learning theory.
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 Practicals
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Security
 CIS 4930 / CIS 5930 Offensive Computer Security Florida State University
 Course taught by W. Owen Redwood and Xiuwen Liu. It covers a wide range of computer security topics, starting from Secure C Coding and Reverse Engineering to Penetration Testing, Exploitation and Web Application Hacking, both from the defensive and the offensive point of view.
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 Assignments
 CS 155 Computer and Network Security Stanford
 Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system holes, application security (web, email, databases), viruses, social engineering attacks, privacy, and digital rights management. Course projects focus on building reliable code. Recommended: Basic Unix. Primarily intended for seniors and firstyear graduate students.
 CS 161 Computer Security UC Berkeley
 Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and languagebased security. Case studies from realworld systems.
 CS 259 Security Modeling and Analysis Stanford
 The course will cover a variety of contemporary network protocols and other systems with security properties. The course goal is to give students handson experience in using automated tools and related techniques to analyze and evaluate security mechanisms. To understand security properties and requirements, we will look at several network protocols and their properties, including secrecy, authentication, key establishment, and fairness. In parallel, the course will look at several models and tools used in security analysis and examine their advantages and limitations. In addition to fully automated finitestate model checking techniques, we will also study other approaches, such as constraint solving, process algebras, protocol logics, probabilistic model checking, game theory, and executable models based on logic programming.
 CS 261 Internet/Network Security UC Berkeley
 This class aims to provide a thorough grounding in network security suitable for those interested in conducting research in the area, as well as students more generally interested in either security or networking. We will also look at broader issues relating to Internet security for which networking plays a role. Topics include: denialofservice; capabilities; network intrusion detection; worms; forensics; scanning; traffic analysis / inferring activity; architecture; protocol issues; legality and ethics; web attacks; anonymity; honeypots; botnets; spam; the underground economy; research pitfalls. The course is taught with an emphasis on seminal papers rather than bleedingedge for a given topic.
 CS 5430 System Security Cornell University
 This course discusses security for computers and networked information systems. We focus on abstractions, principles, and defenses for implementing military as well as commercialgrade secure systems.
 Syllabus
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 CSCI 4968 Modern Binary Exploitation Rensselaer Polytechnic Institute
 This repository contains the materials as developed and used by RPISEC to teach Modern Binary Exploitation at Rensselaer Polytechnic Institute in Spring 2015. This was a university course developed and run solely by students to teach skills in vulnerability research, reverse engineering, and binary exploitation.
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 CSCI 4976 Malware Analysis Rensselaer Polytechnic Institute
 This repository contains the materials as developed and used by RPISEC to teach Malware Analysis at Rensselaer Polytechnic Institute in Fall 2015. This was a university course developed and run solely by students, primarily using the
 EECS 588 Computer & Network Security University of Michigan
 Taught by J. Alex Halderman who has analyzed the security of Electronic Voting Machines in the US and over seas.
 This intensive research seminar covers foundational work and current topics in computer systems security.
 Readings Practical Malware Analysis book by Michael Sikorski and Andrew Honig, to teach skills in reverse engineering, malicious behaviour, malware, and antianalysis techniques.
 Lectures Notes
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 6.857 Computer and Network Security MIT
 Emphasis on applied cryptography and may include: basic notion of systems security, cryptographic hash functions, symmetric cryptography (onetime pad, stream ciphers, block ciphers), cryptanalysis, secretsharing, authentication codes, publickey cryptography (encryption, digital signatures), publickey attacks, web browser security, biometrics, electronic cash, viruses, electronic voting, Assignments include a group final project. Topics may vary year to year. Lecture Notes References
 6.858 Computer Systems Security MIT
 Design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
 Taught by James Mickens and Nickolai Zeldovich
 Video Lectures and Labs
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 18636 Browser Security Stanford
 The Web continues to grow in popularity as platform for retail transactions, financial services, and rapidly evolving forms of communication. It is becoming an increasingly attractive target for attackers who wish to compromise users' systems or steal data from other sites. Browser vendors must stay ahead of these attacks by providing features that support secure web applications. This course will study vulnerabilities in existing web browsers and the applications they render, as well as new technologies that enable web applications that were never before possible. The material will be largely based on current research problems, and students will be expected to criticize and improve existing defenses. Topics of study include (but are not limited to) browser encryption, JavaScript security, plugin security, sandboxing, web mashups, and authentication.
Artificial Intelligence
 CS 188 Introduction to Artificial Intelligence UC Berkeley
 This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decisiontheoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
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 CS 4700 Foundations of Artificial Intelligence Cornell University
 This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a projectbased course, in which you will implement several computer vision algorithms throughout the semester.
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 CS 6700 Advanced Artificial Intelligence Cornell University
 6.868J The Society of Mind MIT
 This course is an introduction, by Prof. Marvin Minsky, to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. commonsense theories of learning.
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Computer Graphics
 CAP 5415 Computer Vision University of Central Florida
 An introductory level course covering the basic topics of computer vision, and introducing some fundamental approaches for computer vision research.
 Lectures and Videos
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 CIS 581 Computer Vision and Computational Photography University of Pennsylvania
 An introductory course in computer vision and computational photography focusing on four topics: image features, image morphing, shape matching, and image search.
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 CMU 462 Computer Graphics Carnegie Mellon University
 This course provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their crosscutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, highperformance ray tracing, differential equations, time integration, numerical differentiation, physicallybased animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, examplebased synthesis.
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 CS 378 3D Reconstruction with Computer Vision UTexas
 In this labbased class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use opensource frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying stateofthe art approaches to geometric computer vision
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 CS 4620 Introduction to Computer Graphics Cornell University
 The study of creating, manipulating, and using visual images in the computer.
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 Exams
 CS 4670 Introduction to Computer Vision Cornell University
 This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a projectbased course, in which you will implement several computer vision algorithms throughout the semester.
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 CS 6670 Computer Vision Cornell University
 Introduction to computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project.
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 CSCI 1230 Introduction to Computer Graphics Brown University
 This course offers an indepth exploration of fundamental concepts in 2D and 3D computer graphics. It introduces 2D raster graphics techniques, including scan conversion, simple image processing, interaction techniques and user interface design. The bulk of the course is devoted to 3D modeling, geometric transformations, and 3D viewing and rendering.
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 CSCIGA.2270001 Graduate Computer Graphics New York University
 Stepbystep study computer graphics, with reading and homework at each lecture (Fall2015)
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Misc
 AM 207 Monte Carlo Methods and Stochastic Optimization Harvard University
 This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. All of this in Python!
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 CS 75 Introduction to Game Development Tufts University
 The course taught by Ming Y. Chow teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game physics, sprites, animation, game development methodology, sound, testing, MMORPGs and online games, and addressing mobile development in Android, HTML5, and iOS. Most to all of the development is focused on PyGame for learning principles
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 CS 50 Intro to Game Developement Harvard University
 This course picks up where Harvard College’s CS50 leaves off, focusing on the development of 2D and 3D interactive games. Students explore the design of such childhood games as Super Mario Bros., Legend of Zelda, and Portal in a quest to understand how video games themselves are implemented. Via lectures and handson projects, the course explores principles of 2D and 3D graphics, animation, sound, and collision detection using frameworks like Unity and LÖVE 2D, as well as languages like Lua and C#. By class’s end, students will have programmed several of their own games and gained a thorough understanding of the basics of game design and development.
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 CS 100 Open Source Software Construction UC Riverside
 This is a course on how to be a hacker. Your first four homework assignments walk you through the process of building your own unix shell. You'll be developing it as an open source project, and you will collaborate with each other at various points.
 Github Page
 Assignments
 CS 108 Object Oriented System Design Stanford
 Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams.
 CS 168 Computer Networks UC Berkeley
 This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python).
 You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.
 CS 193a Android App Development, Spring 2016 Stanford University
 Course Description: This course provides an introduction to developing applications for the Android mobile platform.
 Prerequisite: CS 106B or equivalent. Java experience highly recommended. OOP highly recommmended.
 Devices: Access to an Android phone and/or tablet recommended but not required.
 Videos: Videos list can be found here
 Other materials: Some codes, handsout, homework ..... and lecture notes are not downloadable on the site due to login requirement. Please head to my Github repo here to download them.
 CS 193p Developing Applications for iOS Stanford University
 Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multitouch technologies. Objectoriented design using modelviewcontroller paradigm, memory management, ObjectiveC programming language. Other topics include: objectoriented database API, animation, multithreading and performance considerations.
 Prerequisites: C language and objectoriented programming experience
 Recommended: Programming Abstractions
 Updated courses for iOS8  Swift
 Updated courses for iOS9  Swift
 CS 223A Introduction to Robotics Stanford University
 The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.
 CS 262a Advanced Topics in Computer Systems UC Berkeley
 CS262a is the first semester of a yearlong sequence on computer systems research, including operating systems, database systems, and Internet infrastructure systems. The goal of the course is to cover a broad array of research topics in computer systems, and to engage you in topflight systems research. The first semester is devoted to basic thematic issues and underlying techniques in computer systems, while the second semester goes deeper into topics related to scalable, parallel and distributed systems. The class is based on a discussion of important research papers and a research project.
 Parts: Some Classics, Persistent Storage, Concurrency, HigherLevel Models, Virtual Machines, Cloud Computing, Parallel and Distributed Computing, Potpourri.
 Prerequisites: The historical prerequisite was to pass an entrance exam in class, which covered undergraduate operating systems material (similar to UCB's CS162). There is no longer an exam. However, if you have not already taken a decent undergrad OS class, you should talk with me before taking this class. The exam had the benefit of "paging in" the undergrad material, which may have been its primary value (since the pass rate was high).
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 CS 294 Cuttingedge Web Technologies Berkeley
 Want to learn what makes future web technologies tick? Join us for the class where we will dive into the internals of many of the newest web technologies, analyze and dissect them. We will conduct survey lectures to provide the background and overview of the area as well as invite guest lecturers from various leading projects to present their technologies.
 CS 411 Software Architecture Design Bilkent University
 This course teaches the basic concepts, methods and techniques for designing software architectures. The topics include: rationale for software architecture design, modeling software architecture design, architectural styles/patterns, architectural requirements analysis, comparison and evaluation of architecture design methods, synthesisbased software architecture design, software productline architectures, domain modeling, domain engineering and application engineering, software architecture implementation, evaluating software architecture designs.
 CS 3152 Introduction to Computer Game Development Cornell University
 A projectbased course in which programmers and designers collaborate to make a computer game. This course investigates the theory and practice of developing computer games from a blend of technical, aesthetic, and cultural perspectives. Technical aspects of game architecture include software engineering, artificial intelligence, game physics, computer graphics, and networking. Aesthetic and cultural include art and modeling, sound and music, game balance, and player experience.
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 CS 4152 Advanced Topics in Computer Game Development Cornell University
 Projectbased followup course to CS/INFO 3152. Students work in a multidisciplinary team to develop a game that incorporates innovative game technology. Advanced topics include 3D game development, mobile platforms, multiplayer gaming, and nontraditional input devices. There is a special emphasis on developing games that can be submitted to festivals and competitions, or that can be commercialized.
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 CS 4154 Analyticsdriven Game Design Cornell University
 A projectbased course in which programmers and designers collaborate to design, implement, and release a video game online through popular game portals. In this course, students will use the internet to gather data anonymously from players. Students will analyze this data in order to improve their game over multiple iterations. Technical aspects of this course include programming, database architecture, and statistical analysis.
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 CS 4812 Quantum Information Processing Cornell University
 Hardware that exploits quantum phenomena can dramatically alter the nature of computation. Though constructing a working quantum computer is a formidable technological challenge, there has been much recent experimental progress. In addition, the theory of quantum computation is of interest in itself, offering strikingly different perspectives on the nature of computation and information, as well as providing novel insights into the conceptual puzzles posed by the quantum theory. The course is intended both for physicists, unfamiliar with computational complexity theory or cryptography, and also for computer scientists and mathematicians, unfamiliar with quantum mechanics. The prerequisites are familiarity (and comfort) with finite dimensional vector spaces over the complex numbers, some standard group theory, and ability to count in binary.
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 CS 4860 Applied Logic Cornell University
 In addition to basic firstorder logic, when taught by Computer Science this course involves elements of Formal Methods and Automated Reasoning. Formal Methods is concerned with proving properties of algorithms, specifying programming tasks and synthesizing programs from proofs. We will use formal methods tools such as interactive proof assistants (see www.nuprl.org). We will also spend two weeks on constructive type theory, the language used by the Coq and Nuprl proof assistants.
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 CS 5150 Software Engineering Cornell University
 Introduction to the practical problems of specifying, designing, building, testing, and delivering reliable software systems
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 CS 5220 Applications of Parallel Computers Cornell University
 How do we solve the largescale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics coprocessors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers. Topics include:
 Singleprocessor architecture, caches, and serial performance tuning
 Basics of parallel machine organization
 Distributed memory programming with MPI
 Shared memory programming with OpenMP
 Parallel patterns: data partitioning, synchronization, and load balancing
 Examples of parallel numerical algorithms
 Applications from science and engineering
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 How do we solve the largescale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics coprocessors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers. Topics include:
 CS 5540 Computational Techniques for Analyzing Clinical Data Cornell University
 CS5540 is a masterslevel course that covers a wide range of clinical problems and their associated computational challenges. The practice of medicine is filled with digitally accessible information about patients, ranging from EKG readings to MRI images to electronic health records. This poses a huge opportunity for computer tools that make sense out of this data. Computation tools can be used to answer seemingly straightforward questions about a single patient's test results (“Does this patient have a normal heart rhythm?”), or to address vital questions about large populations (“Is there any clinical condition that affects the risks of Alzheimer”). In CS5540 we will look at many of the most important sources of clinical data and discuss the basic computational techniques used for their analysis, ranging in sophistication from current clinical practice to stateoftheart research projects.
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 CS 5724 Evolutionary Computation Cornell University
 This course will cover advanced topics in evolutionary algorithms and their application to openended computational design. The field of evolutionary computation tries to address largescale optimization and planning problems through stochastic populationbased methods. It draws inspiration from evolutionary processes in nature and in engineering, and also serves as abstract models for these phenomena. Evolutionary processes are generally weak methods that require little information about the problem domain and hence can be applied across a wide variety of applications. They are especially useful for openended problem domains for which little formal knowledge exists and the number of parameters is undefined, such as for the general engineering design process. This course will provide insight to a variety of evolutionary computation paradigms, such as genetic algorithms, genetic programming, and evolutionary strategies, as well as governing dynamics of coevolution, arms races and mediocre stable states. New methods involving symbiosis models and pattern recognition will also be presented. The material will be intertwined with discussions of representations and results for design problems in a variety of problem domains including software, electronics, and mechanics.
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 CS 6452 Datacenter Networks and Services Cornell University
 CS6452 focuses on datacenter networks and services. The emerging demand for web services and cloud computing have created need for large scale data centers. The hardware and software infrastructure for datacenters critically determines the functionality, performance, cost and failure tolerance of applications running on that datacenter. This course will examine design alternatives for both the hardware (networking) infrastructure, and the software infrastructure for datacenters.
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 CS 6630 Realistic Image Synthesis Cornell University
 CS6630 is an introduction to physicsbased rendering at the graduate level. Starting from the fundamentals of light transport we will look at formulations of the Rendering Equation, and a series of Monte Carlo methods, from sequential sampling to multiple importance sampling to Markov Chains, for solving the equation to make pictures. We'll look at light reflection from surfaces and scattering in volumes, illumination from luminaries and environments, and diffusion models for translucent materials. We will build working implementations of many of the algorithms we study, and learn how to make sure they are actually working correctly. It's fun to watch integrals and probability distributions transform into photographs of a slightly too perfect synthetic world.
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 CS 6640 Computational Photography Cornell University
 A course on the emerging applications of computation in photography. Likely topics include digital photography, unconventional cameras and optics, light field cameras, image processing for photography, techniques for combining multiple images, advanced image editing algorithms, and projectorcamera systems.cornell.edu/courses/CS6630/2012sp/about.stm)
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 CS 6650 Computational Motion Cornell University
 Covers computational aspects of motion, broadly construed. Topics include the computer representation, modeling, analysis, and simulation of motion, and its relationship to various areas, including computational geometry, mesh generation, physical simulation, computer animation, robotics, biology, computer vision, acoustics, and spatiotemporal databases. Students implement several of the algorithms covered in the course and complete a final project. This offering will also explore the special role of motion processing in physically based sound rendering.
 CS 6840 Algorithmic Game Theory Cornell University
 Algorithmic Game Theory combines algorithmic thinking with gametheoretic, or, more generally, economic concepts. The course will study a range of topics at this interface
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 CSE 154 Web Programming University of Washington
 This course is an introduction to programming for the World Wide Web. Covers use of HTML, CSS, PHP, JavaScript, AJAX, and SQL.
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 ESM 2964F GIS & Spatial Analysis UC Santa Barbara
 Taught by James Frew, Ben Best, and Lisa Wedding
 Focuses on specific computational languages (e.g., Python, R, shell) and tools (e.g., GDAL/OGR, InVEST, MGET, ModelBuilder) applied to the spatial analysis of environmental problems
 GitHub (includes lecture materials and labs)
 ICS 314 Software Engineering University of Hawaii
 Taught by Philip Johnson
 Introduction to software engineering using the "Athletic Software Engineering" pedagogy
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 IGME 582 Humanitarian Free & Open Source Software Development Rochester Institute of Technology
 This course provides students with exposure to the design, creation and production of Open Source Software projects. Students will be introduced to the historic intersections of technology and intellectual property rights and will become familiar with Open Source development processes, tools and practices.
 I485 / H400 Biologically Inspired Computation Indiana University
 Course taught by Luis Rocha about the multidisciplinary field algorithms inspired by naturally occurring phenomenon. This course provides introduces the following areas: Lsystems, Cellular Automata, Emergence, Genetic Algorithms, Swarm Intelligence and Artificial Immune Systems. It's aim is to cover the fundamentals and enable readers to build up a proficiency in applying various algorithms to realworld problems.
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 Open Sourced Elective: Database and Rails Intro to Ruby on Rails University of Texas
 An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman.
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 SCICOMP An Introduction to Efficient Scientific Computation Universität Bremen
 This is a graduate course in scientific computing created and taught by Oliver Serang in 2014, which covers topics in computer science and statistics with applications from biology. The course is designed topdown, starting with a problem and then deriving a variety of solutions from scratch.
 Topics include memoization, recurrence closed forms, string matching (sorting, hash tables, radix tries, and suffix tries), dynamic programming (e.g. SmithWaterman and NeedlemanWunsch), Bayesian statistics (e.g. the envelope paradox), graphical models (HMMs, Viterbi, junction tree, belief propagation), FFT, and the probabilistic convolution tree.
 Lecture videos on Youtube and for direct download
 14740 Fundamentals of Computer Networks CMU
 This is an introductory course on Networking for graduate students. It follows a topdown approach to teaching Computer Networks, so it starts with the Application layer which most of the students are familiar with and as the course unravels we learn more about transport, network and link layers of the protocol stack.
 As far as prerequisites are concerned  basic computer, programming and probability theory background is required.
 The course site contains links to the lecture videos, reading material and assignments.
Statistics

STAT 340 Applied Regression Methods Smith College

This course covers the beautiful and useful tool of regression, which is the central technique of statistical modeling. By the end of this course you will be able to:
 Discern research questions and data that are wellsuited to regression analysis.
 Conduct thorough exploratory data analysis of data in multiple dimensions.
 Understand the mathematical foundations of regression.
 Perform regression analysis in a modern computing environment.
 Interpret your model and communicate what it implies.