- [18-447](http://www.ece.cmu.edu/~ece447/s14/doku.php?id=start) **Introduction to Computer Architecture***CMU*
- Very comprehensive material on Computer Architecture - definitely more than just "introduction". Online material is very user-friendly, even the recitation videos available online. This is the Spring'14 version by professor [Onur Mutlu ](http://users.ece.cmu.edu/~omutlu/).
- [Lectures and Recitation](http://www.ece.cmu.edu/~ece447/s14/doku.php?id=schedule)
- [Homeworks](http://www.ece.cmu.edu/~ece447/s14/doku.php?id=homeworks) 7 HWs with answer set as well
- Write a real-time OS microkernel in C, and application code to operate a model train set in response to real-time 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 time-consuming courses at the University of Waterloo.
- 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.
- [SPAC](http://homes.cs.washington.edu/~djg/teachingMaterials/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
- Explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing in an easy-to-read set of lecture notes, combined with complete exercises and solutions.
- 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](http://www.amazon.com/Guide-Reliable-Distributed-Systems-High-Assurance/dp/1447124154) is also a gem and recommended as a must read in Google's tutorial on [Distributed System Design](http://www.hpcs.cs.tsukuba.ac.jp/~tatebe/lecture/h23/dsys/dsd-tutorial.html)
- 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.
- 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.
- 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 de-mystify 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.
- [ECE 459](http://patricklam.ca/p4p/) **Programming for Performance***University of Waterloo*
- Learn techniques for profiling, rearchitecting, and implementing software systems that can handle industrial-sized 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.
- 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.
- Course by Prof. Krishnamurthi (author of [HtDP](http://htdp.org/2003-09-26/Book/)) and numerous other [awesome](http://cs.brown.edu/courses/cs173/2012/book/) [books](http://papl.cs.brown.edu/2014/index.html) on programming languages. Uses a custom designed [Pyret](http://www.pyret.org/) programming language to teach the concepts. There was an [online class](http://cs.brown.edu/courses/cs173/2012/OnLine/) hosted in 2012, which includes all lecture videos for you to enjoy.
- If you're a fan of Prof Matt's writing on his [fantastic blog](http://matt.might.net/articles/) 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](http://matt.might.net/teaching/compilers/spring-2015/#collaboration) the page has tons of interesting links on programming languages, parsing and compilers.
- Course that uses OCaml to teach functional programming and programming language design. Each assignment is a part of an interpreter and compiler for an object-oriented 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).
- [CSE-373](http://www3.cs.stonybrook.edu/~skiena/373/) **Analysis of Algorithms***Stony Brook University*
- Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal [book](http://www.algorist.com/) has been touted by many to be best for [getting that job in Google](http://steve-yegge.blogspot.com/2008/03/get-that-job-at-google.html). In addition, he's also well-known for tutoring students in competitive [programming competitions](http://www.programming-challenges.com/pg.php?page=index). If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course.
- 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.
- [COS226](http://www.cs.princeton.edu/courses/archive/fall14/cos226/info.php) **Data Structures and Algorithms***Princeton University*
- The [popular](https://www.coursera.org/course/algs4partI) algorithms class covering most important algorithms and data structures in use on computers taught by Robert Sedgewick.
- 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.
- [CSCI 135](http://compsci.hunter.cuny.edu/~sweiss/courses/csci135.php) **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 small-scale 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.
- 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.
- 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.
- 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!
- Advanced course in algorithms by Dr. David Karger covering topics such as amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms.
- **Register** on [NB](http://nb.mit.edu/subscribe?key=D3a8CYpoO2VcR1ZcfaxmR5KbyjCGXd3INNXvL3mxEakYJ7qGJw) to access the [problem set and lectures](http://nb.mit.edu/).
- [6.851](http://courses.csail.mit.edu/6.851/spring14/index.html) **Advanced Data Structures***MIT*
- This is an advanced DS course, you must be done with the [Advanced Algorithms](http://courses.csail.mit.edu/6.854/current/) course before attempting this one.
- [Lectures](http://courses.csail.mit.edu/6.851/spring14/lectures/) Contains videos from sp2012 version, but there isn't much difference.
- [Assignments](http://courses.csail.mit.edu/6.851/spring14/hmwk.html) contains the calendar as well.
- [CIS 500](http://www.seas.upenn.edu/~cis500/cis500-f14/index.html) **Software Foundations***University of Pennsylvania*
- An introduction to formal verification of software using the Coq proof assistant. Topics include basic concepts of logic, computer-assisted theorem proving, functional programming, operational semantics, Hoare logic, and static type systems.
* [Lectures and Assignments](http://www.seas.upenn.edu/~cis500/cis500-f14/index.html#schedule)
- This course discusses the complexity-theory foundations of modern cryptography, and looks at recent results in the field such as Fully Homomorphic Encryption, Indistinguishability Obfuscation, MPC and so on.
- An graduate level course on complexity theory that introduces P vs NP, the power of randomness, average-case complexity, hardness of approximation, and so on.
- [CS103](http://web.stanford.edu/class/cs103/) **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](https://courses.engr.illinois.edu/cs173/fa2014/A-lecture/index.html) **Discrete Structures***Univ of Illinois Urbana-Champaign*
- 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](http://web.engr.illinois.edu/~mfleck/building-blocks/) Written by the professor. Includes Instructor's Guide.
- [CS 10](https://inst.eecs.berkeley.edu/~cs10/fa14/) **The Beauty and Joy of Computing***UC Berkeley*
- CS10 is UCB's introductory computer science class, taught using the beginners' drag-and-drop language [Snap*!*](http://snap.berkeley.edu) (based on Scratch by MIT).
- [CS 50](https://cs50.harvard.edu/) **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 non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan.
- CS101 teaches the essential ideas of Computer Science for a zero-prior-experience 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.
- 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: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software engineering principles.
- 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.
- Topics: Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++)
- [CS 378](https://github.com/ut-cs378-vision-2014fall/course-info) **3D Reconstruction with Computer Vision***UTexas*
- In this lab-based 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 open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
- [ESM 296-4F](http://ucsb-bren.github.io/esm296-4f/) **GIS & Spatial Analysis***UC Santa Barbara*
- Taught by [James Frew](http://www.bren.ucsb.edu/people/Faculty/james_frew.htm), [Ben Best](http://mgel.env.duke.edu/people/ben-best/), and [Lisa Wedding](http://www.centerforoceansolutions.org/team/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 ](http://ucsb-bren.github.io/esm296-4f/) (includes lecture materials and labs)
- 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.
- 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.
- [CIS 581](https://alliance.seas.upenn.edu/~cis581/wiki/index.php?title=CIS_581:_Computer_Vision_%26_Computational_Photography) **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.
- [EECS 588](https://www.eecs.umich.edu/courses/eecs588/) **Computer & Network Security***University of Michigan*
- Taught by [J. Alex Halderman](https://jhalderm.com/) who has analyzed the security of Electronic Voting Machines in the [US](https://jhalderm.com/pub/papers/dcvoting-fc12.pdf) and [over](https://jhalderm.com/pub/papers/ivoting-ccs14.pdf) [seas](https://jhalderm.com/pub/papers/evm-ccs10.pdf).
- This intensive research seminar covers foundational work and current topics in computer systems security.
- [ICS 314](http://philipmjohnson.github.io/ics314f13/) **Software Engineering***University of Hawaii*
- Taught by [Philip Johnson](http://philipmjohnson.org/)
- Introduction to software engineering using the ["Athletic Software Engineering" pedagogy](http://philipmjohnson.org/2013/12/16/athletic-software-engineering-education-initial-results/)
- Course taught by [Tony Jebara](http://www.cs.columbia.edu/~jebara/resume.html) 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.
- [Lectures and Assignments](http://www.cs.columbia.edu/~jebara/4771/handouts.html)
Have a few courses in mind that you think are awesome and would fit in this list? Feel free to send a [pull request](https://github.com/prakhar1989/awesome-courses/pulls). However, do note that I'm not keen on adding popular courses (such as MOOCs / MIT OCW) as there are services like [ClassCentral](https://www.class-central.com/) doing a great job of aggregation. This list is ideally for courses which are relatively unknown and make their material (assignments, lectures, exams etc.) available online for free.
