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Utkarsh Sinha
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@ -16,6 +16,8 @@ Table of Contents
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- [Introduction to CS](#introduction-to-cs)
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- [Machine Learning](#machine-learning)
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- [Security](#security)
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- [Artificial Intelligence](#artificial-intelligence)
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- [Computer Graphics](#computer-graphics)
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- [Misc](#misc)
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### Legend
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@ -31,9 +33,6 @@ Courses
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### Systems
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- [CIS 198](http://cis198-2016s.github.io/) **Rust Programming** *UPenn* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
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- 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.
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- [Assignments](https://github.com/cis198-2016s/homework)
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- [CS 61C](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/) **Great Ideas in Computer Architecture (Machine Structures)** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- [Lecture Videos](https://www.youtube.com/playlist?list=PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_)
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- [Lecture Notes](http://www-inst.eecs.berkeley.edu/~cs61c/sp15/#Calendar)
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@ -160,6 +159,12 @@ Courses
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- [Assignments](https://www.ops-class.org/asst/0/)
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- [Old Exams](https://www.ops-class.org/exams/)
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- [CSCI-UA.0202: Operating Systems (Undergrad)](http://www.cs.nyu.edu/~mwalfish/classes/15sp/index.html) **Operating Systems** *NYU* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process shceduling, file system, ect. It also includes some recomended reading materials. What's more, there are a series of hands-on lab materials, helping you easily understand OS.
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- [Assignments](http://www.cs.nyu.edu/~mwalfish/classes/15sp/labs.html)
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- [Lectures](http://www.cs.nyu.edu/~mwalfish/classes/15sp/syllabus.html)
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- [Old Exams](http://www.cs.nyu.edu/~mwalfish/classes/15sp/exams.html)
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-------
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### Programming Languages / Compilers
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@ -168,6 +173,9 @@ Courses
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- 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.
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- [Previous](http://www.seas.upenn.edu/~cis194/spring13/index.html) semester also available, with more exercises
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- [Assignments & Lectures](http://www.seas.upenn.edu/~cis194/lectures.html)
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- [CIS 198](http://cis198-2016s.github.io/) **Rust Programming** *UPenn* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
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- 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.
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- [Assignments](https://github.com/cis198-2016s/homework)
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- [Clojure](http://mooc.cs.helsinki.fi/clojure) **Functional Programming with Clojure** *University of Helsinki* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
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- The course is an introduction to functional programming with a dynamically typed language Clojure. We start with an introduction to Clojure; its syntax and development environment. Clojure has a good selection of data structures and we cover most of them. We also go through the basics of recursion and higher-order functions. The course material is in English.
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- [Github Page](http://iloveponies.github.io/120-hour-epic-sax-marathon/index.html)
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@ -181,7 +189,7 @@ Courses
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- [Assignments](http://www.cs.princeton.edu/~dpw/courses/cos326-12/assignments.php)
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- [CS 164](https://sites.google.com/a/bodik.org/cs164/home) **Hack your language!** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
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- Introduction to programming languages by designing and implementing domain-specific languages.
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- [Lecture Videos](https://www.youtube.com/playlist?list=PL421867F00A53B833)
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- [Lecture Videos](https://www.youtube.com/playlist?list=PL3A16CFC42CA6EF4F)
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- [Code for Assignments](https://bitbucket.org/cs164_overlord/)
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- [CS 173](http://cs.brown.edu/courses/cs173/2014/) **Programming Languages** *Brown University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
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- 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.
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@ -257,6 +265,13 @@ Courses
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- It covers concepts such as atomicity, safety, liveness and deadlock.
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- It covers how to measure and understand performance and scalability of parallel programs.
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- It covers tools and methods to find bugs in concurrent programs.
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- [CS 143](https://web.stanford.edu/class/cs143/) **Compiler construction** *Stanford University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
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- CS143 is a Stanford's course in the practical and theoretical aspects of compiler construction.
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- [Home] (https://web.stanford.edu/class/cs143/)
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- [Syllabus](https://web.stanford.edu/class/cs143/schedule.html)
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- [Lectures](https://web.stanford.edu/class/cs143/)
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- [Assignments](https://web.stanford.edu/class/cs143/)
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- [CS143 - 2011](http://www.keithschwarz.com/cs143/WWW/sum2011/)
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-------
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@ -541,11 +556,6 @@ Courses
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- [Lectures](https://work.caltech.edu/lectures.html)
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- [Homework](https://work.caltech.edu/homeworks.html)
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- [Textbook](https://work.caltech.edu/textbook.html)
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- [CS 188](http://ai.berkeley.edu/home.html) **Introduction to Artificial Intelligence** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20 " height="20" alt="Lecture Notes" title="Lecture Notes" />
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- 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 decision-theoretic 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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- [Lectures](http://ai.berkeley.edu/lecture_videos.html)
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- [Projects](http://ai.berkeley.edu/project_overview.html)
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- [Exams](http://ai.berkeley.edu/exams.html)
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- [CS 224d](http://cs224d.stanford.edu/) **Deep Learning for Natural Language Processing** *Stanford University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
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- 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 end-to-end model and do not require traditional, task-specific 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 cutting-edge research in deep learning applied to NLP.
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- [Syllabus](http://cs224d.stanford.edu/syllabus.html)
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@ -559,6 +569,14 @@ Courses
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- The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
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- [Lectures Notes](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#syllabus)
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- [Assignments](http://www.cs.berkeley.edu/~pabbeel/cs287-fa13/#assignments)
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- [CS 4786](http://www.cs.cornell.edu/courses/CS4786/2015sp/index.htm) **Machine Learning for Data Science** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- An introductory course in machine learning, with a focus on data modeling and related methods and learning algorithms for data sciences. Tentative topic list:
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- 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).
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- Clustering, such as k-means, Gaussian mixture models, the expectation-maximization (EM) algorithm, link-based clustering. (We do not expect to cover hierarchical or spectral clustering.).
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- Probabilistic-modeling topics such as graphical models, latent-variable models, inference (e.g., belief propagation), parameter learning.
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- Regression will be covered if time permits.
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- [Assignments](http://www.cs.cornell.edu/courses/CS4786/2015sp/assignments.htm)
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- [Lectures](http://www.cs.cornell.edu/courses/CS4786/2015sp/lectures.htm)
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- [CS 4780](http://www.cs.cornell.edu/courses/CS4780/2014fa/) **Machine Learning** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- This course will introduce you to technologies for building data-centric 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 cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
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- [Syllabus](http://www.cs.cornell.edu/courses/CS4780/2014fa/)
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@ -587,16 +605,19 @@ Topics covered include probability theory and Bayesian inference; univariate dis
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- 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 programminging tool. GPU programming experiences are preferred although not required. Frequent paper presentations and a heavy programming workload are expected.
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- [Readings](http://llcao.net/cu-deeplearning15/reading.html)
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- [Assignments](http://llcao.net/cu-deeplearning15/programming_problem.html)
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- [Lecture Notes](http://llcao.net/cu-deeplearning15/index.html)
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- [6.868J](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/index.htm) **The Society of Mind** *MIT* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- This course is an introduction, by Prof. [Marvin Minsky](http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0), 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. common-sense theories of learning.
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- [Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/video-lectures/)
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- [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/assignments/)
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- [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/readings/)
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- [Lecture Notes](http://llcao.net/cu-deeplearning15/index.html)
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- [EECS E6893 & EECS E6895](http://www.ee.columbia.edu/~cylin/course/bigdata/) **Big Data Analytics & Advanced Big Data Analytics** *Columbia University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
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- 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.
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- Taught by [Dr. Ching-Yung Lin](http://researcher.watson.ibm.com/researcher/view.php?person=us-chingyung)
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- [Course Site](http://www.ee.columbia.edu/~cylin/course/bigdata/)
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- Assignments - Assignments are present in the Course Slides
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- [Info 290](http://www.ischool.berkeley.edu/courses/i290-abdt) **Analyzing Big Data with Twitter** *UC Berkeley school of information* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
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- In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge 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, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
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- [Lecture Videos](http://www.ischool.berkeley.edu/newsandevents/audiovideo/webcast/21963)
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- [Previous Years coursepage](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)
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-------
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###Security
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### Security
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- [6.857](http://courses.csail.mit.edu/6.857/2015/) **Computer and Network Security** *MIT* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
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- Emphasis on applied cryptography and may include: basic notion of systems security, crypotographic hash functions, symmetric crypotography (one-time pad, stream ciphers, block ciphers), cryptanalysis, secret-sharing, authentication codes, public-key cryptography (encryption, digital signatures), public-key attacks, web browser security, biometrics, electronic cash, viruses, electronic voting, Assignments include a group final project. Topics may vary year to year.
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[Lecture Notes](http://courses.csail.mit.edu/6.857/2015/handouts)
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@ -651,13 +672,34 @@ and anti-analysis techniques.
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- [Readings](https://www.eecs.umich.edu/courses/eecs588/readings.html)
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-------
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###Artificial Intelligence
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- [6.868J](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/index.htm) **The Society of Mind** *MIT* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
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- This course is an introduction, by Prof. [Marvin Minsky](http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0), 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. common-sense theories of learning.
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- [Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/video-lectures/)
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- [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/assignments/)
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- [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/readings/)
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- [CS 188](http://ai.berkeley.edu/home.html) **Introduction to Artificial Intelligence** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20 " height="20" alt="Lecture Notes" title="Lecture Notes" />
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- 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 decision-theoretic 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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- [Lectures](http://ai.berkeley.edu/lecture_videos.html)
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- [Projects](http://ai.berkeley.edu/project_overview.html)
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- [Exams](http://ai.berkeley.edu/exams.html)
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- [CS 4700](http://www.cs.cornell.edu/courses/CS4700/2014fa/) **Foundations of Artificial Intelligence** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
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- 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 project-based course, in which you will implement several computer vision algorithms throughout the semester.
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- [Assignments](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
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- [Lectures](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
|
||||
- [CS 6700](http://www.cs.cornell.edu/courses/CS6700/2013sp/) **Advanced Artificial Intelligence** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS6700/2013sp/lectures/CS6700-Overview_v2.pptx)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [Readings](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [CS L333](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm) **Introduction to Artificial Intelligence** *IIT Delhi* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- Introduction to Artificial Intelligence- Problem Solving, Game Playing, Knowledge Representation, Handling uncertainty using probabilistic models and Fuzzy Logic. Expert systems and Intelligent agents. Machine Learning, Soft computing and NLP.
|
||||
- [Lectures](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm)
|
||||
- [Assignments](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm)
|
||||
- [Readings](http://www.cse.iitd.ernet.in/~saroj/AI/ai2013/ai_main_13.htm#book)
|
||||
|
||||
### Misc
|
||||
- [AM 207](http://am207.org/) **Monte Carlo Methods and Stochastic Optimization** *Harvard University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
|
||||
- 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!
|
||||
- [Lecture Videos](http://cm.dce.harvard.edu/2015/02/24104/publicationListing.shtml)
|
||||
- [Assignments](http://am207.github.io/2015/homework.html)
|
||||
- [Lecture Notes](http://am207.github.io/2015/lectures.html)
|
||||
-------
|
||||
### Computer Graphics
|
||||
- [CAP 5415](http://crcv.ucf.edu/courses/CAP5415/) **Computer Vision** *University of Central Florida* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
|
||||
- An introductory level course covering the basic topics of computer vision, and introducing some fundamental approaches for computer vision research.
|
||||
- [Lectures and Videos](http://crcv.ucf.edu/videos/Lecture_Videos/)
|
||||
@ -666,6 +708,33 @@ and anti-analysis techniques.
|
||||
- An introductory course in computer vision and computational photography focusing on four topics: image features, image morphing, shape matching, and image search.
|
||||
- [Lectures](https://alliance.seas.upenn.edu/~cis581/wiki/index.php?title=Schedule)
|
||||
- [Assignments](https://alliance.seas.upenn.edu/~cis581/wiki/index.php?title=Projects)
|
||||
- [CS 378](https://github.com/ut-cs378-vision-2014fall/course-info) **3D Reconstruction with Computer Vision** *UTexas* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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
|
||||
- [Lectures](https://github.com/ut-cs378-vision-2014fall/course-info/tree/master/meeting-notes)
|
||||
- [CS 4620](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#) **Introduction to Computer Graphics** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" />
|
||||
- The study of creating, manipulating, and using visual images in the computer.
|
||||
- [Assignments](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#asgn)
|
||||
- [Exams](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#exams)
|
||||
- [CSCI-GA.2270-001](https://mrl.nyu.edu/~perlin/courses/fall2015/) **Graduate Computer Graphics** *New York University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" />
|
||||
- Step-by-step study computer graphics, with reading and homework at each lecture (Fall2015)
|
||||
- [Lectures](https://mrl.nyu.edu/~perlin/courses/fall2015/)
|
||||
- [CS 4670](http://www.cs.cornell.edu/courses/CS4670/2015sp/) **Introduction to Computer Vision** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- 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 project-based course, in which you will implement several computer vision algorithms throughout the semester.
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS4670/2015sp/projects/projects.html)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS4670/2015sp/lectures/lectures.html)
|
||||
- [CS 6670](https://canvas.instructure.com/courses/904706) **Computer Vision** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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.
|
||||
- [Syllabus](https://canvas.instructure.com/courses/904706/assignments/syllabus)
|
||||
- [Lectures](https://canvas.instructure.com/courses/904706)
|
||||
- [Assignments](https://canvas.instructure.com/courses/904706/assignments)
|
||||
|
||||
-------
|
||||
### Misc
|
||||
- [AM 207](http://am207.org/) **Monte Carlo Methods and Stochastic Optimization** *Harvard University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" />
|
||||
- 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!
|
||||
- [Lecture Videos](http://cm.dce.harvard.edu/2015/02/24104/publicationListing.shtml)
|
||||
- [Assignments](http://am207.github.io/2015/homework.html)
|
||||
- [Lecture Notes](http://am207.github.io/2015/lectures.html)
|
||||
- [CS 75](http://ocw.tufts.edu/Course/75) **Introduction to Game Development** *Tufts University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- The course taught by [Ming Y. Chow](http://mchow01.github.io) 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
|
||||
- [Text Lectures](http://ocw.tufts.edu/Course/75/Learningunits)
|
||||
@ -675,7 +744,7 @@ and anti-analysis techniques.
|
||||
- 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](https://github.com/mikeizbicki/ucr-cs100)
|
||||
- [Assignments](https://github.com/mikeizbicki/ucr-cs100/tree/2015winter/assignments)
|
||||
- [CS 108](http://web.stanford.edu/class/cs108/) **Object Oriented System Design** *Stanford* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- [CS 108](http://web.stanford.edu/class/cs108/) **Object Oriented System Design** *Stanford* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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 193p](https://itunes.apple.com/us/course/developing-ios-7-apps-for/id733644550) **Developing Applications for iOS** *Stanford University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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 multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
|
||||
@ -686,9 +755,6 @@ and anti-analysis techniques.
|
||||
- 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.
|
||||
- [Lectures](http://see.stanford.edu/see/lecturelist.aspx?coll=86cc8662-f6e4-43c3-a1be-b30d1d179743)
|
||||
- [Assignments](http://see.stanford.edu/see/materials/aiircs223a/assignments.aspx)
|
||||
- [CS 378](https://github.com/ut-cs378-vision-2014fall/course-info) **3D Reconstruction with Computer Vision** *UTexas* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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
|
||||
- [Lectures](https://github.com/ut-cs378-vision-2014fall/course-info/tree/master/meeting-notes)
|
||||
- [CS 411](http://video.bilkent.edu.tr/course_videos.php?courseid=10) **Software Architecture Design** *Bilkent University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
|
||||
- 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, synthesis-based software architecture design, software product-line architectures, domain modeling, domain engineering and application engineering, software architecture implementation, evaluating software architecture designs.
|
||||
- [CS 3152](http://www.cs.cornell.edu/courses/CS3152/2014sp/) **Introduction to Computer Game Development** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /><img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
@ -706,29 +772,6 @@ and anti-analysis techniques.
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS4154/2014fa/about/faq.php)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS4154/2014fa/lectures/index.php)
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS4154/2014fa/assignments/index.php)
|
||||
- [CS 4620](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#) **Introduction to Computer Graphics** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" />
|
||||
- The study of creating, manipulating, and using visual images in the computer.
|
||||
- [Assignments](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#asgn)
|
||||
- [Exams](http://www.cs.cornell.edu/Courses/CS4620/2014fa/index.shtml#exams)
|
||||
- [CSCI-GA.2270-001](https://mrl.nyu.edu/~perlin/courses/fall2015/) **Graduate Computer Graphics** *New York University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" />
|
||||
- Step-by-step study computer graphics, with reading and homework at each lecture (Fall2015)
|
||||
- [Lectures](https://mrl.nyu.edu/~perlin/courses/fall2015/)
|
||||
- [CS 4670](http://www.cs.cornell.edu/courses/CS4670/2015sp/) **Introduction to Computer Vision** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- 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 project-based course, in which you will implement several computer vision algorithms throughout the semester.
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS4670/2015sp/projects/projects.html)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS4670/2015sp/lectures/lectures.html)
|
||||
- [CS 4700](http://www.cs.cornell.edu/courses/CS4700/2014fa/) **Foundations of Artificial Intelligence** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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 project-based course, in which you will implement several computer vision algorithms throughout the semester.
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS4700/2014fa/)
|
||||
- [CS 4786](http://www.cs.cornell.edu/courses/CS4786/2015sp/index.htm) **Machine Learning for Data Science** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- 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 k-means, Gaussian mixture models, the expectation-maximization (EM) algorithm, link-based clustering. (We do not expect to cover hierarchical or spectral clustering.).
|
||||
- Probabilistic-modeling topics such as graphical models, latent-variable models, inference (e.g., belief propagation), parameter learning.
|
||||
- Regression will be covered if time permits.
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS4786/2015sp/assignments.htm)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS4786/2015sp/lectures.htm)
|
||||
- [CS 4812](https://courses.cit.cornell.edu/physics4481-7681_2014sp/) **Quantum Information Processing** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- 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.
|
||||
- [Syllabus](http://www.cs.cornell.edu/~ginsparg/physics/P4481-P7681-CS4812/Fa12.html)
|
||||
@ -767,28 +810,18 @@ and anti-analysis techniques.
|
||||
- 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.
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS6452/2012sp/lectures.php)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS6452/2012sp/lectures.php)
|
||||
- [CS 6630](http://courses2.cit.cornell.edu/cs5724/) **Realistic Image Synthesis** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- This course will cover advanced topics in evolutionary algorithms and their application to open-ended computational design. The field of evolutionary computation tries to address large-scale optimization and planning problems through stochastic population-based 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 open-ended 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 co-evolution, 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 6630](http://www.cs.cornell.edu/courses/CS6630/2012sp/about.stm) **Realistic Image Synthesis** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
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- CS6630 is an introduction to physics-based 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 luminaires 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.
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS6630/2012sp/about.stm)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS6630/2012sp/schedule.stm)
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS6630/2012sp/schedule.stm)
|
||||
- [Readings](http://www.cs.cornell.edu/courses/CS6630/2012sp/schedule.stm)
|
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- [CS 6640](http://www.cs.cornell.edu/courses/CS6640/2012fa/index.shtml#) **Realistic Image Synthesis** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
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- [CS 6640](http://www.cs.cornell.edu/courses/CS6640/2012fa/index.shtml#) **Computational Photography** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
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- 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 projector-camera systems.cornell.edu/courses/CS6630/2012sp/about.stm)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS6640/2012fa/index.shtml#schedule)
|
||||
- [Assignments](http://www.cs.cornell.edu/courses/CS6640/2012fa/index.shtml#hw)
|
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- [CS 6650](http://www.cs.cornell.edu/courses/CS6650/2013fa/) **Computational Motion** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- 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 spatio-temporal 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 6670](https://canvas.instructure.com/courses/904706) **Computer Vision** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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.
|
||||
- [Syllabus](https://canvas.instructure.com/courses/904706/assignments/syllabus)
|
||||
- [Lectures](https://canvas.instructure.com/courses/904706)
|
||||
- [Assignments](https://canvas.instructure.com/courses/904706/assignments)
|
||||
- [CS 6700](http://www.cs.cornell.edu/courses/CS6700/2013sp/) **Advanced Artificial Intelligence** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS6700/2013sp/lectures/CS6700-Overview_v2.pptx)
|
||||
- [Lectures](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [Readings](http://www.cs.cornell.edu/courses/CS6700/2013sp/)
|
||||
- [CS 6840](http://www.cs.cornell.edu/courses/CS6840/2014sp/) **Algorithmic Game Theory** *Cornell University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments"/> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
|
||||
- Algorithmic Game Theory combines algorithmic thinking with game-theoretic, or, more generally, economic concepts. The course will study a range of topics at this interface
|
||||
- [Syllabus](http://www.cs.cornell.edu/courses/CS6840/2014sp/)
|
||||
@ -820,17 +853,8 @@ and anti-analysis techniques.
|
||||
- [Lectures](http://www.schneems.com/ut-rails/)
|
||||
- [Assignments](http://www.schneems.com/ut-rails/)
|
||||
- [Videos](https://www.youtube.com/playlist?list=PL7A85FD7803A8CB1F)
|
||||
- [Info 290](http://www.ischool.berkeley.edu/courses/i290-abdt) **Analyzing Big Data with Twitter** *UC Berkeley school of information* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
|
||||
- In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge 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, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
|
||||
- [Lecture Videos](http://www.ischool.berkeley.edu/newsandevents/audiovideo/webcast/21963)
|
||||
- [Previous Years coursepage](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)
|
||||
- [CS294](http://inst.eecs.berkeley.edu/~cs294-101/sp15/) **Cutting-edge Web Technologies** *Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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.
|
||||
- [EECS E6893 & EECS E6895](http://www.ee.columbia.edu/~cylin/course/bigdata/) **Big Data Analytics & Advanced Big Data Analytics** *Columbia University* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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. Ching-Yung Lin](http://researcher.watson.ibm.com/researcher/view.php?person=us-chingyung)
|
||||
- [Course Site](http://www.ee.columbia.edu/~cylin/course/bigdata/)
|
||||
- Assignments - Assignments are present in the Course Slides
|
||||
- [SCICOMP](http://mlecture.uni-bremen.de/ml/index.php?option=com_content&view=article&id=233) **An Introduction to Efficient Scientific Computation** *Universität Bremen* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
|
||||
- This is a graduate course in scientific computing created and taught by [Oliver Serang](http://colorfulengineering.org/) in 2014, which covers topics in computer science and statistics with applications from biology. The course is designed top-down, 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. Smith-Waterman and Needleman-Wunsch), Bayesian statistics (e.g. the envelope paradox), graphical models (HMMs, Viterbi, junction tree, belief propagation), FFT, and the probabilistic convolution tree.
|
||||
@ -842,4 +866,8 @@ and anti-analysis techniques.
|
||||
- [CS 168](https://inst.eecs.berkeley.edu/~cs168/fa15/) **Computer Networks** *UC Berkeley*<img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- 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 262a](http://www.cs.berkeley.edu/~brewer/cs262/) **Advanced Topics in Computer Systems** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
|
||||
- CS262a is the first semester of a year-long 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 top-flight 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, Higher-Level 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](https://cs162.eecs.berkeley.edu/)). 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).
|
||||
- [Readings & Lectures](http://www.cs.berkeley.edu/~brewer/cs262/)
|
||||
|
||||
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