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@ -362,7 +362,7 @@ Courses
- [CS 276](http://www.cs.berkeley.edu/~sanjamg/classes/cs276-fall14/) **Foundations of Cryptography** *UC Berkeley* <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 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.
- [CS 278](http://www.cs.berkeley.edu/~luca/cs278-08/) **Complexity Theory** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" />
- 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.
- A graduate level course on complexity theory that introduces P vs NP, the power of randomness, average-case complexity, hardness of approximation, and so on.
- [CS 374](https://courses.engr.illinois.edu/cs498374/fa2014/) **Algorithms & Models of Computation (Fall 2014)** *University of Illinois Urbana-Champaign* <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 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 context-free languages, finite-state automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth- and breadth-first search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NP-completeness. The course also has a strong focus on clear technical communication.
- [Assignments/Exams](https://courses.engr.illinois.edu/cs498374/fa2014/work.html)
@ -453,7 +453,7 @@ Courses
- [Lectures](http://see.stanford.edu/see/lecturelist.aspx?coll=2d712634-2bf1-4b55-9a3a-ca9d470755ee)
- [Assignments](http://see.stanford.edu/see/materials/icsppcs107/assignments.aspx)
- [CS 109] (http://otfried.org/courses/cs109/index.html) **Programming Practice Using Scala** *KAIST* <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 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 commmand line, using libraries, and the use of basic data structures such as arrays, lists, sets, and maps. We will use Scala for this course.
- 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](http://www.cs.cornell.edu/courses/CS1109/2013su/) **Fundamental Programming Concepts** *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" />
@ -539,7 +539,7 @@ Courses
- [Lectures](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)
- [Project Ideas and Datasets](http://www.cs.cmu.edu/~tom/10701_sp11/proj.shtml)
- [10-708](http://www.cs.cmu.edu/~epxing/Class/10708-14/index.html) **Probabilistic Graphical Models** *Carnegie Mellon 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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- 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 an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level 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.
- 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, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level 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.
- [Lecture Videos](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
- [Assignments](http://www.cs.cmu.edu/~epxing/Class/10708-14/homework.html)
- [Lecture notes](http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html)
@ -608,7 +608,7 @@ Topics covered include probability theory and Bayesian inference; univariate dis
- [Lectures and Assignments](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- [Source code](https://github.com/oxford-cs-ml-2015/)
- [EECS E6894](http://llcao.net/cu-deeplearning15/index.html) **Deep Learning for Computer Vision and Natural Language Processing** *Columbia 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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- 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.
- 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.
- [Readings](http://llcao.net/cu-deeplearning15/reading.html)
- [Assignments](http://llcao.net/cu-deeplearning15/programming_problem.html)
- [Lecture Notes](http://llcao.net/cu-deeplearning15/index.html)
@ -625,7 +625,7 @@ Topics covered include probability theory and Bayesian inference; univariate dis
### Security
- [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" />
- 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.
- Emphasis on applied cryptography and may include: basic notion of systems security, cryptographic hash functions, symmetric cryptography (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.
[Lecture Notes](http://courses.csail.mit.edu/6.857/2015/handouts)
[References](http://courses.csail.mit.edu/6.857/2015/references)
- [6.858](http://css.csail.mit.edu/6.858/2014/) **Computer Systems Security** *MIT* <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="" width="20" height="20" alt="Readings" title="Readings" />
@ -817,7 +817,7 @@ and anti-analysis techniques.
- [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://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" />
- 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.
- 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 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.
- [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)