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I think that machine learning deserves its own section

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@ -14,6 +14,7 @@ Table of Contents
- [Algorithms](#algorithms)
- [CS Theory](#cs-theory)
- [Introduction to CS](#introduction-to-cs)
- [Machine Learning](#machine-learning)
- [Misc](#misc)
- [Contributing](#contributing)
@ -299,6 +300,20 @@ Courses
-------
### Machine Learning
- [11-785](http://deeplearning.cs.cmu.edu/) **Deep Learning** *Carnegie Mellon 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/1f4da.png" width="20" height="20" alt="Readings" />
- The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. *Lectures* and *Reading Notes* are available on the page.
- [15-781](http://www.cs.cmu.edu/~tom/10701_sp11/) **Machine Learning** *Carnegie Mellon 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" />
- Taught by one of the leading experts on Machine Learning - **Tom Mitchell**
- [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)
- [COMS 4771](http://www.cs.columbia.edu/~jebara/4771/index.html) **Machine Learning** *Columbia 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" />
- 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)
-------
### Misc
- [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* <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" />
@ -309,9 +324,6 @@ Courses
- Course taught by [W. Owen Redwood](http://ww2.cs.fsu.edu/~redwood/) and [Xiuwen Liu](http://www.cs.fsu.edu/~liux/). It covers a wide range of computer security topics, starting from Secure C Coding and Reverse Engineering to Penetration Testing, Exploitation and Web Application Hacking, both from the defensive and the offensive point of view.
- [Lectures and Videos](http://www.cs.fsu.edu/~redwood/OffensiveComputerSecurity/lectures.html)
- [Assignments](http://www.cs.fsu.edu/~redwood/OffensiveComputerSecurity/assignments.html)
- [COMS 4771](http://www.cs.columbia.edu/~jebara/4771/index.html) **Machine Learning** *Columbia 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" />
- 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)
- [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" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="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)
@ -357,12 +369,6 @@ Courses
- Course taught by [Luis Rocha](http://www.informatics.indiana.edu/rocha/lr_form.html) about the multi-disciplinary field algorithms inspired by naturally occurring phenomenon. This course provides introduces the following areas: L-systems, Cellular Automata, Emergence, Genetic Algorithms, Swarm Intelligence and Artificial Immune Systems. It's aim is to cover the fundamentals and enable readers to build up a proficiency in applying various algorithms to real-world problems.
- [Lectures](http://www.informatics.indiana.edu/rocha/i-bic/#materials)
- [Assignments](http://www.informatics.indiana.edu/rocha/i-bic/#labs)
- [11-785](http://deeplearning.cs.cmu.edu/) **Deep Learning** *Carnegie Mellon 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/1f4da.png" width="20" height="20" alt="Readings" />
- The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. *Lectures* and *Reading Notes* are available on the page.
- [15-781](http://www.cs.cmu.edu/~tom/10701_sp11/) **Machine Learning** *Carnegie Mellon 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" />
- Taught by one of the leading experts on Machine Learning - **Tom Mitchell**
- [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)
- [Open Sourced Elective: Database and Rails](http://www.schneems.com/ut-rails/) **Intro to Ruby on Rails** *University of Texas* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" /><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" />
- An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman.
- [Lectures](http://www.schneems.com/ut-rails/)