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Added DSGA.1008 Deep Learning course by NYU

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Andrew Drozdov 2016-02-22 21:30:11 -05:00
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@ -566,6 +566,10 @@ Topics covered include probability theory and Bayesian inference; univariate dis
- The course concentrates on recognizing and solving convex optimization problems that arise in applications. Topics addressed include the following. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.
- [Textbook](http://web.stanford.edu/~boyd/cvxbook/)
- [Lectures and Assignments](https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/courseware/7206c57866504e83821d00b5d3f80793/)
- [DS-GA 1008](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning2015:schedule) **Deep Learning** *New York 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 increasingly popular course is taught through the Data Science Center at NYU. Originally introduced by [Yann Lecun](http://yann.lecun.com/), it is now led by [Zaid Harchaoui](http://www.harchaoui.eu/), although Prof. Lecun is rumored to still stop by from time to time. It covers the theory, technique, and tricks that are used to achieve very high accuracy for machine learning tasks in computer vision and natural language processing. The assignments are in Lua and hosted on Kaggle.
- [Course Page](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning2015:schedule)
- [Recorded Lectures](http://techtalks.tv/deep-learning-nyu-spring-2015/)
- [**Machine Learning: 2014-2015**](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) *University of Oxford* <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" />
- The course focusses on neural networks and uses the [Torch](https://github.com/torch/torch7/wiki/Cheatsheet) deep learning library (implemented in Lua) for exercises and assignments. Topics include: logistic regression, back-propagation, convolutional neural networks, max-margin learning, siamese networks, recurrent neural networks, LSTMs, hand-writing with recurrent neural networks, variational autoencoders and image generation and reinforcement learning
- [Lectures and Assignments](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)