diff --git a/README.md b/README.md index 56c444d..cf5535e 100644 --- a/README.md +++ b/README.md @@ -502,6 +502,10 @@ 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/) +- [**Machine Learning: 2014-2015**](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) *University of Oxford* Lecture Videos Lecture Notes 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 + - [Lecutures and Assignments](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) + - [Source code](https://github.com/oxford-cs-ml-2015/) -------