From 9c3a71b1f35c50b96a96da6225993e08836a1589 Mon Sep 17 00:00:00 2001 From: Julien Perrissin Date: Tue, 26 Jan 2016 08:58:30 +0100 Subject: [PATCH 1/2] Fix typography mistake MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace ‘Lecutures’ by ‘Lecture’. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5ce0af3..65c85af 100644 --- a/README.md +++ b/README.md @@ -557,13 +557,13 @@ Topics covered include probability theory and Bayesian inference; univariate dis - [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/) + - [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* Lecture Notes Assignments 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. - [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) + - [Lecture Notes](http://llcao.net/cu-deeplearning15/index.html) ------- From b59e5fa58e79de6c457d3049888d849d0739d6a7 Mon Sep 17 00:00:00 2001 From: Julien Perrissin Date: Tue, 26 Jan 2016 09:11:35 +0100 Subject: [PATCH 2/2] Added MIT 6.868J course MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add ‘The Society of Mind’ course. This course is about general AI, and is a good introduction about how computers can learn and think like us. --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 65c85af..b2cdcda 100644 --- a/README.md +++ b/README.md @@ -564,6 +564,11 @@ Topics covered include probability theory and Bayesian inference; univariate dis - [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) +- [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* Lecture Notes Assignments Readings + - 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. + - [Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/video-lectures/) + - [Assignments](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/assignments/) + - [Readings](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/readings/) -------