From fdc7a163b5c91ef640d1cbb3f5a7e0a142aa483a Mon Sep 17 00:00:00 2001 From: Prakhar Srivastav Date: Fri, 19 Jun 2015 17:49:10 +0530 Subject: [PATCH] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3f717b5..4f49d07 100644 --- a/README.md +++ b/README.md @@ -468,7 +468,8 @@ Courses - [11-785](http://deeplearning.cs.cmu.edu/) **Deep Learning** *Carnegie Mellon University* Assignments 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. -- [10-601](http://www.cs.cmu.edu/~ninamf/courses/601sp15/) **Machine Learning** *Carnegie Mellon University* Assignments Lecture Notes Readings +- [10-601](http://www.cs.cmu.edu/~ninamf/courses/601sp15/) **Machine Learning** *Carnegie Mellon University* Assignments Lecture Notes Readings Lecture Videos + - This course covers the theory and practical algorithms for machine learning from a variety of perspectives. It covers topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning. - 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)