Machine Learning at Stanford University

vinayak — Sun, 28 Mar 2010 11:34:09 +0000

This time we bring to you an online video tutorial on CS 229: Machine Learning course from Stanford University. Besides 19 Lectures on machine learning you will be provided with relevant lecture notes, links other related miscellaneous stuff . Professor Andrew Ng will introduce machine learning and statistical pattern recognition which will cover topics like supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Other applications of machine learning, such as to robotic control, data mining, autonomous navigation, bio informatics, speech recognition, and text and web data processing are also discussed.

This was the first lecture:- (course materials) The first 30 minutes or so of this lecture is introduction to the course and the field of machine learning in general.

In the rest of the lecture, the four main parts of the course is described in some detail along with illustrative examples :-Supervised learning: providing the algorithm a data set, supervising- learn the association between input & output, regression problems, classification problems, support vector machines- infinite number of features
Learning theory
Unsupervised learning: clustering, Cocktail party problem, independent component analysis Reinforcement learning: reward function (good dog, bad dog), feedback function

In the second lecture, Professor Andrew Ng will cover topics like linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

For more visit the home page: http://cs229.stanford.edu/

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Machine Learning at Stanford University