"Lec 13 - Machine Learning (Stanford)" Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. Complete Playlist for the Course: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599 CS 229 Course Website: http://www.stanford.edu/class/cs229/ Stanford University: http://www.stanford.edu/ Stanford University Channel on YouTube: http://www.youtube.com/stanford
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Channels: Computer Science
Tags: science math engineering computer technology robotics learning algorithm expectation maximization EM mixture Gaussian naive Bayes model factor analysis digression distribution unsupervised
Uploaded by: stanfordmachine ( Send Message ) on 04-09-2012.
Duration: 74m 57s
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Lec 1 - Machine Learning (Stanford)
Lec 2 - Machine Learning (Stanford)
Lec 3 - Machine Learning (Stanford)
Lec 4 - Machine Learning (Stanford)
Lec 5 - Machine Learning (Stanford)
Lec 6 - Machine Learning (Stanford)
Lec 7 - Machine Learning (Stanford)
Lec 8 - Machine Learning (Stanford)
Lec 9 - Machine Learning (Stanford)
Lec 10 - Machine Learning (Stanford)
Lec 11 - Machine Learning (Stanford)
Lec 12 - Machine Learning (Stanford)
Lec 14 - Machine Learning (Stanford)
Lec 15 - Machine Learning (Stanford)
Lec 16 - Machine Learning (Stanford)
Lec 17 - Machine Learning (Stanford)
Lec 18 - Machine Learning (Stanford)