"Lec 14 - Machine Learning (Stanford)" Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). 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 unsupervised expectation maximization EM principal component analysis PCA
Uploaded by: stanfordmachine ( Send Message ) on 04-09-2012.
Duration: 80m 40s
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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 13 - Machine Learning (Stanford)
Lec 15 - Machine Learning (Stanford)
Lec 16 - Machine Learning (Stanford)
Lec 17 - Machine Learning (Stanford)
Lec 18 - Machine Learning (Stanford)