Lecture 9 | Machine Learning (Stanford)

Title: Lecture 9 | Machine Learning (Stanford)

Added: Jul 22, 2008

Author: StanfordUniversity

Duration: 74:19

Description:
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding's inequalities. 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=A89DCFA6ADACE599CS 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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Channel: Education

Tags: science  math  engineering  computer  technology  robotics  learning  algorithm  theory  bias  variance  empirical  risk  minimization  erm  union  bound  boole  hoeffding  inequality  uniform  convergence 

science  math  engineering  computer  technology  robotics  learning  algorithm  theory  bias  variance  empirical  risk  minimization  erm  union  bound  boole  hoeffding  inequality  uniform  convergence 

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