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Lecture 9 | Machine Learning (Stanford)

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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.

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Tags: science, union, theory, computer, engineering, learning, technology, bound, risk, math, algorithm, convergence, robotics, uniform, bias, inequality, variance, empirical, minimization
Category: Flag video Educational