info - CS 229 Machine Learning Handout #1: Course...

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CS 229 Machine Learning Handout #1: Course Information Teaching Staff and Contact Info Professor: Andrew Ng Office: Gates 156 TA: Paul Baumstarck Office: B24B TA: Catie Chang Office: B24A TA: Chuong (Tom) Do Office: B24A TA: Zico Kolter (head TA) Office: Gates 124 TA: Daniel Ramage Office: Gates 114 Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
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Prerequisites Students are expected to have the following background: & Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. & Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.) & Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.) Course Materials There is no required text for this course. Notes will be posted periodically on
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info - CS 229 Machine Learning Handout #1: Course...

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