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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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*Sign up*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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