## CS 229: Machine Learning (STATS 229)

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.

Terms: Aut, Spr, Sum
| Units: 3-4

Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Ma, T. (PI)
;
Ng, A. (PI)
;
Re, C. (PI)
;
Bakr, S. (TA)
;
Bhaskhar, N. (TA)
;
Cheong, R. (TA)
;
Chinchali, S. (TA)
;
Chou, K. (TA)
;
Geng, Z. (TA)
;
Jung, S. (TA)
;
Kim, C. (TA)
;
Knowles, T. (TA)
;
Liu, C. (TA)
;
Nicholls, A. (TA)
;
Sachidananda, V. (TA)
;
Song, R. (TA)
;
Srikanth, M. (TA)
;
Thomas, G. (TA)
;
Waites, C. (TA)
;
Wolff, C. (TA)
;
Yan, X. (TA)
;
Zhang, J. (TA)

## MATH 151: Introduction to Probability Theory

A proof-oriented development of basic probability theory. Counting; axioms of probability; conditioning and independence; expectation and variance; discrete and continuous random variables and distributions; joint distributions and dependence; Central Limit Theorem and laws of large numbers. This course is pre-approved as an alternative to
CS 109 for the CS major if one later takes
CS 228 or
CS 229. Prerequisite: Either
Math 61CM or
Math 52, and
Math 115 or equivalent.

Terms: Win
| Units: 3
| UG Reqs: GER:DB-Math, WAY-FR

## STATS 229: Machine Learning (CS 229)

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.

Terms: Aut, Spr, Sum
| Units: 3-4

Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Ma, T. (PI)
;
Ng, A. (PI)
;
Re, C. (PI)
;
Bakr, S. (TA)
;
Bhaskhar, N. (TA)
;
Cheong, R. (TA)
;
Chinchali, S. (TA)
;
Chou, K. (TA)
;
Geng, Z. (TA)
;
Jung, S. (TA)
;
Kim, C. (TA)
;
Knowles, T. (TA)
;
Liu, C. (TA)
;
Nicholls, A. (TA)
;
Sachidananda, V. (TA)
;
Song, R. (TA)
;
Srikanth, M. (TA)
;
Thomas, G. (TA)
;
Waites, C. (TA)
;
Wolff, C. (TA)
;
Yan, X. (TA)
;
Zhang, J. (TA)

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