printer friendly pageSTATS 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, Win
| Units: 3-4
Instructors:
Charikar, M. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
...
more instructors for STATS 229 »
Instructors:
Charikar, M. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
;
Ng, A. (PI)
;
Schmidt, L. (PI)
;
Band, N. (TA)
;
Chen, E. (TA)
;
Chi, R. (TA)
;
Ding, Z. (TA)
;
Fifty, C. (TA)
;
Li, R. (TA)
;
Marx, C. (TA)
;
Sahoo, R. (TA)
;
Zhang, B. (TA)
;
Zhang, P. (TA)
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