printer friendly pageCS 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, Win
| Units: 3-4
Instructors:
Charikar, M. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
...
more instructors for CS 229 »
Instructors:
Charikar, M. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
;
Ng, A. (PI)
;
Schmidt, L. (PI)
;
Chen, E. (TA)
;
Chi, R. (TA)
;
Ding, Z. (TA)
;
Marx, C. (TA)
;
Zhang, P. (TA)
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, Win, Spr
| 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)
;
Chen, E. (TA)
;
Chi, R. (TA)
;
Ding, Z. (TA)
;
Marx, C. (TA)
;
Zhang, P. (TA)
STATS 270: Bayesian Statistics (STATS 370)
This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference. Prerequisites:
Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended;
Stats 200 or equivalent statistical theory course desirable.
Terms: Aut
| Units: 3
STATS 370: Bayesian Statistics (STATS 270)
This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference. Prerequisites:
Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended;
Stats 200 or equivalent statistical theory course desirable.
Terms: Aut
| Units: 3
Filter Results: