## 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, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: linear algebra, and basic probability and statistics.

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

Instructors:
Avati, A. (PI)
;
Ma, T. (PI)
;
Ng, A. (PI)
;
Re, C. (PI)
;
Amidi, S. (TA)
;
Avati, A. (TA)
;
Bakr, S. (TA)
;
Bereket, M. (TA)
;
Bhaskhar, N. (TA)
;
Chinchali, S. (TA)
;
Chute, C. (TA)
;
Colas, G. (TA)
;
Dao, T. (TA)
;
Duan, T. (TA)
;
Dwaracherla, V. (TA)
;
Farhangi, A. (TA)
;
Gao, X. (TA)
;
Geng, Z. (TA)
;
Hua, X. (TA)
;
Huot, F. (TA)
;
Jung, S. (TA)
;
Kaur, J. (TA)
;
Kim, A. (TA)
;
Kim, C. (TA)
;
Koochak, Z. (TA)
;
Li, H. (TA)
;
Lin, C. (TA)
;
Liu, C. (TA)
;
Pandey, S. (TA)
;
Parulekar, A. (TA)
;
Paul, S. (TA)
;
Ramamoorthy, A. (TA)
;
Song, R. (TA)
;
Srouji, M. (TA)
;
Starosta, A. (TA)
;
Steinberg, E. (TA)
;
Townshend, R. (TA)
;
WANG, I. (TA)
;
Wang, C. (TA)
;
Wang, Y. (TA)
;
Wu, X. (TA)
;
Yeh, C. (TA)
;
Yerukola, A. (TA)
;
Zhang, J. (TA)

## CS 229A: Applied Machine Learning

You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as
Math 51.

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

Instructors:
Bensouda Mourri, Y. (PI)
;
Katanforoosh, K. (PI)
;
Ng, A. (PI)
...
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Instructors:
Bensouda Mourri, Y. (PI)
;
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Bao, M. (TA)
;
Bensouda Mourri, Y. (TA)
;
Legros, F. (TA)
;
Magon de La Villehuchet, P. (TA)

## CS 229T: Statistical Learning Theory (STATS 231)

How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. We will present various learning algorithms and prove theoretical guarantees about them. Topics include generalization bounds, implicit regularization, the theory of deep learning, spectral methods, and online learning and bandits problems. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (
STATS 315A or
CS 229).

Terms: Aut
| Units: 3

## CS 332: Advanced Survey of Reinforcement Learning

This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic. Prerequisites: CS221 or
AA238/CS238 or CS234 or CS229 or similar experience.

Terms: Aut
| Units: 3

Instructors:
Brunskill, E. (PI)
;
Zanette, A. (TA)

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