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, 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
| Units: 3-4
Instructors:
Boneh, D. (PI)
;
Ng, A. (PI)
;
Qu, S. (PI)
;
Avati, A. (TA)
;
Cho, P. (TA)
;
Dery, L. (TA)
;
Dwaracherla, V. (TA)
;
Genthial, G. (TA)
;
Haque, A. (TA)
;
Heereguppe Radhakrishna, S. (TA)
;
Huang, J. (TA)
;
Irvin, J. (TA)
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Jiang, Q. (TA)
;
Koochak, Z. (TA)
;
Le Calonnec, Y. (TA)
;
Legros, F. (TA)
;
Li, H. (TA)
;
Liu, V. (TA)
;
Liu, X. (TA)
;
Mahajan, A. (TA)
;
Mehra, S. (TA)
;
Meng, C. (TA)
;
Oshri, B. (TA)
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Patil, I. (TA)
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Sankar, V. (TA)
;
Voisin, M. (TA)
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Wu, Y. (TA)
;
Xie, Z. (TA)
;
Yue, C. (TA)
;
Zhang, B. (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: Spr
| Units: 3-4
Instructors:
Ng, A. (PI)
;
Bensouda Mourri, Y. (TA)
CS 230: Deep Learning
Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. 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 in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications).
CS 229 may be taken concurrently.
Terms: Aut, Win, Spr
| Units: 3-4
Instructors:
Katanforoosh, K. (PI)
;
Ng, A. (PI)
;
Bagul, A. (TA)
;
Barnes, Z. (TA)
;
Bartolome Aramburu, C. (TA)
;
Cho, P. (TA)
;
Dery, L. (TA)
;
Eng, D. (TA)
;
Genthial, G. (TA)
;
Heereguppe Radhakrishna, S. (TA)
;
Hemmati, S. (TA)
;
Kaplan, R. (TA)
;
Keramati, R. (TA)
;
Koochak, Z. (TA)
;
Le Calonnec, Y. (TA)
;
Liu, X. (TA)
;
Mahajan, A. (TA)
;
Moindrot, O. (TA)
;
Momeni, A. (TA)
;
Nair, S. (TA)
;
Shenoi, A. (TA)
;
Whang, J. (TA)
;
Yang, B. (TA)
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