BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude
more »
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS109, and basic machine learning such as
CS229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
...
more instructors for BIODS 237 »
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
;
Liu, R. (TA)
;
Palamuttam, R. (TA)
BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude
more »
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS109, and basic machine learning such as
CS229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
...
more instructors for BIOMEDIN 273B »
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
;
Liu, R. (TA)
;
Palamuttam, R. (TA)
CS 229: Machine Learning (STATS 229)
Topics: statistical pattern recognition, linear and nonlinear regression, nonparametric 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: 34

Grading: Letter or Credit/No Credit
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)
;
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)
;
Patil, I. (TA)
;
Sankar, V. (TA)
;
Voisin, M. (TA)
;
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 (kmeans), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flippedclassroom format. You will watch videos and complete indepth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an openended 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: 34

Grading: Letter or Credit/No Credit
Instructors:
Ng, A. (PI)
;
Bensouda Mourri, Y. (TA)
CS 229T: Statistical Learning Theory (STATS 231)
How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include classical asymptotics, method of moments, generalization bounds via uniform convergence, kernel methods, online learning, and multiarmed bandits. Prerequisites: A solid background in linear algebra and probability theory, statistics and machine learning (
STATS 315A or
CS 229). Convex optimization (
EE 364A) is helpful but not required.
Terms: not given this year, last offered Spring 2017

Units: 3

Grading: Letter or Credit/No Credit
CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude
more »
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS109, and basic machine learning such as
CS229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
;
Liu, R. (TA)
;
Palamuttam, R. (TA)
CS 332: Advanced Survey of Reinforcement Learning
This class will provide a core overview of essential topics and newnresearch frontiers in reinforcement learning. Planned topics include:nmodel free and model based reinforcement learning, policy search, MontenCarlo Tree Search planning methods, off policy evaluation, exploration,nimitation learning, temporal abstraction/hierarchical approaches, safetynand risk sensitivity, humanintheloop RL, inverse reinforcementnlearning, learning to communicate, and insights from human learning.nStudents are expected to create an original research paper on a relatedntopic. Prerequisites: CS221 or
AA238/CS238 or CS234 or CS229 or similarnexperience.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B)
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Stude
more »
Recent breakthroughs in highthroughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and highthroughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as
CS109, and basic machine learning such as
CS229. No prior knowledge of genomics is necessary.
Terms: Aut

Units: 3

Grading: Medical Option (MedLtrCR/NC)
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
...
more instructors for GENE 236 »
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Ghorbani, A. (TA)
;
Liu, R. (TA)
;
Palamuttam, R. (TA)
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