## BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236)

Recent breakthroughs in high-throughput 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 high-throughput 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

## BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236)

Recent breakthroughs in high-throughput 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 high-throughput 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

Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Devorah, A. (TA)
...
more instructors for BIOMEDIN 273B »

Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Devorah, A. (TA)
;
Hussami, N. (TA)
;
Ramesh, J. (TA)

## CS 20SI: Tensorflow for Deep Learning Research

This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments. Prerequisites: CS229 or
CS224D/N.

Terms: Win
| Units: 2

Instructors:
Manning, C. (PI)

## 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
| Units: 3-4

Instructors:
Duchi, J. (PI)
;
Ng, A. (PI)
;
Agrawal, P. (TA)
;
Bhargava, R. (TA)
;
Desai, N. (TA)
;
Dixit, K. (TA)
;
Germain, F. (TA)
;
Ji, J. (TA)
;
Katanforoosh, K. (TA)
;
Kumar, P. (TA)
;
Levy, D. (TA)
;
Meng, C. (TA)
;
Pai, S. (TA)
;
Ruban, T. (TA)
;
Seshadri, A. (TA)
;
Sheng, H. (TA)
;
Tian, Y. (TA)
;
Wang, B. (TA)
;
Wang, D. (TA)
;
Xie, Z. (TA)
;
Yin, Z. (TA)
;
Zhou, B. (TA)
;
Zhu, M. (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 multi-armed 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: Spr
| Units: 3

Instructors:
Duchi, J. (PI)

## CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236)

Recent breakthroughs in high-throughput 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 high-throughput 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

Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Devorah, A. (TA)
;
Hussami, N. (TA)
;
Ramesh, J. (TA)

## GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B)

Terms: Aut
| Units: 3

Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)

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