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
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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)
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. 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)
;
Devorah, A. (TA)
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more instructors for BIOMEDIN 273B »
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
;
Devorah, A. (TA)
;
Hussami, N. (TA)
;
Ramesh, J. (TA)
CS 109: Introduction to Probability for Computer Scientists
Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of
MATH 51 or
CME 100 or equivalent.
Terms: Win, Spr, Sum

Units: 35

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Monroe, W. (PI)
;
Piech, C. (PI)
;
Sahami, M. (PI)
;
Corcoran, B. (TA)
;
Liu, Y. (TA)
;
Meister, M. (TA)
CS 109L: Statistical Computing with R Laboratory
Supplemental lab to
CS109. Introduces the R programming language for statistical computing. Topics include basic facilities of R including mathematical, graphical, and probability functions, building simulations, introductory data fitting and machine learning. Provides exposure to the functional programming paradigm. Corequisite:
CS109.
Terms: not given this year

Units: 1

Grading: Satisfactory/No Credit
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, genome sequences, social networks. Methods include: string algorithms, edit distance, language modeling, the noisy channel, naive Bayes, inverted indices, collaborative filtering, PageRank. Applications such as question answering, sentiment analysis, information retrieval, text classification, social network models, chatbots, genomic sequence alignment, spell checking, speech processing, recommender systems. Prerequisite:
CS103,
CS107,
CS109.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Jurafsky, D. (PI)
CS 246: Mining Massive Data Sets
The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Topics include: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommender Systems, Clustering, Link Analysis, Largescale machine learning, Data streams, Analysis of Socialnetwork Graphs, and Web Advertising. Prerequisites: At lease one of CS107 or
CS145; At least one of CS109 or STAT116, or equivalent.
Terms: Win

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Leskovec, J. (PI)
;
Ullman, J. (PI)
CS 250: Error Correcting Codes: Theory and Applications (EE 387)
Introduction to the theory of error correcting codes, emphasizing diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; constructions like ReedSolomon, ReedMuller, and expander codes; listdecoding, listrecovery and locality. Applications include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178), and ¿mathematical maturity¿ (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wootters, M. (PI)
;
Deligiannis, P. (TA)
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)
;
Devorah, A. (TA)
;
Hussami, N. (TA)
;
Ramesh, J. (TA)
EE 387: Error Correcting Codes: Theory and Applications (CS 250)
Introduction to the theory of error correcting codes, emphasizing diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; constructions like ReedSolomon, ReedMuller, and expander codes; listdecoding, listrecovery and locality. Applications include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178), and ¿mathematical maturity¿ (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wootters, M. (PI)
;
Deligiannis, P. (TA)
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)
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