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)
Instructors:
Kundaje, A. (PI)
;
Zou, J. (PI)
BIODS 260A: Workshop in Biostatistics (STATS 260A)
Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Aut

Units: 12

Repeatable for credit

Grading: Medical Satisfactory/No Credit
Instructors:
Palacios, J. (PI)
;
Sabatti, C. (PI)
BIODS 299: Directed Reading and Research
For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum

Units: 118

Repeatable for credit

Grading: Medical Option (MedLtrCR/NC)
Instructors:
Bustamante, C. (PI)
;
Hastie, T. (PI)
;
Olshen, R. (PI)
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Instructors:
Bustamante, C. (PI)
;
Hastie, T. (PI)
;
Olshen, R. (PI)
;
Rivas, M. (PI)
;
Sabatti, C. (PI)
;
Tibshirani, R. (PI)
;
Zou, J. (PI)