## BIODS 210: Configuration of the US Healthcare System and the Application of Big Data/Analytics

Almost no country anywhere in the world can afford their healthcare. Starbucks spends more money on healthcare than their coffee. General Motors spends more money on healthcare than steel for their cars. The macro economic effects of healthcare costs along with significant sociodemographic shifts impact every part of our society. In the United States there have been twin massive policy responses (health technology and health payment reform) which are now being coupled with unprecedented venture and industry activity. nnThe purpose of this course will be to understand real world and industry based opportunities within the healthcare system. The course will offer an introductory framework for understanding healthcare configuration encompassing macro economics, health policy 101 and market dynamics. The bulk of the course will focus on the major changes in how (big) data and analytics is being applied in industry and why this matters. There will be an introduction to the most relevant frontier technologies being applied to healthcare data including but not limited to mobility, connectivity, machine learning, "omics". Each technology will be showcased by a seminar with a relevant company outlining their business model application to a care healthcare data technology. nnWhat differentiates this course from other health policy and health data courses:n-- Marketplace overview + trends (Prospective look at new emerging models versus what has previously worked)n-- Industry perspectiven-- Venture perspectiven-- Data in action (industry applications of "big data")n-- Course will be heavily focused on real world/industry based application.

Terms: Aut
| Units: 2
| Repeatable for credit

## 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

## 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: 1-2
| Repeatable for 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: 1-18
| Repeatable for credit

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)