What genes cause cancer ? Have we inherited genes from Neanderthals ? How does a single genome code for the different cells ?

We can now begin to answer these fascinating questions in biology because the cost of genome sequencing has fallen faster than Moore's law. The bottleneck in answering these questions has shifted from data generation to powerful statistical models and inference algorithms that can make sense of this data. Statistical machine learning provides an important toolkit in this endeavor. Further, biological datasets offer new challenges to the field of machine learning.

We will learn about probabilistic models, inference and learning in these models, model assessment, and interpreting the inferences to address the biological questions of interest. The course aims to introduce CS/Statistics students to an important set of problems and Bioinformatics/Human Genetics students to a rich set of tools.

Familiarity with probability, statistics, linear algebra and algorithms is expected. No familiarity with biology is needed.

Instructor: Sriram Sankararaman

Office Hours: Boelter 4531D, Tuesday 10:00a - 11:00a (or by appointment)

Email: sriram at cs dot ucla dot edu

*Machine Learning: A Probabilistic Perspective*by Kevin Murphy.*Elements of Statistical Learning*by Trevor Hastie, Robert Tibshirani and Jerome Friedman*Biological Sequence Analysis*by Richard Durbin, Sean Eddy, Anders Krogh and Tim Mitchison.*Principles of Population Genetics*by Daniel Hartl and Andy Clark

- Readings: Each class will be assigned one or two readings. At the end of the class, please post a short summary or comments or critiques on the readings to CCLE.
- Scribed lecture notes: Each student will be assigned one lecture to scribe. The scribed lectures will be due one week after the assigned lecture. A latex template will be made available for scribing.
- Homework: There will be three homeworks. Questions on the homework will include programming exercises and data analyses as well as questions drawn from the assigned readings. You are free to use a programming language of your choice though R is preferred. The homeworks must be submitted in hard copy in class on the day they are due. Late submissions will not be accepted.
You are free to discuss the homework problems. However, you must write up your own solutions. You must also acknowledge all collaborators.

- Project: A major component of this course will be an open-ended project. The project can focus on the development of a statistical model/algorithm to a biological problem or application of an existing technique. I will post a list of potential projects on CCLE. You are welcome to propose any project that is relevant to the course, including rotation projects. Each group should decided on their project by the third week. The group will be expected to present their project in class near the end of the quarter and submit a project report.

- Project: 50% (30% paper, 20% presentation)
- Homeworks: 30%
- Scribing: 10%
- Readings: 10%

The course website is based on material developed by Ameet Talwalkar and Fei Sha. Some of the administrative content on the course website is adapted from material from Jenn Wortman Vaughan, Rich Korf, and Alexander Sherstov.