This page and the main course page serve as syllabus for this class.


Major advances in technology for genomic studies are bringing the prospect of personalized and individualized medicine closer to reality. Many of these advances are predicated on the ability to generate data at an unprecedented rate, posing a significant need for computational data analysis that is clinically and biologically useful and robust.

This course will concentrate on the fundamental computational and statistical methods required to meet this need. It will cover topics in functional genomics, population genetics and epigenetics. Computational methods studied for this type of analysis include: supervised, unsupervised and semi-supervised learning, data visualization, statistical modeling and inference, probabilistic graphical models, sparse methods, and numerical optimization. Machine learning methods will be a core component of this class. No prior knowledge of biology is required.

Course Information

Course Calendar

The course calendar including material to be covered is available in the course homepage.



You will be working in teams of 2-4 students in this class, where each team will carry out three tasks: a) present a paper in class, b) lead discussion of a paper presented by another team in class, c) the final project.

You can find more information about each task below. Form teams using the piazza class page. Teams need to be formed by the end of the second week of class. More information about team formation will be given in the first lecture.

In-class Paper Presentations

Each team will present one paper during the semster. The (30 minute) presentation should center on methods and techniques, as the instructor will introduce required Biology concepts beforehand. For each presentation, another team will be assigned to lead discussion. This team should be prepared to ask questions, provide clarifications, and/or add perspective as required. More information about paper assignments will be given in the first lecture.

Preliminary Project Guidelines

A substantial amount of the evaluation in this course (20%) will consist of a project. In general you can choose from four types of projects:

  1. Algorithm/method design: Design and provide a preliminary implementation of an algorithm or method to analyze a particular type of genomic assay
  2. Algorithm/method application: Apply existing algorithms or methods to existing genomic datasets
  3. Literature review: Review the existing methods for a particular analysis task
  4. Other: Any other approved project analyzing high-throughput genomic assays

More information about projects and possible project topics will be provided later in the semester.


Coursework policies

Absence policies

Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall: a) Make a reasonable attempt to inform the instructor of his/her illness prior to the class. b) Upon returning to the class, present their instructor with a self-signed note attesting to the date of their illness. Each note must contain an acknowledgment by the student that the information provided is true and correct. Providing false information to University officials is prohibited under Part 9(i) of the Code of Student Conduct (V-1.00(B) University of Maryland Code of Student Conduct) and may result in disciplinary action.

The self-documentation may not be used for the Major Scheduled Grading Events as defined below and it may only be used for only 1 class meeting (or more, if you choose) during the semester. Any student who needs to be excused for a prolonged absence (2 or more consecutive class meetings), or for a Major Scheduled Grading Event, must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. In addition, it must contain the name and phone number of the medical service provider to be used if verification is needed. No diagnostic information will ever be requested. The Major Scheduled Grading Events for this course include: a) Paper presentation - per presentation schedule b) Project presentation - 5/6,5/8 or 5/13 per presentation schedule

Other policies