Fall 2017

Course Director

Christopher James Langmead (Carnegie Mellon University) - cjl-at-cs.cmu.edu
Office hours;  Wednesdays, 1-2pm GHC 7215, or by appointment

Teaching Assistants

Daniel Bork - dbork-at-andrew.cmu.edu 
Office hours: Fridays, 2 – 3 pm, GHC 7405

Zhuo Li-  zhuol1-at-andrew.cmu.edu
Office hours: Tuesdays, 6:30 - 7:30PM, GHC 5417

Kyle Xiong - kxiong-at-andrew.cmu.edu
Office hours: Thursdays, 2 - 3 PM, GHC 7405

Meeting Times
First day of class: Tuesday, August 29, 2017. 
Lectures: Tu,Th 4:30-5:50 pm, Hamerschlag Hall (HH) B103

Course Description & Syllabus

CMU 02-450 (9 units) & CMU 02-750 (12 units)

Biology has become a "big data" science, as biomedical research has been revolutionized by automated methods for generating large amounts of data on diverse biological processes. Integration of data from many types of experiments is required to construct detailed, predictive models of cell, tissue or organism behaviors, and the complexity of the systems suggests that need for these models to be constructed automatically. This requires iterative cycles of acquisition, analysis, modeling, and experimental design, since it is not feasible to do all possible biological experiments. This course will cover a range of automated biological research methods (especially high-throughput, high-content, and robotic methods for gene sequencing, cell-based drug screening, and nanoassays), and a range of computational methods for automating the acquisition and interpretation of the data (especially active learning, proactive learning, compressed sensing and model structure learning). It assumes a basic knowledge of machine learning. Class sessions will consist of a combination of lectures and discussions of important research papers. Grading will be based on quizzes, homeworks, and a final project.


The course is designed for graduate and upper-level undergraduate students with a wide variety of backgrounds.  The course is intended to be self-contained but students may need to do some additional work to gain fluency in core concepts.  Students should have a basic knowledge of biology, programming, statistics, and machine learning. Students must also understand and agree to comply with Carnegie Mellon University's policies on academic integrity  (see also here).

Course Requirements

  • Quizzes (10%) 
    • Six in-class quizzes will be given. The instructor will announce the date of each quiz via email. The lowest quiz grade will be dropped. 
  • Homework (45%) 
    • Six graded assignments based on class lectures and readings.
    • Lateness policy: 25% credit deducted per day for late assignments. Each student will receive 3 days of grace period credit to be distributed over assignments throughout the semester.  Further extensions will be granted only under extreme circumstances.  All assignments must be completed to pass the course.
    • Cheating policy: All work must be your own.  Unauthorized collaboration or plagiarism will result in a negative grade (e.g., a homework worth 100 points will be factored in as a -100 points towards your final grade) and will be reported to your academic advisor and dean.
  • Project (45%) 
    • A project proposal will be due mid-semester. See Proposals for more information.
    • Lateness policy: 50% credit deducted per day for late projects. Grace days cannot be applied to the project.
    • Cheating policy: All work must be your own and novel. You should never cut and paste any text from a published paper, website, etc.  Unauthorized collaboration, falsified data, or plagiarism will result in a failing grade in the course and will be reported to your academic advisor and dean. 
    • Double dipping policy:  You may not re-use data, reports, manuscripts, or publications from your research or from other courses. However, you  may extend your previous work, as long as you inform the instructor that you are doing so. Please contact the instructor if you have any questions regarding this policy.

Course Outline
  1. Introduction and Course Overview
  2. Biology Review
  3. Biotechnology Review
  4. Modeling Biological Systems
  5. Passive Machine Learning
  6. Online Machine Learning
  7. Active Machine Learning
  8. Applications of Active Learning to Biology

Required Text


Course Outcomes

Students who complete the course successfully will be able to:
  • Understand and explain core concepts, theories, and experimental methods in Genomics, Molecular Biology, Cell Biology, and Systems Biology.
  • Understand and explain the concept of regret in online learning
  • Understand, implement, and apply core algorithms for online learning
  • Understand the core data access models and query section strategies used in active learning
  • Understand, implement, and apply core algorithms in active learning
  • Apply knowledge of active learning to select strategies for automating research in Biology, and explain why those strategies are suitable
  • Select, customize, implement, and apply appropriate data structures, algorithms, and software to automate a research objective
  • Evaluate and interpret the results of the approach
  • Understand, explain and critique published papers that employ automation for biological research

Your well-being

Be sure to take care of yourself.  Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

CaPS: 412-268-2922

Re:solve Crisis Network: 888-796-8226

If the situation is life threatening, call the police:

           On campus: CMU Police: 412-268-2323

           Off campus: 911

If you have questions about this or your coursework, please let me know.