Spring 2018

Course Director

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

Teaching Assistant

Shefali Umrania - sumrania-at-andrew.cmu.edu 
Office hours: Fridays, 3-4pm.  Location:  TBD

Meeting Times
First day of class: Tuesday, January 16, 2018. 
Lectures: Tu,Th 4:30-5:50 pm, Margaret Morrison (MM) A14

Course Description & Syllabus

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

Biological systems are too complex for humans to understand, completely, but the practice of biological research has only partially adapted to this realization. In the past, biologists typically studied one gene or protein at a time, and measured the effect of other variables on that gene. As part of the “systems biology” revolution, automated instruments have been developed for carrying out biological experiments at a rate orders of magnitudes faster than humans can do them, but the basic structure of those experiments remains varying one variable while holding all other constant. The result is that many experiments are “wasted” on conditions where no effect is observed, or, more importantly, where the effect could have been predicted from other experiments. Given the impossibility of doing experiments for all combinations of biological variables, new, automated, approaches to biological research and engineering are needed.

This course will cover a range of automated biological research methods and a range of computational methods for automating the acquisition and interpretation of the data (especially active learning). It assumes a basic knowledge of machine learning and biology, although remedial materials will be provided. Class sessions will consist of a combination of lectures and discussions of important research papers. Grading will be based on quizzes, homeworks, and two exams.


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 (20%) 
    • Six quizzes will be given during lectures. The quizzes will be given on Thursdays of most even-numbered weeks.  See the lecture schedule in the left-hand navigation bar for details. 
    • You will need a computer to take each quiz, but you are welcome to take them remotely.  That is, lecture attendance is not required to take quizzes. 
    • Your lowest quiz grade will be dropped to accommodate any traveling that you might have during the semester for interviews, etc.
  • Homework (50%) 
    • Five 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.
    • Undergrad section (02-450): The final homework is optional.  However, you can earn extra credit if you do the final homework.
  • First exam (10%)
    • An in-class exam will be given the final lecture before Spring break. 
  • Second exam (20%)
    • An in-class exam will be given the final lecture of the semester.   


If you are enrolled in 02-450, the following map will be used to determine your final grade:

 Percentage  Letter Grade
 40-54% D 

If you are enrolled in 02-750, the following map will be used to determine your final grade:
 Percentage   Letter Grade
97-100%  A+
 93-96% A  
90-92%  A-
 87-89% B+
 83-86% B 
 80-82% B-
 77-79% C+
 73-76% C 
 70-72% C-
 60-69% D 
 <60% R 

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

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 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.