Spring 2019

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

Trevor Frisby: 
Office hours: Mondays, 1-2pm, GHC 7416

Mo Li: 
Office hours: Saturdays, 11am-12, GHC 7416

Meeting Times
First day of class: Tuesday, January 15, 2019. 
Lectures: Tu,Th 4:30-5:50 pm, Gates-Hillman (GHC) 4211

Course Description & Syllabus

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

Automated scientific instruments are used widely in research and engineering. Robots dramatically increase the reproducibility of scientific experiments, and are often cheaper and faster than humans, but are most often used to execute brute-force sweeps over experimental conditions.  The result is that many experiments are “wasted” on conditions where the effect could have been predicted. Thus, there is a need for computational techniques capable of selecting the most informative experiments. 

This course will introduce students to techniques from Artificial Intelligence and Machine Learning for automatically selecting experiments to accelerate the pace of discovery and to reduce the overall cost of research.  Real-world applications from Biology, Bioengineering, and Medicine will be studied.  Grading will be based on homeworks and two exams.  The course is intended to be self-contained but students should have a basic knowledge of biology, programming, statistics, and machine learning.


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 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 roughly every two weeks, starting in week 3.  See the lecture schedule in the left-hand navigation bar for details. 
    • 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 will be given. They will be based on class lectures and readings.
    • Each student will receive a credit of 3 grace days to be applied to assignments. You do not need to ask permission to use late days, they will be deducted automatically.  
    • Lateness policy: After your grace days have been exhausted, a 25% penalty will be applied for each day beyond the official due date. 
    • 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 fifth homework is optional for those enrolled in 02-450.  However, you can earn up to 2.5% (two and one half percent) 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. Modeling Biological Systems
  3. Online Machine Learning 
  4. Active Machine Learning
  5. Applications of Active Learning to Automate 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.