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Automation of Scientific Research

Course Number: 02-750

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.

Prerequisite Knowledge: The course is designed for graduate and upper-level undergraduate students with a wide variety of backgrounds.

Units: 12
Prerequisite(s): (10601) or (10701) or (02620)

Learning Objectives

• 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

Assessment Structure:

Grading will be based on class participation, homework, and a final project.

1. 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.

• 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.

• An in-class exam will be given the final lecture before Spring break.

• An in-class exam will be given the final lecture of the semester.