Course Syllabus

 David Koes 
 748 Murdoch Building
 Maria Chikina
 833 Murdoch Building


Teaching Assistant:

Michael Gorczyca


Course Description

This course will focus on the practical aspects of effectively applying state-of-the-art machine learning methods at scale to large, biomedically relevant datasets.
High-throughput techniques are revolutionizing biomedical research.  From whole genome sequencing, to RNA-Seq transcriptome profiling, to high-throughput mass spectrometry for protein profiling, to high-throughput biochemical screening, to flow cytometry for cell profiling, to high-content screening, to literature analysis and electronic medical records, from molecule to patient, modern techniques generate vast quantities of data.  As machine learning is the science of finding and applying patterns in data, it is an essential tool for turning data into knowledge and actionable insights and has been rising in prominence in biomedical research.  In order to be effective, biomedical researchers require the appropriate computational tools to correctly interpret and utilize this data.



Course material will be posted to Canvas: Slack  will be used for group discussions, announcements, and contacting staff:


Lectures will be 1:00-2:20pm on Tuesdays and Thursdays.  Lectures will be in-person in the Murdoch 814 classroom when the University of Pittsburgh adopts a fully in-person teaching modality (currently scheduled to start January 27).   Remote instruction will be over Zoom.


Recitations will be 3:00pm–3:50pm on Wednesdays in the Murdoch 814 classroom and/or on Zoom.  Recitations will be a mix of practical, in-class projects and lectures.  

Class Recordings

All lectures and recitations will be recorded and available for asynchronous viewing on Panopto, but this is not intended as a substitute for attending class. Students should make every reasonable effort to attend class in real-time as in-class group work and discussion is an important part of lecture.  


There will be 8 assignments. They are the heart and soul of the course.  All assignments will take the form of a "friendly competition" where students submit their solutions and have their (anonymous) results posted to a leaderboard.  There will always be preset thresholds for achieving an "A" (95) quality solution.  Students work individually on the assignments.  Undergraduates may choose to work on assignments in groups with the permission of the instructors.


There will be two take-home quizzes. These quizzes will be open book/notes/internet but not open classmate. All students must complete the quizzes without assistance from others.  Quizzes are expected to take less than an hour to complete, but students will have a 24 hour window to take each quiz.


A final overall score of at least 93% will be required for an A and at least 83% for a B.  Assignment grades will be based on the final status of the leaderboard (not the best scoring solution submitted).

80% Assignments
15% Quizzes
5% Class Participation (includes Journal Club presentation)


Assignments should be handed in on-time.  When this is not possible, course instructors should be contacted with as much advance notice as possible.  In general, requests for one late day per an assignment for no more than two total assignments will be approved.  Requests beyond that will require substantial justification and/or be subject to additional grade penalties.  Late assignments will have a maximum possible score of 95%.

Academic Honesty 

You must do all your own work.  You are encouraged to discuss general concepts, strategies for debugging, and the particulars of a specific software package with other class members.  However, specifics of individual assignments should not be discussed, and you should not show your code to fellow classmates.  Any attempt to "hack" the autograder will result in expulsion from the class and a referral to the dean's office.


All University policies will be followed and will take precedence over any course policies.  See for more information.  Once in-person instruction begins, students with a need to attend remotely must notify the instructors in advance and they will be provided with the Zoom password.