Course Syllabus

Instructors:
 David Koes 
 748 Murdoch Building
dkoes@pitt.edu
 
 Maria Chikina
 833 Murdoch Building
 mchikina@pitt.edu

Course Description

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.  In order to be effective, biomedical researchers require the appropriate computational tools to correctly interpret and utilize this 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.  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.

Communication 

Course material will be posted to Canvas. Slack  will be used for group discussions, announcements, and contacting staff: http://mscbio2066.slack.com/. 

Lectures

Lectures will be 1:00-2:30pm on Tuesdays and Thursdays.  All lectures will be held over Zoom until COVID19 conditions permit in-person learning. 

Recitations

Recitations will be at 1:00pm on Wednesdays.  Recitations will often be practical, in-class projects.  Students may work on the in-class project in breakout rooms.  Later recitations will take the form of a journal club with students presenting a recent publication.

Assignments

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' quality solution.  Students work individually on the assignments.  Undergraduates may choose to work on assignments in groups with the permission of the instructors.

Grades

A final overall score of at least 92% will be required for an A and at least 80% for a B.

     80%  Assignments  
     10% Class Participation (includes Journal Club presentation) 
     10% Final  

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.

COVID19

All University policies will be followed and will take precedence over any course policies.  See \url{https://www.coronavirus.pitt.edu/} for more information.  All lectures will be streamed over Zoom.  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.  However, all lectures will be recorded and available for asynchronous viewing on Panopto (this will include in-person lectures, should they occur).