Simon Fraser University
Engaging the World

Course Outlines

Spring 2018 - CMPT 884 G100

Special Topics in Database Systems (3)

Mach Learn Life Sciences

Class Number: 10855

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 3 – Apr 10, 2018: Tue, 11:30 a.m.–12:50 p.m.
    Burnaby

    Jan 3 – Apr 10, 2018: Thu, 11:30 a.m.–12:50 p.m.
    Burnaby

Description

COURSE DETAILS:

Special Topics Title:   Machine Learning in Life Sciences

This course will introduce students to state-of-the-art machine learning methods for the life sciences. Prerequisite is familiarity with the basics of data mining/machine learning. The course will be suitable for computer science students with some background in the life sciences and life science students with some background in computational (in particular machine learning) methods. This will be a seminar course, where students will present selected research papers and conduct small research projects (ideally in teams consisting of computer science and life science students).

Topics

  • Probabilistic graphical models
  • Deep neural networks
  • Protein function prediction
  • Biomarker discovery
  • Discovery of causal genes
  • Patient stratification
  • Drug-target interaction prediction

Grading

NOTES:

Will be based on the presentation of research papers and on the course project report, possibly a course project presentation.
There will be no exam.
Details to be discussed in the first class.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://students.sfu.ca/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS