Statistical Modeling & Machine Learning for Biological Analyses
Overview:
This course will introduce graduate students in molecular
biology and life sciences to major concepts in statistical modeling
and machine learning, through practical assignments using the R
statistics package. Specific topics covered include
regression, classification, probabilistic models and clustering. The
course is aimed at graduate students planning to work in the areas
of genomics and computational biology.
This course is offered jointly by the Department of
Molecular Genetics and the Department of Cell &
Systems Biology at the University of Toronto.
Instructors are Quaid
Morris and Alan
Moses.
Course Information:
- Lectures will be held Wednesdays from 10:00am to 12:00pm in
CCBR Black room
- Tutorials (optional) will be held on Tuesdays at 4pm on March
1st and March 8th (location TBA)
Evaluation:
4 problem sets to be completed using the R statistics package.
Each will be worth 25% of the final grade. Assignments should
submitted by email to the course email address which is ml4bio at
gmail.
Lecture Schedule and important
links for 2016:
Feb. 24th. Welcome
& Lecture 1 - Introduction to Machine Learning
Assignment #0 will not be graded,
but is meant to get you started using R and to give you a sense of
what the problem sets will be like
March 2nd. Lecture
2 - Clustering
Assignment #1 is due on Tuesday
March 15th.
March 9th. Lecture
3 - Dimensionality Reduction
March 16th. Lecture
4 - Classification
March 23rd. Lecture
5 - Regression
March 30th.
Lecture 6 - Advanced topics: probability models for machine learning