BNFO 615: Machine learning for bioinformatics
Fall 2017

Instructor: Usman Roshan
Office: GITC 4207
Office hours: MW 1-2:30, Tue 1-3

TA: Yunzhe Xue

Course Overview: We will cover basic Python programming, basic machine learning, and several machine learning applications in bioinformatics and genomics. There will be several programming assignments in Python and Python scikit-learn that will constitute the total grade.

Course plan:

Introduction and Python
Week of 09/06/2017
Nearest means and naive Bayes in Python Week of 09/11/2017
Protein sequence classification Week of 09/18/2017
Protein sequence classification Week of 09/25/2017
Linear classifiers and support vector machines Week of 10/02/2017
SVM software and non-linear classification 10/09/2017
Feature selection 10/16/2017
Dimensionality reduction 10/23/2017
Empirical kernel map for protein sequence classification 10/30/2017
Decision trees and ensemble methods 11/06/2017
Unsupervised learning 11/13/2017
Disease risk prediction 11/20/2017
Regression 11/27/2017
Feature learning for sequence analysis 12/04/2017
Specific topics for the course and relevant papers