INFO-B 429 Machine Learning for Bioinformatics
3 credits
- Prerequisite(s): INFO-I 223 or AII-I 200 or INFO-B 211 or CSCI-A 205 and PBHL-B 302
- Delivery: On-Campus
Description
This course covers machine learning theories and methods and their application to biological sequence analysis, gene expression data analysis, genomics and proteomics data analysis, and other problems in bioinformatics.
Learning Outcomes
- Access public-domain biological datasets.
- Analyze genomics and proteomics data using decision theories, decision trees, and random forests.
- Analyze gene expression data using linear classification, logistic regression, SVM, clustering, and biclustering.
- Analyze biological sequence data using expectation-maximization methods and hidden Markov models.
- Analyze and visualize biological data sets using R packages for machine learning.
- Design computational experiments for training and evaluating machine learning methods for solving bioinformatics problems.
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.