INFO-B 429 Machine Learning for Bioinformatics
- Prerequisites: INFO-I 223, PBHL-B 302, and BIOL-K 101
- Delivery: On-Campus
- 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.
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.
This course is not being offered this semester.