INFO-B 429 Machine Learning for Bioinformatics
- Prerequisites: INFO-I 223, PBHL-B 302, and BIOL-K 101
- Delivery: On-Campus
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.
- 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
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