Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.
More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/
More tutorial at
http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm
http://www.raetschlab.org/lectures/MLBioinformatics
http://www.raetschlab.org/lectures/bertinoro08
Book at
https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf
Comments
Deep learning in bioinformatics @ https://arxiv.org/pdf/1603.06430.pdf