Machine Learning

Catalog description

Introduction to Machine Learning, covering key algorithms in supervised, unsupervised, and reinforcement learning, such as Kernel Methods, Bayesian Networks, Hidden Markov Models, K-Means, etc. The class will also address key concepts and challenges in Machine Learning, such as the biasvariance tradeoff, generalization, regularization, boosting, etc. The course is project-based, with a focus on application in computational biology/bioinformatics. A basic knowledge of statistics and probability is a must.