CS 475 Fundamentals of Machine Learning (4)
Selected topics in supervised learning, unsupervised learning, and reinforcement learning: perception, logistic regression, linear discriminant analysis, decision trees, neural networks, naïve Bayes, support vector machines, k-nearest neighbors algorithm, hidden Markov Models, expectation-maximization algorithm, K-means, Gaussian mixture model, bias-variance tradeoff, ensemble methods, feature extraction and dimensionality reduction methods, principle component analysis, Markov decision processes, passive and active learning.
- General Education Requirement Fulfillment: Mathematical Sciences
- Prerequisite: CS 241
- Offering: Alternate years
- Instructor: Staff