DATA252

Selected topics in supervised learning, unsupervised learning, and reinforcement learning: perceptron, logistic regression, linear discriminant analysis, decision trees, neural networks, naive 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 dimentionality reduction methods, principal component analysis, Markov decision processes, passive and active learning.

Even Years

Spring Semester