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Computer Science

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

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