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MATH 462 Machine Learning (3 credits)

Offered by: Mathematics and Statistics (Faculty of Science)

Overview

Mathematics & Statistics (Sci) : Introduction to supervised learning: decision trees, nearest neighbors, linear models, neural networks. Probabilistic learning: logistic regression, Bayesian methods, naive Bayes. Classification with linear models and convex losses. Unsupervised learning: PCA, k-means, encoders, and decoders. Statistical learning theory: PAC learning and VC dimension. Training models with gradient descent and stochastic gradient descent. Deep neural networks. Selected topics chosen from: generative models, feature representation learning, computer vision.

Terms: This course is not scheduled for the 2024-2025 academic year.

Instructors: There are no professors associated with this course for the 2024-2025 academic year.

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