40718 Machine Learning TheoryHamid Beigy, Sharif University of Technology, Spring Semester 201920.
Course DescriptionMachine learning theory concerns questions such as: What kinds of guarantees can we prove about practical machine learning methods, and can we design algorithms achieving desired guarantees? This course answers these questions by studying the theoretical aspects of machine learning, with a focus on statistically and computationally efficient learning. Broad topics will include: PAClearning, uniform convergence, PACBayesian, model selection; supervised learning algorithms including SVM, boosting, kernel methods; online learning algorithms, ranking algorithms, and analysis; unsupervised learning with guarantees. Course Information
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