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Machine Learning

402331 - Machine Learning
Faculty

Faculty of Information Technology

Department

Data Science and AI

Credit Hours

3

Pre-requisite

402201

Co-requisite

-

Distribution

3 + 0
This course covers theoretical and practical algorithms for machine learning from a variety of perspectives. In this course, decision tree learning, statistical learning methods, supervised, unsupervised learning and reinforcement learning are covered. The theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning, and Occam s Razor method are also covered. The assignments include hands-on experiments with various learning algorithms.