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Introduction to Machine Learning
Instructor: Ruslan Satakhutdinov
Department: Computer Science
Institution: University of Toronto
Platform: Independent
Year: 2015
Price: Free
Textbook:
Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.
Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. 2009.
David MacKay. Information Theory, Inference, and Learning Algorithms. 2003.
Description:
This course covers some of the theory and methodology of statistical aspects of machine learning. The preliminary set of topics to be covered include: 1. Linear methods for regression 2. Linear models for classification 3. Probabilistic Generative and Discriminative models 4. Regularization methods 5. Neural Networks 6. Support Vector Machines 7. Mixture models and EM algorithm 8. Reinforcement learning