Machine Intelligence - Lecture 15 (Reinforcement Learning, Q-Learning)

Автор: Kimia Lab

Загружено: 2019-03-29

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SYDE 522 – Machine Intelligence (Winter 2019, University of Waterloo)

Target Audience: Senior Undergraduate Engineering Students

Instructor: Professor H.R.Tizhoosh (http://kimia.uwaterloo.ca/)

Course Outline - The objective of this course is to introduce the students to the main concepts of machine intelligence as parts of a broader framework of “artificial intelligence”. An overview of different learning, inference and optimization schemes will be provided, including Principal Component Analysis, Support Vector Machines, Self-Organizing Maps, Decision Trees, Backpropagation Networks, Autoencoders, Convolutional Networks, Fuzzy Inferencing, Bayesian Inferencing, Evolutionary algorithms, and Ant Colonies.

Lecture 15 - Reinforcement Learning, Q-Learning

Machine Intelligence - Lecture 15 (Reinforcement Learning, Q-Learning)

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