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Machine Intelligence - Lecture 11 (Backpropagation, Topology, Overfitting, Autoencoders)
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SYDE 522 – Machine Intelligence (Winter 2019, University of Waterloo)
Target Audience: Senior Undergraduate Engineering Students
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 11 - Backpropagation, Topology, Overfitting, Autoencoders
Target Audience: Senior Undergraduate Engineering Students
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.
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