Machine Intelligence - Lecture 7 (Clustering, k-means, SOM)
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 7 - Clustering (k-means, self-organizing maps)
Видео Machine Intelligence - Lecture 7 (Clustering, k-means, SOM) канала Kimia Lab
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 7 - Clustering (k-means, self-organizing maps)
Видео Machine Intelligence - Lecture 7 (Clustering, k-means, SOM) канала Kimia Lab
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