Machine Intelligence - Lecture 21 (Naive Bayes, Swarm Intelligence, Ant Colonies)

SYDE 522 - Machine Intelligence (Winter 2019, University of Waterloo)
Target Audience: Senior Undergraduate Engineering Students
Instructor: Professor H.R.Tizhoosh (kimia.uwaterloo...)
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 21 - Naive Bayes, Swarm Intelligence, Ant Colonies

[@lucastejedor8881]

Are you going to post course 22? I'm learning so much with your lectures, so it would be cool if you did it :)

[@Birdsneverfly]

How easy was naive bayes. All the coin tossing examples have become absolutely useless. This lecture is so nice. Thank you