Machine Intelligence - Lecture 20 (Bayesian Learning, Bayes Theorem, Naive Bayes)
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 20 - Bayesian Learning, Bayes Theorem, Naive Bayes
Machine Intelligence - Lecture 21 (Naive Bayes, Swarm Intelligence, Ant Colonies)
Machine Intelligence - Lecture 16 (Decision Trees)
Machine Intelligence - Lecture 18 (Evolutionary Algorithms)
Naive Bayes, Clearly Explained!!!
21. Bayesian Statistical Inference I
Machine Intelligence - Lecture 17 (Fuzzy Logic, Fuzzy Inference)
Machine Intelligence - Lecture 7 (Clustering, k-means, SOM)
Super Simple Explanation of Bayes Theorem!
16. Learning: Support Vector Machines
Machine Intelligence - Lecture 9 (Cluster Validity, Probability, Fuzzy Sets, FCM)
Machine Intelligence - Lecture 19 (Opposition-Based Learning, GAs, DE)
Using Bayesian Approaches & Sausage Plots to Improve Machine Learning - Computerphile
Machine Intelligence - Lecture 1 (methods, history, definitions, Turing Test)
Machine Intelligence - Lecture 8 (SOM learning, Support Vector Machines)
Bayes theorem, the geometry of changing beliefs
Terence Tao: Nobody Understands Why AI Actually Works
2. Bayesian Optimization
Eric J. Ma - An Attempt At Demystifying Bayesian Deep Learning
Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)
