Machine Intelligence - Lecture 18 (Evolutionary Algorithms)
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 18 - Evolutionary Algorithms
Machine Intelligence - Lecture 19 (Opposition-Based Learning, GAs, DE)
Machine Intelligence - Lecture 17 (Fuzzy Logic, Fuzzy Inference)
Introduction to Evolutionary Algorithms
Machine Intelligence - Lecture 1 (methods, history, definitions, Turing Test)
13. Learning: Genetic Algorithms
Yann LeCun | Self-Supervised Learning, JEPA, World Models, and the future of AI
Core Ideas in Artificial Intelligence: From Perceptron to Transformers
Machine Intelligence - Lecture 20 (Bayesian Learning, Bayes Theorem, Naive Bayes)
Evolutionary computation: Keith Downing at TEDxTrondheim
Gradient Descent vs Evolution | How Neural Networks Learn
Machine Intelligence - Lecture 12 (Problems of Learning, RBMs, Autoencoders)
Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)
Machine Intelligence - Lecture 21 (Naive Bayes, Swarm Intelligence, Ant Colonies)
Ethics of Artificial Intelligence - Part 1 :: Machine Intelligence Course, Lecture 23
Why Peter Scholze is once in a Generation Mathematician
Evolutionary Algorithms Explained And Explored
The Knapsack Problem & Genetic Algorithms - Computerphile
A Gentle Introduction to Genetic Algorithms with Python and DEAP
Lec 01. Introduction to Deep Learning
