Additional Resources for Spring 2020:
Lecture Blog Posts in Chronological Order:
- Lecture 1A: Introduction to Course, Metaheuristics, and Evolutionary Algorithms (2020-01-13)
- Lecture 1B: Basics of Evolutionary Algorithms and Population Genetics (2020-01-15)
- Lecture 1C: Implementing the Genetic Algorithm, Part 1 (2020-01-22)
- Lecture 1D: Implementing the Genetic Algorithm, Part 2 (2020-01-27)
- Lecture 2A: GA Approaches Beyond Optimization - Artificial Immune Systems (2020-01-29)
- Lecture 2B: GA Approaches Beyond Optimization - Evolutionary Programming (2020-02-03)
- Lecture 3A: Introduction to Multi-criteria Decision Making and Pareto Optimality (2020-02-05)
- Lecture 3B: Multi-Objective Genetic Algorithms (MOGA and Friends), Part 1 (2020-02-10)
- Lecture 3C: Multi-Objective Genetic Algorithms (MOGA and Friends), Part 2 (2020-02-12)
- Lecture 4A: From Multiobjective Genetic Algorithms to DGA, PGA, and Multimodal Optimization (2020-02-17)
- Lecture 4B: Niching Methods for Multi-modal Optimization [audio only] (2020-02-19)
- Lecture 5A: Introduction to Simulated Annealing (2020-02-24)
- Lecture 5B: Physics-Inspired Algorithms and Simulated Annealing (2020-02-26)
- Lecture 5C: From MCMC Sampling to Optimization by Simulated Annealing (2020-03-02)
- Lecture 6A: Distributed AI and Swarm Intelligence, Part 1 - Ant Colony Optimization (ACO) (2020-03-04)
- Lecture 6B: Distributed AI and Swarm Intelligence, Part 2 - Bacterial Foraging Optimization and Intro to Particle Swarm Optimization (PSO) (2020-03-16)
- Lecture 6C: Distributed AI and Swarm Intelligence, Part 3 - Classical Particle Swarm Optimization (PSO) & Its Motivations (2020-03-08)
- Lecture 6D: Distributed AI and Swarm Intelligence, Part 4 - Particle Swarm Optimization (PSO) and friends (2020-03-23)
- Lecture 7A: Introduction to Neural Networks (2020-03-25)
- Lecture 7B: Introduction to Neural Networks: RBF, MLP, & Backpropagation (2020-03-30)
- Lecture 7C: Deep Neural Networks (backpropagation, CNNs, & insect brains) (2020-04-01)
- Lecture 7D: More Complex Neural Networks and their Training (2020-04-06)
- Lecture 7E: Reinforcement and Unsupervised Learning (2020-04-08)
- Lecture 7F: Unsupervised Learning, Multidimensional Scaling, & Hebbian/Associative Learning with Artificial Neural Networks (2020-04-13)
- Lecture 7G: Intro. to Spiking Neural Networks and Neuromorphic Computing (2020-04-15)
- Lecture 7H: Associative Learning, Spiking Neural Networks, and Alternatives to Backpropagation (2020-04-20)
- Lecture 8A: Complex Systems Approaches to Computation - Interacting Particle Systems and the Voter Model (2020-04-22)
- Lecture 8B: Elementary Cellular Automata, Computation, and Experimentation (2020-04-27)
- Lecture 8C: Elementary Cellular Automata, Stochastic Cellular Automata, Ising models, and Cellular Evolutionary Algorithms (2020-04-29)
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