Additional Resources for Spring 2025:
Lecture Blog Posts in Chronological Order:
(unlinked lecture titles are what is tentatively planned for the future)
- Lecture 1A (2025-01-14): Introduction to the Course, Its Policies, and Its Motivations
- Lecture 1B (2025-01-16): The Evolutionary Approach to Engineering Design Optimization
- Lecture 1C (2025-01-21): Population Genetics of Evolutionary Algorithms
- Lecture 1D (2025-01-28): The Genetic Algorithm and its Implementation, Part 1
- Lecture 1E (2025-01-30): The Genetic Algorithm and its Implementation, Part 2
- Lecture 2A (2025-02-04): GA Approaches Beyond Optimization - Artificial Immune Systems
- Lecture 2B (2025-02-06): GA Approaches Beyond Optimization - AIS and Intro. to Evolutionary Programming
- Lecture 3A (2025-02-11): Introduction to Multi-criteria Decision Making and Pareto Optimality
- Lecture 3B (2025-02-13): Multi-Objective Genetic Algorithms (MOGA and Friends), Part 1
- Lecture 3C (2025-02-18): Multi-Objective Genetic Algorithms (MOGA and Friends), Part 2
- Lecture 4A (2025-02-20): From Multiobjective Genetic Algorithms to DGA, PGA, and Multimodal Optimization
- Lecture 4B (2025-02-25): Niching Methods for Multi-modal Optimization
- Lecture 5A (2025-02-27): Introduction to Simulated Annealing and Maximum Entropy (MaxEnt) Methods
- Lecture 5B (2025-03-04): From MaxEnt Methods to MCMC Sampling
- Lecture 5C (2025-03-18): From MCMC Sampling to Optimization by Simulated Annealing
- Lecture 5D/6A (2025-03-20): Simulated Annealing Wrap-up and Distributed AI and Swarm Intelligence, Part 1 - Ant Colony Optimization (ACO)
- Lecture 6B (2025-03-25): Distributed AI and Swarm Intelligence, Part 2 - ACO and Introduction to Bacterial Foraging Optimization (BFO)
- Lecture 6C (2025-03-27): Distributed AI and Swarm Intelligence, Part 3 - BFO and Intro to Classical Particle Swarm Optimization (PSO) & Its Motivations
- Lecture 6D (2025-04-01): Distributed AI and Swarm Intelligence, Part 4 - Particle Swarm Optimization (PSO)
- Lecture 7A (2025-04-03): Introduction to Neural Networks
- Lecture 7B (2025-04-08): Introduction to Neural Networks: SLP, RBFNN, and MLP
- Lecture 7C (2025-04-10): Deep Neural Networks (UAT, MLP, and Backpropagation)
- Lecture 7D (2025-04-15): CNNs, Insect Brains, More Complex Neural Networks (TDNNs and RNNs)
- Lecture 7E (2025-04-17): RNNs and Their Training, LSTM, and Reservoir Machines
- Lecture 7F (2025-04-22): ANN Reinforcement Learning, Unsupervised Learning, Multidimensional Scaling, & Hebbian/Associative Learning
- Lecture 7G (2025-04-24): Decentralized Associative/Hebbian Learning and Intro. to Spiking Neural Networks and Neuromorphic Computing
- Lecture 7H (2025-04-29): From Spiking Neural Networks to Continual Learning and Beyond
- Lecture 8A (2025-05-01) – Split into two parts:
- Lecture 8A-Intro: Clarifications about Final Exam and Final Project
- Lecture 8A: Complex Systems Approaches to Computation - Interacting Particle Systems and the Voter Model
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