
Learning: Support Vector Machines
49:34Learning: Boosting Algorithms
51:40Learning: Near Misses and Felicity Conditions in AI
46:54Learning: Sparse Spaces and Phonological Generalization
47:49Genetic Algorithms: MIT Artificial Intelligence
47:16Deep Neural Networks: MIT Artificial Intelligence
49:06Neural Networks and Backpropagation
50:43Learning: Identification Trees and Disorder
49:37Patrick Winston's MIT lecture explores genetic algorithms, examining how populations evolve towards desirable traits through fitness and diversity ranking.
MIT 6.034 Artificial Intelligence, Fall 2010
This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. We briefly discuss how this space is rich with solutions.