Every AI Model Explained in 19 Minutes
A quick explainer for Artificial Intelligence (MIT 6.034) by Explainer Chris and explainerguy01. 19 min. Watch on YouTube
Course: Artificial Intelligence (MIT 6.034) (Computer Science). Covers: search constraint propagation learning neural nets genetic algorithms.
All lectures in Artificial Intelligence (MIT 6.034)
- 1. Introduction and Scope · 47 min
- 2. Reasoning: Goal Trees and Problem Solving · 46 min
- 3. Reasoning: Goal Trees and Rule-Based Expert Systems · 50 min
- 4. Search: Depth-First, Hill Climbing, Beam · 49 min
- 5. Search: Optimal, Branch and Bound, A* · 49 min
- 6. Search: Games, Minimax, and Alpha-Beta · 48 min
- 7. Constraints: Interpreting Line Drawings · 49 min
- 9. Constraints: Visual Object Recognition · 52 min
- 10. Introduction to Learning, Nearest Neighbors · 50 min
- 11. Learning: Identification Trees, Disorder · 50 min
- 12a: Neural Nets · 51 min
- 12b: Deep Neural Nets · 49 min
- 13. Learning: Genetic Algorithms · 47 min
- 14. Learning: Sparse Spaces, Phonology · 48 min
- 15. Learning: Near Misses, Felicity Conditions · 47 min
- 16. Learning: Support Vector Machines · 50 min
- 17. Learning: Boosting · 52 min
- 18. Representations: Classes, Trajectories, Transitions · 49 min
- 19. Architectures: GPS, SOAR, Subsumption, Society of Mind · 49 min
- 21. Probabilistic Inference I · 49 min
- 22. Probabilistic Inference II · 49 min
- 23. Model Merging, Cross-Modal Coupling, Course Summary · 50 min
- Mega-R1. Rule-Based Systems · 47 min
- Mega-R2. Basic Search, Optimal Search · 52 min
- Mega-R3. Games, Minimax, Alpha-Beta · 51 min
- Mega-R4. Neural Nets · 53 min
- Mega-R5. Support Vector Machines · 50 min
- Mega-R6. Boosting · 50 min
- Mega-R7. Near Misses, Arch Learning · 33 min