MIT 6.034 Artificial Intelligence: study plan and how long it takes
The MIT OpenCourseWare playlist is 30 videos and 24.3 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.
30 videos · 24.3 hours · MIT OpenCourseWare
MIT 6.034 (Fall 2010), taught by the late Patrick Winston, is a classic introduction to AI: reasoning and goal trees, search (depth-first, hill climbing, A*), games and minimax, constraints, nearest neighbours, identification trees, neural nets, genetic algorithms, SVMs, boosting, probabilistic inference and representations.
The playlist includes the lectures plus 'Mega-Recitations' that work through exam problems.
Who it's for: Students who want a classic, idea-driven introduction to AI from one of its most famous teachers.
How long does it take?
The full playlist is 24.3 hours across 30 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 49 days | 33 days | 25 days |
| 1 h a day | 25 days | 17 days | 13 days |
| 2 h a day | 13 days | 9 days | 7 days |
Make your own schedule
The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.
Week-by-week schedule
A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 5 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–7 · 5h 38m
Day 1 · 47m
- 11. Introduction and Scope47m
Day 2 · 46m
- 22. Reasoning: Goal Trees and Problem Solving46m
Day 3 · 50m
- 33. Reasoning: Goal Trees and Rule-Based Expert Systems50m
Day 4 · 49m
- 44. Search: Depth-First, Hill Climbing, Beam49m
Day 5 · 49m
- 55. Search: Optimal, Branch and Bound, A*49m
Day 6 · 48m
- 66. Search: Games, Minimax, and Alpha-Beta48m
Day 7 · 49m
- 77. Constraints: Interpreting Line Drawings49m
Week 2Videos 8–14 · 5h 43m
Day 8 · 45m
- 88. Constraints: Search, Domain Reduction45m
Day 9 · 52m
- 99. Constraints: Visual Object Recognition52m
Day 10 · 50m
- 1010. Introduction to Learning, Nearest Neighbors50m
Day 11 · 50m
- 1111. Learning: Identification Trees, Disorder50m
Day 12 · 51m
- 1212a: Neural Nets51m
Day 13 · 49m
- 1312b: Deep Neural Nets49m
Day 14 · 47m
- 1413. Learning: Genetic Algorithms47m
Week 3Videos 15–21 · 5h 43m
Day 15 · 48m
- 1514. Learning: Sparse Spaces, Phonology48m
Day 16 · 47m
- 1615. Learning: Near Misses, Felicity Conditions47m
Day 17 · 50m
- 1716. Learning: Support Vector Machines50m
Day 18 · 52m
- 1817. Learning: Boosting52m
Day 19 · 49m
- 1918. Representations: Classes, Trajectories, Transitions49m
Day 20 · 49m
- 2019. Architectures: GPS, SOAR, Subsumption, Society of Mind49m
Day 21 · 49m
- 2121. Probabilistic Inference I49m
Week 4Videos 22–28 · 5h 51m
Day 22 · 49m
- 2222. Probabilistic Inference II49m
Day 23 · 50m
- 2323. Model Merging, Cross-Modal Coupling, Course Summary50m
Day 24 · 47m
- 24Mega-R1. Rule-Based Systems47m
Day 25 · 52m
- 25Mega-R2. Basic Search, Optimal Search52m
Day 26 · 51m
- 26Mega-R3. Games, Minimax, Alpha-Beta51m
Day 27 · 53m
- 27Mega-R4. Neural Nets53m
Day 28 · 50m
- 28Mega-R5. Support Vector Machines50m
Week 5Videos 29–30 · 1h 23m
Day 29 · 50m
- 29Mega-R6. Boosting50m
Day 30 · 33m
- 30Mega-R7. Near Misses, Arch Learning33m
Tips for finishing
- Watch the Mega-Recitations after their matching lectures; they show how to solve the exam problems.
- Patrick Winston's lecture on how to speak is also worth finding on MIT OCW.