Introduction to Artificial Intelligence

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Contents


Course Resources

Exams

External Programming Exercises

Downloadable Materials

You can download Supplemental Materials, Lesson Videos and Transcripts from Downloadables (bottom right corner of the Classroom) or from the Dashboard (first option on the navigation bar on the left hand side).

Course Syllabus

There are 11 weeks worth of lectures (2 Lessons per week) and 8 problem sets.
22 Lessons, 8 Problem Sets, 1 Midterm exam, 1 Final Assessment
Detailed Syllabus

Part I: Fundamentals of Artificial Intelligence

Lesson 1 Welcome to AI

Course Notes: 1. Welcome to AI

Lesson 2 Problem Solving

Course Notes: 2. Problem Solving

Problem Set 1

Lesson 3 Probability in AI

Course Notes: 3. Probability in AI

Lesson 4 Probabilistic Inference

Course Notes: 4. Probabilistic Inference

Problem Set 2

Lesson 5 Machine Learning

Course Notes: 5. Machine Learning

Lesson 6 Unsupervised Learning

Course Notes: 6. Unsupervised Learning

Problem Set 3

Lesson 7 Representation with Logic

Course Notes: 7. Representation with Logic

Lesson 8 Planning

Course Notes: 8. Planning

Problem Set 4

Lesson 9 Planning under Uncertainty

Course Notes: 9. Planning under Uncertainty

Lesson 10 Reinforcement Learning

Course Notes: 10. Reinforcement Learning

Problem Set 5

Lesson 11 Hidden Markov Models and Filters

Course Notes: 11. Hidden Markov Models and Filters

Lesson 12 Markov Decision Process Review

Course Notes: 12. Markov Decision Process Review

Midterm

Lesson 13 Games

Course Notes: 13. Games

Lesson 14 Game Theory

Course Notes: 14. Game Theory

Problem Set 6

Lesson 15 Advanced Planning

Part II: Applications of Artificial Intelligence

Lesson 16 Computer Vision I

Lesson 17 Computer Vision II

Lesson 18 Computer Vision III

Problem set 7

Lesson 19 Robotics I

Lesson 20 Robotics II

Problem set 8

Lesson 21 Natural Language Processing I

Lesson 22 Natural Language Processing II

Final Assessment