This repository is a collection of notes I wrote when I took the CS 6601: Artificial Intelligence, CS 7642: Reinforcement Learning and CS 7641: Machine Learning courses at Georgia Tech. They're a concise, high-level description of the concepts taught on those courses.
- Initial Concepts
- Games
- Search
- Optimization
- Uncertainty
- Machine Learning
- Temporal Pattern Recognition
- Reinforcement Learning
- Planning
I am by no means an AI expert. The notes may contain gross oversimplification, lack of depth or plain errors. If you find any mistakes, I apologize in advance (and appreciate if you let me know).
Most of the notes are based on the AI course. I later expanded the notes to include concepts of the other courses. I've not covered the parts of the AI involving logic.
As a copyright aside, the notes are heavily based on the textbook used in the course (Artificial Intelligence: A Modern Approach, 3rd Edition, by Stuart Russel and Peter Norvig). In fact, some of the notes' paragraphs are literal copies of passages of the book. The LaTeX formulas have been generated using https://latex.codecogs.com. Other references are cited in the notes. All those references have been done in good faith and for educational purposes only.
This work is licensed under the MIT License.