- School of Computer Science and Engineering, Seoul National University
- Instructor: Prof. Byoung-Tak Zhang
- Office: Online (302-208)
- Main TA: Chang-Hoon Jeong
- E-Mail: chjeong@bi.snu.ac.kr
- Sub TA: Yu-Won Jang, Yun-Hyeok Kwak, Yeon-Ji Song
- Time: Tuesday & Thursday 14:00-15:15
- Objectives:
- To study a wide range of artificial intelligence theory, techniques and systems about machines that behave and think like people.
- To understand concepts, models, and algorithms to develop intelligent agents such as navigation, reasoning, planning, knowledge representation, decision making, learning, visual and language.
- To learn and practice key technologies of artificial intelligence such as empirical search, Bayesian network, Hidden Markov network, and reinforcement learning.
- Syllabus
- 2022 syllabus
- 2022 syllabus
- Textbook
- Artificial Intelligence: A Modern Approach, S. Russell & P. Norvig, Pearson, 2010
- References
- 장병탁, 장교수의 딥러닝, 홍릉과학출판사, 2017
- Evaluation:
- Mid / final terms (60%)
- Project (20%)
- Homework & Practice (20%)
- Lecture Schedule:
| Week | Date | Topics | Slides |
|---|---|---|---|
| 1 | 3/3 | Course Introduction | |
| 3/8 | Lecture 1. Introduction |  |
|
| 2 | 3/10 | Lecture 2. Intelligent Agents |
|
| 3/15 | Lecture 3. Problem Solving by Searching | |
|
| 3 | 3/17 | Lecture 4. Beyond Classical Search | |
| 3/22 | Lecture 5. Adversarial Search |
|
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| 4 | 3/24 | Lecture 6. Logical Agents
Project 1 Announcement |
|
| 3/29 | Lecture 7. First-Order Logic | |
|
| 5 | 3/31 | Lecture 8. Inference in First-Order Logic | |
| 4/5 | Lecture 9. Planning | |
|
| 6 | 4/7 | Lecture 10. Uncertainty and Probability | |
| 4/12 | Lecture 11. Bayesian Networks | |
|
| 7 | 4/14 | Lecture 12. Probabilitic Reasoning over Time | |
| 4/19 | Review | ||
| 8 | 4/21 | Midterm | |
| 4/26 | Lecture 13. Temporal Probability Models
Project 2 Announcement |
|
|
| 9 | 4/28 | Lecture 14. Markov Decision Processes | |
| 5/3 | Lecture 15. Learning from Examples | |
|
| 10 | 5/5 | Children’s Day | |
| 5/10 | Lecture 16. Deep Neural Networks | |
|
| 11 | 5/12 | Lecture 17. Knowledge in Learning | |
| 5/17 | Lecture 18. Learning Probabilistic Models | |
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| 12 | 5/19 | Lecture 19. Reinforcement Learning | |
| 5/24 | Lecture 20. Language | |
|
| 13 | 5/26 | Lecture 21. Vision | |
| 5/31 | Lecture 22. Robotics | |
|
| 14 | 6/2 | Lecture 23. Human Level AI | |
| 6/7 | Review | 15 | 6/9 | Final exam |
| 6/14 | Project 2 Poster Presentation |