- School of Computer Science and Engineering, Seoul National University
- Instructor: Prof. Byoung-Tak Zhang
- TA: Youwon Jang
- E-Mail: ywjang@bi.snu.ac.kr
- Classroom: 302-208
- Time: Tue & Thu 11:00-12:15
- 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
- 2021 syllabus
- 2021 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 | 3/2(Tue) | Course Introduction | |
| 3/4(Thu) | Lecture 1. Introduction |  |
|
| 2 | 3/9(Tue) | Lecture 2. Intelligent Agents |
|
| 3/11(Thu) | Lecture 3. Problem Solving by Searching | |
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| 3 | 3/16(Tue) | Lecture 4. Beyond Classical Search | |
| 3/18(Thu) | Lecture 5. Adversarial Search |
|
|
| 4 | 3/23(Tue) | Lecture 6. Logical Agents
Project 1 Announcement |
|
| 3/25(Thu) | Lecture 7. First-Order Logic | |
|
| 5 | 3/30(Tue) | Lecture 8. Inference in First-Order Logic | |
| 4/1(Thu) | Lecture 9. Planning | |
|
| 6 | 4/6(Tue) | Lecture 10. Uncertainty and Probability | |
| 4/8(Thu) | Lecture 11. Bayesian Networks | |
|
| 7 | 4/13(Tue) | Lecture 12. Probabilitic Reasoning over Time | |
| 4/15(Thu) | Review | ||
| 8 | 4/20(Tue) | Midterm | |
| 4/22(Thu) | Lecture 13. Temporal Probability Models
Project 2 Announcement |
|
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| 9 | 4/27(Tue) | Lecture 14. Markov Decision Processes | |
| 4/29(Thu) | Lecture 15. Learning from Examples | |
|
| 10 | 5/4(Tue) | Lecture 16. Deep Neural Networks | |
| 5/6(Thu) | Lecture 17. Knowledge in Learning | |
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| 11 | 5/11(Tue) | Lecture 18. Learning Probabilistic Models | |
| 5/13(Thu) | Lecture 19. Reinforcement Learning | |
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| 12 | 5/8(Tue) | Lecture 20. Language | |
| 5/20(Thu) | Lecture 21. Vision | |
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| 13 | 5/25(Tue) | Lecture 22. Robotics | |
| 5/27(Thu) | Lecture 23. Human Level AI | |
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| 14 | 6/1(Tue) | Review | |
| 6/3(Thu) | Final exam | 15 | |
| 6/8(Tue) | Project 2 Poster Presentation |