4190.408 인공지능 (Spring 2026)
- Instructor: Prof. Byoung-Tak Zhang
- Department: Computer Science and Engineering
- Classroom: 302-106
- Time: Mon & Wed 17:00-18:15
- Credits: 3-3-0
Course Objectives
This course introduces the basic ideas and techniques underlying the design of intelligent computer systems, with emphasis on probabilistic and decision-theoretic modeling in artificial intelligence.
Students study artificial intelligence theories, techniques, and systems for machines that behave and think like people. Topics include intelligent agents, navigation, reasoning, planning, knowledge representation, decision making, learning, vision, language, empirical search, Bayesian networks, Hidden Markov models, deep learning, and reinforcement learning.
This class is held in a flipped-learning format. Students watch the corresponding pre-uploaded lecture videos before attending class, and class sessions are used for questions, discussion, and debate.
Textbooks
- Artificial Intelligence: A Modern Approach - S. Russell & P. Norvig - Pearson - 2021
- 장교수의 딥러닝 - 장병탁 - 홍릉과학출판사 - 2017
Grading Policy
| 구분 | 비율 |
|---|---|
| 출석 | 10% |
| 과제 | 20% |
| 중간고사 | 25% |
| 기말고사 | 25% |
| 기타 | 20% |
Course Schedule
| Week | Date | Topic |
|---|---|---|
| 1 | 3/5, 3/10 | Lecture 1. Introduction; Lecture 2. Intelligent Agents |
| 2 | 3/12, 3/17 | Lecture 3. Problem-Solving Agents; Lecture 4. Search in Complex Environments |
| 3 | 3/19, 3/24 | Tutorial 1. Scientific computing with Python; Tutorial 2. Bayesian Networks |
| 4 | 3/26, 3/31 | Lecture 4. Search in Complex Environments-2; Lecture 5. Adversarial Search |
| 5 | 4/2, 4/7 | Lecture 6. Logical Inference; Lecture 7. Rule-based Systems; Tutorial 3. Deep Neural Networks-1 |
| 6 | 4/9, 4/14 | Lecture 8. Knowledge Representation; Lecture 9. Automated Planning |
| 7 | 4/16, 4/21 | Lecture 10. Uncertainty, Probability, Information; Midterm Exam |
| 8 | 4/23, 4/28 | Lecture 11. Probabilistic Reasoning; Lecture 12. Temporal Reasoning |
| 9 | 4/30, 5/5 | Lecture 13. Utility-Based Agents; Project 1 Announcement; Buddha’s Birthday |
| 10 | 5/7, 5/12 | Lecture 14. Probabilistic Programming |
| 11 | 5/14, 5/19 | Lecture 15. Machine Learning; Holiday |
| 12 | 5/21, 5/26 | Lecture 16. Deep Learning; Project 2 Announcement; Lecture 17. Reinforcement Learning |
| 13 | 5/28, 6/2 | Tutorial 4. Deep Neural Networks; Lecture 18. Language |
| 14 | 6/4, 6/9 | Lecture 19. Robotics; Lecture 20. Vision |
| 15 | 6/11, 6/16 | Lecture 21. Human-Level AI; Final Exam |