- Dept. of Computer Science and Engineering, Seoul National University
- Instructor: Prof. Byoung-Tak Zhang
- Main TA: Chang-Hoon Jeong
- E-mail: chjeong@bi.snu.ac.kr
- Sub TA: YeonJi Song, YunHyeok Kwak, YouWon Jang, JunSeok Park, JungHyun Kim
- Objectives:
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This course will introduce basic ideas and techniques underlying the design of intelligence computer systems. A specific emphasis will be on the basics of statistical and decision-theoretic modelling paradigm in the field of artificial intelligence.
- Study a wide range of artificial intelligence theory, techniques and systems about machines that behave and think like people.
- Understand concepts, models, and algorithms to develop intelligent agents such as navigation, reasoning, planning, knowledge representation, decision making, learning, visual and language.
- Learn and practice key technologies of artificial intelligence such as empirical search, Bayesian network, Hidden Markov network, and reinforcement learning.
Upon successful completion of this course, students will be able to: - Syllabus:
- 2022 syllabus
- 2022 syllabus
- Textbook:
- Reference:
- 장병탁, 장교수의 딥러닝, 홍릉과학출판사, 2017
- Grading Policy:
- Attendance/Participation (10%)
- Assignment (20%)
- Project (20%)
- Midterm Exam (25%)
- Final Exam (25%)
Course Schedule
| Week | Date | Mode | Topic | Assignment | Slide | 1 | 3/3(Thu) | Online | Lecture 1. Introduction |
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| 2 | 3/8(Tue) | Hybrid | Lecture 2. Intelligent Agents |
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| 3/10(Thu) | Hybrid | Lecture 3. Problem-Solving Agents |
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| 3 | 3/15(Tue) | Hybrid | Lecture 4. Search in Complex Environments |
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| 3/17(Thu) | Online | Lecture 5. Knowledge-Based Agents |
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| 4 | 3/22(Tue) | Online | Lecture 5. Knowledge-Based Agents 2 | |||
| 3/24(Thu) | Video | Lecture 6. Logical Inference |
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| 5 | 3/29(Tue) | Video | Lecture 7. Rule-based Systems |
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| 3/31(Thu) | Video | Lecture 8. Knowledge Representation |
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| 6 | 4/5(Tue) | Video | Lecture 9. Automated Planning |
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| 4/7(Thu) | Video | Lecture 10. Uncertainty, Probability, Information |
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| 7 | 4/12(Tue) | Video | Lecture 11. Probabilistic Reasoning |
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| 4/14(Thu) | Online | Tutorial 1. Scientific computing with Python |
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| 8 | 4/19(Tue) | Online | Tutorial 2. Bayesian Networks |
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| 4/21(Thu) | Offline | Midterm Exam (Assignment 1~5) | ||||
| 9 | 4/26(Tue) | None | Project 1 Presentation |
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| 4/28(Thu) | Hybrid | Lecture 12. Temporal Reasoning |
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| 10 | 5/3(Tue) | Hybrid | Lecture 13. Utility-Based Agents |
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| 5/5(Thu) | - | Holiday | ||||
| 11 | 5/10(Tue) | Video | Lecture 14. Probabilistic Programming |
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| 5/12(Thu) | Video | Lecture 15. Machine Learning
Project 2 Announcement |
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| 12 | 5/17(Tue) | Video | Lecture 16. Deep Learning |
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| 5/19(Thu) | Online | Tutorial 3. Deep Neural Networks |
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| 13 | 5/24(Tue) | Online | Tutorial 4. Advanced Deep Learning |
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| 5/26(Thu) | Hybrid | Lecture 17. Reinforcement Learning |
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| 14 | 5/31(Tue) | Hybrid | Lecture 18. Language |
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| 6/2(Thu) | Hybrid | Lecture 19. Vision |
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| 15 | 6/7(Tue) | Video | Lecture 20. Robotics |
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| 6/9(Thu) | Hybrid | Lecture 21. Human-Level AI |
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| 16 | 6/14(Tue) | Offline | Final exam (Assignment 6~10) | |||
| 6/16(Thu) | None | Project 2 Presentation |
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