This course covers a wide range of topics related to AI -- machine learning, search algorithms, Markov decision process, reinforcement learning, games, constraint satisfaction problem, probabilistic graphical models, and logic. This course focuses on understanding the fundamental concepts and principles on each topic. Accordingly, programming assignments will be implementing the core ideas in Python without using AI libraries.

Prerequisites:
CSED233 Data structure
Mathematical backgrounds in calculus, linear algebra, and probability & statistics
Some level of programming skills.
수강 정원이 찼을 경우, POSTECH 컴공과 학부생 및 컴공지망 무은재학부생만 추가 등록 가능.

HW 20%
Midterm 40%
Final 40%

Midterm and final exams will be conducted offline during class (TuTh 2pm-3:15pm).
Make sure there are no conflicts in the exam time.
There will be no makeup exams for time conflicts.
Exams on Tuesday test how well you understand the lecture materials.
Exams on Thursday test how well you did the assignments on your own.
Doing programming assignments using ChatGPT will seriously damage your grade.

Related topics are also discussed in the following books.
Russell and Norvig. Artificial Intelligence: A Modern Approach. A comprehensive reference for all the AI topics that we will cover.
Koller and Friedman. Probabilistic Graphical Models. Covers factor graphs and Bayesian networks.
Sutton and Barto. Reinforcement Learning: An Introduction. Covers Markov decision processes and reinforcement learning. (Available free online)
Hastie, Tibshirani, and Friedman. The elements of statistical learning. Covers machine learning. (Available free online)
Tsang. Foundations of constraint satisfaction. Covers constraint satisfaction problems. (Available free online)

W1. IntroAI, ML1 (HW1 out)
W2. ML2
W3. ML3 (HW2 out)
W4. Search1
W5. Search2 (HW3 out)
W6. Markov Decision Process
W7. Reinforcement Learning (HW4 out)
W8. Midterm
W9. Games (HW5 out)
W10. Factor graph & CSP
W11. Markov networks (HW6 out)
W12. Bayes1
W13. Bayes2 (HW7 out)
W14. Logics
W15. Conclusion (HW8 out)
W16. Final exam

This semester, face-to-face classes will be held in the classroom.
It is important to take notes during class, because there is no textbook and slides are abstract.
Lecture slides will be posted to the PLMS prior to class.
It is recommended to pre-study materials (slides) before class, because this course covers many topics at a fast pace. (Use your class time mainly for Q&A.)
There will be biweekly (programming) assignments which require significant amount of time to complete, so start early, or you won’t be able to finish them on time.
Programming assignments must be written in Python 3.x, not lower (Python 2.x) version. We recommend that you use a UNIX environment (e.g., Linux or OS X) for programming assignments.

You must write up HWs and code from scratch independently. The following are considered to be honor code violations:
Looking at the writeup or code of another student.
Showing your writeup or code to another student.
Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.
Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.
Looking at solutions from previous years' homeworks - either official or written up by another student.
When debugging code together, you are only allowed to look at the input-output behavior of each other's programs (so you should write good test cases!).
It is important to remember that even if you didn't copy but just gave another student your solution, you are still violating the honor code, so please be careful.
We periodically run similarity-detection software over all submitted student programs, including programs from past semesters and any solutions found online on public websites.
Anyone violating the honor code will get F no matter what.