This course will introduce the overview of artificial intelligence research and recent trends, exploring different topics among many active research areas in AI: Search, Probabilistic reasoning and logic (AI), Machine Learning (ML), Computer Vision (CV) and Natural Language Processing (NLP). In this course, we will present the relevant core subjects and discuss various interdisciplinary topics to form an integrated viewpoint on AI research.

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

midterm 35%
final 35%
3-4 (programming) assignments 30%

Related topics are also discussed in the following books, but require much more time to read and understand the same concepts.
Artificial Intelligence: A Modern Approach, 4th ed. by Stuart Russell (UC Berkeley) and Peter Norvig (Google).
Computer Vision: A Modern Approach (2nd Edition), David A. Forsyth and Jean Ponce, Pearson, 2011, 013608592X
Speech and Language Processing (3rd ed. draft), Dan Jurafsky and James H. Martin.
Machine Learning: A Probabilistic Perspective, Kevin P. Murphy

These course materials come from POSTECH AIDS team teaching and Berkeley AI course, CS188 - http://ai.berkeley.edu
AI: Introduction
AI: Heuristic Search
AI: CSP, game, MDP
AI: Bayesian Reasoning
AI: DecisionNet, HMM
AI: Logics, KR
ML: Supervised learning
ML: NB, NN, DT, Kernel
ML: Unsupervised learning
ML: Reinforcement learning
CV: Deep learning for visual recognition
NLP: Deep Learning NLP

- the course homepage: https://nlp.postech.ac.kr/courses/artificial-intelligence
- instruction language: English
- the 3-4 assignments will be for solving (including programming) several interesting AI problems (every 4-5 weeks)
- 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.