This course will introduce the overview of artificial intelligence research and recent trends, exploring different topics among many active research areas in AI: Search & Planning, 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 are mathematical backgrounds in calculus, linear algebra, and probability & statistics, and some level of programming skills.

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

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
NLP: Chatgpt & generative AI

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