Artificial Intelligence

(These course materials come from POSTECH AIDS team teaching and Berkeley AI course, CS188 - http://ai.berkeley.edu)

Gary Geunbae Lee, Eng 2-211, gblee@postech.ac.kr, 279-2254

1.    Course objectives

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.

2. Course prerequisites

Prerequisites are mathematical backgrounds in calculus, linear algebra, and probability & statistics, and some level of programming skills.

3. Grading

midterm 35%

final 35%

3-4 (programming) assignments  30%

4.  texts or references

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

5. Others

instruction language: English

the 3-4 assignments will be for solving (including programming) several interesting AI problems (every 4-5 weeks)

6. Course schedule

1. AI: Introduction

2. AI: Heuristic Search

3. AI: CSP, game, MDP

4. AI: Bayesian Reasoning

5. AI: DecisionNet, HMM

6. AI: Logics, KR

7. ML: Supervised learning

8. ML: NB, NN, DT, Kernel

9. ML: Unsupervised learning

10. ML: Reinforcement learning

11. CV: Deep learning for visual recognition

12. NLP: Deep Learning NLP

13. NLP: Chatgpt & generative AI