2025-Fall Artificial Intelligence (CSED342-01) The course syllabus

1.Course Information

Course No. CSED342 Section 01 Credit 3.00
Category Major required Course Type prerequisites
Postechian Core Competence
Hours MON, WED / 14:00 ~ 15:15 / Science BldgⅡ[102]Lecture Room Grading Scale G

2. Instructor Information

Lee Gary Geunbae Name Lee Gary Geunbae Department Grad. School of AI
Email address gblee@postech.ac.kr Homepage http://nlp.postech.ac.kr/home/
Office Office Phone 279-2254
Office Hours MW 15:15-15:45 or make an appointment with email

3. 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.

4. Prerequisites & require

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

5. Grading

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

6. Course Materials

Title Author Publisher Publication
Year/Edition
ISBN
There is no required textbook for this class, and you should be able to learn everything from lecture notes and public websites. 0000

7. Course 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

8. Course Plan

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

9. Course Operation

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

10. How to Teach & Remark

11. Supports for Students with a Disability

- Taking Course: interpreting services (for hearing impairment), Mobility and preferential seating assistances (for developmental disability), Note taking(for all kinds of disabilities) and etc.

- Taking Exam: Extended exam period (for all kinds of disabilities, if needed), Magnified exam papers (for sight disability), and etc.

- Please contact Center for Students with Disabilities (279-2434) for additional assistance