(These course materials come from POSTECH AIDS team teaching and Berkeley AI course, CS188 - http://ai.berkeley.edu)
Gary Geunbae Lee, Eng 2-211, firstname.lastname@example.org, 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.
3-4 (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
instruction language: English
the 3-4 assignments will be for solving (including programming) several interesting AI problems (every 4-5 weeks)
6. Course schedule
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