Artificial Intelligence


Gary Geunbae Lee, Eng 2-211,, 279-2254


1.  Course objectives

This course teaches basic knowledge of modern AI including

heuristics search and problem solving, logics and knowledge

representation, probabilistic and bayesian reasoning, MDP and

reinforcement learning, machine learning and

neural networks, and several applications such as vision, NLP,

robotics, etc


2. Course prerequisites

no required pre-requisite


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).


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

*Most of the slides come from Berkeley AI course-CS188 which are available at




Heuristic search

Constraint satisfaction problem

Game search

Markov decision process

Reinforcement learning



Knowledge representation

Symbolic planning



Bayesian network

Bayesian inference

Decision network

Hidden markov model


ML-Naïve bayes

Neural network

Decision tree

Kernels and clustering