Artificial Intelligence (CS 4793), Summer 2016
University of Information Technology (UIT), Vietnam
July 12  August 4, 2016
Lecture: Tuesday, Thursday, Saturday: 8:0011:00 AM
Room: E3.3
Instructor
 Dr. David Cline, Visiting Lecturer
 david.cline@okstate.edu
 Office hours: 1:30PM  3:30PM MF
Teaching Assistants

Thanh Ngo Duc
 Head of Multimedia Processing Lab
 thanhnd@uit.edu.vn

Thang Cap Pham Dinh
 Lecturer of Computer Science Faculty
 thangcpd@uit.edu.vn
Text
 Artificial Intelligence: A Modern Approach, third edition. Stuart Russell and Peter Norvig.
Course Objectives
This class covers topics in Artificial Intelligence, including Search strategies, Reasoning with uncertain knowledge, Learning from data, Control systems, and Robotics. Some of the topics we will cover are:
 Recursive search methods
 Advarsarial games
 Planning methods
 Reinforcement learning
 Simple control theory
 Probabilistic reasoning
 Examplebased learning
Assignments
 Inclass Problems
 There will be a number of inclass problems that must be worked out and turned in during class. Because of this, you should plan to bring a pencil and paper to class. I may also call on random students to present their solutions in front of the class.
 Problem Sets
 There will be 7 problem sets throughout the term to help you cement your knowledge of the concepts and algorithms presented in class, and to help prepare you for the tests. These are indicated by (H) on the schedule.
 Code Projects
 During the semester there will be 3 coding assignments, which may be written in the language of your choice. These are indicated by (P) on the schedule.
 Tests
 This class has a midterm and a final test. The midterm will cover chapters 3,4,5, and 10. It will be administered in class. The final will be comprehensive, and will also be administered in class.
Homeworks assignments:
 Homework 1: Chapter 3  Recursion, Permutations, Combinations
 Homework 2: Chapter 3  Recursion and Searching
 Homework 3: Chapter 4  Local search and uncertainty
 Homework 4: Chapter 5  Adversarial search
 Homework 5: Chapter 10  Planning
 Homework 6: Chapter 21, 25  Reinforcement learning, controllers
 Homework 7: Chapter 18, 13  Probability and Bayes’ rule
Programs:
 Program 1: Combinations
 Program 2: Math Dice
 Program 3: Blocks World
Grade Breakdown
 Inclass Problems: 15%
 Written Homework: 20%
 Programs: 20%
 Tests: 45%
Lecture Notes aiSlides.zip
Homeworks aiHomeworks.zip
Programs aiPrograms.zip
Example code aiCode.zip
keys and tests aiKeys.zip
Tentative Schedule
Date  Reading (AIMA)  Topics  Assignments 

July 12  [Preliminaries, Recursion]  
[Permutations, Combinations]  
3.13.4  [Search problems]  
July 14  3.4  [Uninformed search]  
3.5  [A* search]  
3.6  [Local search]  (H1:ch3)  
July 16  4.14.2  [Local search]  
4.3  [Nondeterminism]  (H2:ch3)  
4.44.5  [Online search]  (P1:Combinations)  
July 19  5.15.2  [Adversarial games]  
5.35.6  [AlphaBeta pruning]  
Sliding puzzle code  (H3:ch4)  
July 21  10.1  [Classical Planning]  
10.2  [POP and Planning Graphs]  
Math dice work session  (H4:ch5)  
July 23  21.121.3  [Reinforcement learning]  
21.421.6  [Active reinforcement learning]  
25.125.3  [Robot perception]  (P2:Math dice)  
July 26  25.425.6  [Robot movement, controllers]  
Pendulum controller  
Midterm Review  (H5:ch10)  
July 28  (T) Midterm Exam  
July 30  13.113.3  [Probability and inference]  
13.413.5  [Bayes’ rule]  
18  [Decision Trees]  
Aug 2  18.118.3  [Learning from examples]  
18.418.6  [Statistical Models]  
Robot work session  (H7:ch18,13)  
Aug 4  18.718.9  [Neural Networks]  (H6:ch21,25) 
14.114.2  Bayesian Networks  
Final Review  (P3:Blocks world)  
Aug 5  (T) Final Exam 