1. CMPT 420 / CMPG 720 Artificial Intelligence
Instructor: Tina Tian

2. About me Email: tina.tian@manhattan.edu Office: RLC 203
Office Hours: Tuesday, Friday 3:30 – 5:00 PM
or by appointment
Website: turing.manhattan.edu/~tina.tian

3. About the Course Tue, Fri 11:00–12:15 Prerequisite: CMPT 238 Textbook:
Artificial Intelligence: A Modern Approach (AIMA), 3rd Edition, by Stuart Russell and Peter Norvig, Prentice Hall, ISBN:

4. Grading Midterm Exam (in class) 20% Final Exam 30% Homework 25%
Projects %

5. Homework Writing problems Hard copy Strict deadline!
Except for graphs, trees, etc.
Strict deadline!
Due in a week after being announced
Late homework will not be accepted

6. Homework You may discuss the homework with other students.
However, you must independently write up your own solutions.

7. Projects CMPT 420 CMPG 720 Group work Maximum 3 students
Individual work

8. Projects Programming No partial credit is given to projects.
C++, Java or Python
Submitted to Moodle (lms.manhattan.edu)
One submission per group
No partial credit is given to projects.
Due by the last day of class (May 3)

9. What to submit Readme.doc Source code and executable files (.zip)
Algorithm chosen (if applicable)
Explanation of functions and data structures used
Input and output format (give an example)
Source code and executable files (.zip)
.cpp and .exe
.java and .class
.py
zip the whole project folder if you are using Eclipse, NetBeans or VS

10. Advices
Take notes
Start the homework and projects early

11. What AI covers
1997 game between the chess champion Garry Kasparov and DEEP BLUE
Asimo humanoid robot
Thomas Bayes (1702 – 1761)
Mars Exploration Rover ( )
Alan Turing (1912 – 1954)
Shakey (1966 – 1972) with its project leader Charles Rosen (1917 – 2002)
Aristotle (384 B.C. – 322 B.C.) and his planning algorithm in original Greek
Bayesian network for medical diagnosis

12. Subfields of AI Heuristic Search Adversarial Search (Games)
Natural Language Processing
Knowledge Representation
Computer Vision
Robotics
Planning
Learning
...

13. What you will learn Introduction of AI and intelligent agents
Searching algorithms
Uninformed search, informed search, local search

14. What you will learn
Game tree
Backtracking
(CSP)

15. What you will learn
Machine learning
Decision tree
Weka

16. Reading
AIMA Chapter 1