1. CMPT 463 Artificial Intelligence Instructor: Tina Tian

2. About me Email: tina.tian@manhattan.edu Office: RLC 203A Office Hours: Tue, Fri 12:30 – 2:00PM or by appointment Website: home.manhattan.edu/~tina.tian

3. About the Course Tue, Wed, Fri 10:00–10:50AM Prerequisite: CMPT238 Textbook: – Artificial Intelligence: A Modern Approach (AIMA), 3 rd Edition, by Stuart Russell and Peter Norvig, Prentice Hall, 2010. ISBN: 0136042597

4. What you care the most Grading: – 1 st Midterm Exam (in class, 5 th week)12.5% – 2 nd Midterm Exam (in class, 10 th week) 12.5% – Final Exam 25% – Homework 25% – Projects25%

5. Homework Writing problems Hard copy – Except 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 acknowledge the people you worked with. And you must independently write up your own solutions. Any written sources used (apart from the text) must also be acknowledged.

7. Projects Group work – Maximum 3 students Programming – C++ or Java Submitted to Moodle (lms.manhattan.edu) No partial credit is given to projects. Due before the last day of class (May 10)

8. What to submit Make a nice cover page (Readme.doc) – Description of the problem – Algorithm chosen and why – 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 – zip the whole project folder if you are using Eclipse, NetBeans or VS

9. Advices Take notes Start the homework and projects early

10. What AI covers 1997 game between the chess champion Garry Kasparov and DEEP BLUE Asimo humanoid robot Thomas Bayes (1702 – 1761) Mars Exploration Rover (2004 - ) 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

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

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

13. What you will learn Game tree Backtracking

14. What you will learn Machine learning – Decision tree, neural networks

15. Reading AIMA Chapter 1