CMPT 420 / CMPG 720 Artificial Intelligence Instructor: Tina Tian

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

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, 2010. ISBN: 0136042597

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

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

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

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

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)

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

Advices Take notes Start the homework and projects early

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

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

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

What you will learn Game tree Backtracking (CSP)

What you will learn Machine learning Decision tree Weka

Reading AIMA Chapter 1