Artificial Intelligence is the study of how machines can be designed to think and perform rational, goal-oriented actions. This course will introduce students to some of the most fundamental concepts and techniques in artificial intelligence, as well as some of the philosophical and historical aspects of artificial intelligence. The topics we will cover include search (uninformed and heuristic), genetic algorithms, constraint satisfaction, decision theory, reasoning under uncertainty, game theory, and machine learning.
This course is intended for upper division undergraduates and graduate students. No specific background in artificial intelligence is required, but a solid background in programming is necessary (equivalent to CS 3331). It is also strongly recommended that you have taken a course in probability and statistics (e.g., STATS 3320).
Textbook and Readings
The main text for the class is Artificial Intelligence: A Modern Approach (3rd edition) by Stuart Russell and Peter Norvig. IBSN-13: 978-0-13-604259-4. Additional readings may be assigned to cover specific topics.
Grades for this course will be determined by a combination of midterm exams, a final exam, homework assignments, and a final project. The approximate weightings of each component are listed below, along with more detailed descriptions.
Students taking the course for graduate credit will be held to higher standards in grading, and will have additional challenge problems on homework/exams. In addition, graduate students will be required to read and present recent research papers in artificial intelligence.
There will be one midterm exam in addition to a cumulative final exam. If you have a serious scheduling conflict you must see the instructor to make arrangements as soon as possible.
Homework Assignments and Late Policy
Homework assignments will be a combination of written exercises and programming assignments. Reading and homework assignments will be announced in class. If you miss a class it is your responsibility to find out what you missed. Late assignments will be penalized at a rate of 10% per day. No assignment will ever be accepted more than 3 days after the deadline.
Students will be required to complete a final project related to the content of the class. Example projects will be suggested, but students are encouraged to design a project specification that meets their own interests.
Conduct and Academic Dishonesty
You are expected to conduct yourself in a professional and courteous manner, as prescribed by the Handbook of Operating Procedures: Student Conduct and Discipline which can be accessed at the following URL: http://admin.utep.edu/Default.aspx?tabid=73922. Professors are required to -- and will -- report academic dishonesty and any other violation of the Standards of Conduct as described in the Handbook.
Graded work, e.g., homework and tests, is to be completed independently and should be unmistakably your own work (or, in the case of group work, your team's work), although you may discuss your project with other students in a general way. You may not represent as your own work material that is transcribed or copied from another person, book, or any other source, e.g., a web page. Academic dishonesty includes but is not limited to cheating, plagiarism and collusion.
If you have a disability and need classroom accommodations, please contact The Center for Accommodations and Support Services (CASS) at 747-5148, or by email to email@example.com, or visit their office located in UTEP Union East, Room 106. For additional information, please visit the CASS website at www.sa.utep.edu/cass.
Tentative Course Schedule