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Please use this identifier to cite or link to this item: http://hdl.handle.net/10117/5133

Title: Artificial Intelligence 4003-455/4005-750
Authors: RIT - Department of Computer Science
Issue Date: 
Publisher: RIT - Department of Computer Science
Citation: http://www.cs.rit.edu/~jdb/ai/syllabus.html
Abstract: Syllabus Artificial Intelligence 4003-455/4005-750 Winter 2004 Catalog Description An introduction to the field of artificial intelligence, including both theory and applications. A programming language that allows effective symbolic manipulation (PROLOG) is used to demonstrate the capabilities and limitations of the material presented in class. Topics include search strategies and their implementation, logic, networks, frames and scripts, productions, symbolic manipulation and list processing, problem-solving methods, expert systems, natural language understanding, and selections from vision, robotics, planning and learning. Programming assignments are an integral part of the course. Contact Information Instructor: Jessica Bayliss Office: bldg. 70, room 3509 Email: jdb on cs.rit.edu Web Page: http://www.cs.rit.edu/~jdb Office hours: see main web page Asking questions via email seems to work best for many people. Lectures Tuesday/Thursday 10:00-11:50, 86-1150. Required Book Artificial Intelligence: A modern approach by Russel and Norvig, second edition, Prentice Hall, 2003. Note: this is the most commonly used broad text on AI, it is a common reference, and should be available used in many places. Other Materials Course Web Page: http://www.cs.rit.edu/~jdb/ai I will distribute copies of other materials required for class. Information about reading and homework assignments, exams, etc. will be linked from the course web page. You are responsible for reading the course web page for information. Prerequisites Programming Language Concepts (4003-455 or 4003-709) These prerequisites will be enforced. Homework and Project Assignments Reading assignments will be given in class and may be expected to be completed by the next class time. Each written/coded homework assignment will be collected and graded. Written/coded homework assignments are posted at least 5 days before they are due and are due when stated in the assignment. The actual assignments will be available off of the course web page. There will be a grace period consisting of 24 hours where a student may submit the assignment late without penalty. Assignments will not be accepted after this period. If stated in the homework/project, you may work on the assignment in groups of 1 or 2. If you choose to work as a group of 2, both of you should contribute significantly to the solution for every problem. Groups of 2 may be asked to do additional work for an assignment. You should submit only one copy of the homework with both of your names on it. You are not allowed to discuss the homework with anyone except your partner and me. You should submit only work that is completely your own and you should be able to explain all of your homework to me. Quizzes Starting with week 2, a quiz will be given every other Wednesday. While each quiz will be closed book and notes, you may bring one sheet of letter-sized paper with your own hand-written notes. Quizzes may not be made up when missed, but I will drop the lowest quiz grade. Final Exam A cumulative final exam will be given. The exam is closed book and notes but you may bring one sheet of letter-sized paper with your own hand-written notes. Exams cannot be made up except for real emergencies in which case proper documentation (like a doctor's note) will be required. If at all possible, you should contact me prior to the exam. Oversleeping, cars that don't start etc. do not constitute a valid excuse. Graduate Students and Extra Credit for Undergraduates If you are taking this class as a graduate student, you will be expected to read extra technical papers on a variety of AI topics. Five times in the quarter you will get together as a group and discuss/present these papers outside of the normal class hours. Undergraduate students may elect to participate in this activity for extra credit counting towards their homework points. More depth on this requirement will be posted on the home page for the course. Undergraduate Evaluation 45% Homework 10% Final Project 5% Participation and attendance 25% Quizzes 15% Final Exam Graduate Evaluation 35% Homeworks 10% Final Project 10% Papers 5% Participation and attendance 25% Quizzes 15% Final Exam All grades Numerical grades will be converted to letter grades according to the following scale: > 90%: A; 80%-89.9%: B; 70%-79.9%: C; 60%-69.9%: D; < 60%: F. Your final grade will never be more than one letter grade higher than your weighted average exam grade. In addition, if your weighted average exam grade is below 60%, you fail the course with an F. Disputing Your Grade If you feel that an error was made in grading your homework or exam, you have one week from the moment the graded work was handed back to dispute your grade. Academic Dishonesty The DCS Policy on Academic Dishonesty will be enforced. You should only submit work that is completely your own. Failure to do so counts as academic dishonesty and so does being the source of such work. Submitting work that is in large part not completely your own work is a flagrant violation of basic ethical behavior and will be punished in accordance with the DCS Policy.
URI: http://www.citidel.org/handle/10117/5133
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