CS347 - Introduction to Artificial Intelligence

Spring Semester 2005 Syllabus

Description

This course is meant as a first introduction to the field of Artificial Intelligence (AI). Instead of providing a (necessarily) superficial survey of the many topics covered by AI, this course will instead primarily emphasize a few important AI topics, including advanced non-informed search, informed (heuristic) search, and adversarial (game) search.

Course Objectives

To provide students sufficient background and understanding of AI to identify applicable problems and implement suitable AI techniques to solve those problems, and also to give students a taste of what AI is about so that they can decide whether they want to take advanced AI courses such as CS378 - Introduction to Neural Networks, CS404 - Data Mining & Knowledge Discovery, and CS448 - Introduction to Evolutionary Computation.

Prerequisites

  • For undergraduate students: CS253 - Data Structures II
  • For graduate students: C++ programming proficiency

    • Attendance

    Attendance will be taken and is strongly encouraged. Students who fall below an A grade average for this course and whose attendance is unsatisfactory are required to show cause to the instructor as to why they should not be dropped from the class. Unsatisfactory attendance is defined as having more than three absences before the last day to drop classes without a WD showing in one's transcript or more than five absences total. The instructor has the right to drop any student who fails to show cause to the instructor's satisfaction.

    Students not present at the calling of the roll at the beginning of the class hour will be considered absent, unless they arrive late and explicitely request the instructor at the end of the class hour to mark them as present.

    If you know you will be missing class or will be late for class, let the instructor know in advance!

    Minimal Grade Average Requirement

    Students whose class grade average drops below a C are required to show cause to the instructor as to why they should not be dropped from the class. The instructor has the right to drop any students who fail to do so or whose cause the instructor deems insufficient.

    Makeup Exams, Quizzes, and Homeworks

    There will be NO makeup exams, makeup quizzes, nor makeup homeworks. However, your lowest exam and lowest quiz grades will be dropped, effectively allowing you to miss one exam and one quiz without penalizing your grade. It is in your best interest to avoid this at all costs; it is meant for special situations like illness, death in family, etc.

    Cumulative Exam Grade Calculation

    There will be three exams during the semester and one comprehensive final exam during finals week. The cumulative exam grade will be determined as follows:
    Max((Exam1+Exam2+Exam3)/3,(Exam1+Exam2+Exam3+2*Final-Min(Exam1,Exam2,Exam3))/4)
    This means that students happy with their grade at the end of the semester can skip taking the comprehensive final exam, but it also means that taking the final exam can only improve your grade, never lower it!

    Homework

    Homework is always due at the start of class and must be submitted in person on paper and be typed (except for diagrams which may be hand-drawn)! Off-campus students need to make prior arrangement with the instructor for this type of submission (e.g., via E-mail or fax). Note: in this course, programming assignments are distinct from homework and will always specify submission rules.

    Cheating & Plagiarism

    Neither will be tolerated. The first time either occurs will result in a zero grade, the second time the student will be dropped from the class; in both instances the student's advisor will be notified and further action may be taken as well. Also note that those who allow others to copy their work are just as guilty of plagiarism and will suffer the same consequences.

    Instructor
    Name Dr. Daniel Tauritz
    Office 324 Computer Science Building
    Office hours See instructor's website or office door for current office hours
    E-mail tauritzd@umr.edu
    WWW http://web.umr.edu/~tauritzd
    Phone (573) 341-7218
    Fax (573) 341-4501
    Secretary phone (573) 341-4491

    Course information
    Required textbook Stuart Russell and Peter Norvig Artificial Intelligence: A Modern Approach, 2nd edition, Prentice Hall, 2003, ISBN 0-13-790395-2.
    Course website http://web.umr.edu/~tauritzd/courses/cs347/sp2005
    Meeting times MW 2:00 pm - 3:15 pm
    Course Schedule Dynamic schedule

    Grading
    Exams (3 during semester + 1 comprehensive) 30% of total grade
    Quizzes 15% of total grade
    Homework 10% of total grade
    Puzzle project 15% of total grade
    Game project 30% of total grade
    Final grade for undergraduate students 90-100: A, 80-89: B, 70-79: C, 60-69: D, <60: F
    Final grade for graduate students 90-100: A, 80-89: B, 70-79: C, <70: F

    Resolving Issues

    You should always first try and resolve issues concerning the class with the instructor. If you are unable to resolve them to your satisfaction, please consult your advisor; if you are still unable to resolve them, you are encouraged to talk to the department chair.