CSC 425
Artificial Intelligence
Summer 2011
Syllabus


Professor: Richard Fox
Phone: (859) 572-5334
Email: foxr@nku.edu
Office: GH 444

Semester: Summer 2011
Class Meeting Time: M or W 6:15-9:00 pm
Class Meeting Place: BEP 296
Office Hours: By appointment


Textbook and Materials:

Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edition, George F. Luger, Addison-Wesley, ISBN 0-321-54589-3, ISBN-13 978-0-321-54589-3.


Prerequisites and Credits:

Undergraduates: C- or better in CSC 364. Graduate Students: Regular admissions to the MSCS program. 3 Credit hours. This is an elective class for students in the Computer Science undergraduate or graduate program. NOTE: only graduate students may sign up for CSC 525.


Course Topics:

An introduction to the field of Artificial Intelligence (AI). We start with an examination of intelligence and what AI tries to accomplish. We examine in detail the role of knowledge in problem solving and how to codify it through knowledge representations. We examine a wide variety of AI algorithms including search techniques, rule-based chaining, algorithms for handling uncertainty, symbolic learning, neural network training, genetic algorithms and stochastic and probabilistic approaches. We will examine different AI applications and solutions including diagnosis, planning and design, automated reasoning, speech recognition, common sense reasoning, and natural language processing. This course does not assume that the student already knows any AI programming language such as LISP or Prolog.


Student Learning Outcomes:

By the end of this course, students will learn to:

  • understand the role of knowledge representations in AI and be able to transfer knowledge into one of several forms of representation
  • understand the role of search in AI and be able to implement a variety of search algorithms
  • understand how expert systems work and the various strategies used
  • understand the basic problem solving strategies for diagnosis and planning
  • understand learning algorithms, how they work, their uses and their drawbacks
  • understand the types of neural networks used in AI and their strengths and weaknesses
  • understand genetic algorithms and their uses
  • understand the various forms of uncertainty handling
  • be able to implement a rule-based system in Clips, Prolog, or a related language, a search based algorithm, and a learning or uncertainty handling algorithm


    Structure of the Class

    This class will meet one day per week during the 8 week summer session. The other meeting day will be held on-line. It is expected that all students will participate. On-line sessions can take place any time after the day we meet in class up until the next class period. In-class sessions will primarily involve lectures with occasional class participation. On-line sessions will include notes to read, activities through which you can gain practical experience with concepts and algorithms, and discussion board postings to discuss your own ideas regarding the concepts and results of the activities as well as any research you may have done to support your own ideas. There will be weekly homeworks and 3 programming projects separate from the on-line sessions.


    Absentee Policy:

    For financial aid purposes, the University is now requiring that attendance be monitored during the first three weeks of class. Students may be administratively dropped during that time for non-attendance. Students who miss class and wish to remain registered in class should notify their instructor. Aside from this initial period, the instructor will not take attendance. It is up to the student to attend class regularly and to determine what materials were missed in the event of an absence. If an assignment is due on a date that the student is absent, it is the student’s responsibility to make sure that the instructor receives the assignment prior to the beginning of class time (whether by Email or having someone reliable drop off the assignment to the instructor in his office, mailbox or classroom).


    Course Materials:

    All course materials will be made available through the instructor’s course web site at http://www.nku.edu/~foxr/CSC425/csc425.html with discussion board postings taking place on Blackboard (http://learnonline.nku.edu/). Material posted to the website will include useful links, powerpoint and word doc notes, assignments (in word doc form) and the course schedule. If you cannot access some material, contact the instructor as soon as you discover a problem.


    Student Assessment:

    Grading Breakdown:

    ItemUndergraduatesGraduate Students
    Homeworks40%35%
    Programs25%25%
    Discussion20%15%
    Research papern/a10%
    Final15%15%

    The grading scale (subject to curve if necessary) is:
    A: 93-100 A-: 90-92 B+: 87-89 B: 83-86 B-: 80-82 C+: 77-79 C: 73-76 C-: 70-72 D+: 66-69 D: 60-65 F: 0-59
    NOTE: graduate student grades below a 70 will receive an F.
    The last date to drop with a grade of W is Monday, June 27


    Homework and Programming Information and Policies:

    Due dates will be provided for each assignment when it is posted. All assignments are due at the beginning of the class period of the due date. Late assignments will be accepted with a penalty of 20% per day late. Once an answer key has been posted, late assignments will no longer be accepted. All homework assignments must be word processed (although figures and other awkward components of the answers may be hand drawn). Assignments may be submitted via Email as long as that Email is received at least 1 hour prior to the class period when it is due. If the Email arrives late, is unreadable, or is in a format that the instructor cannot access, the assignment will be considered late. Programming assignments should include the source code and sample output and a report detailing what your system does and what you learned in creating it. Undergraduate students may work in groups of up to 3 on programming assignments. Graduate students must work alone on programming assignments. All homework assignments individual assignments meaning that students should work alone at all times except for clarification.


    Schedule of topics and readings:

    See http://www.nku.edu/~foxr/CSC425/schedule.html


    Student Retention and Disabilities Services:

    Students experiencing roadblocks to academic success may seek assistance from Retention Coordinators in Student Retention and Assessment (SRA). Financial, personal, and social concerns sometimes interfere with the dedicated focus needed to be successful in college. SRA helps students connect to academic and support services, create individual learning plans, and advance successfully towards graduation. More information is available at www.nku.edu/~retention. Call 859 572 6497 for an appointment or stop by University Center 352.

    Students with disabilities who require accommodations (academic adjustments, auxiliary aids or services) for this course must register with the Disability Services Office. Please contact the Disability Service Office immediately in the University Center, suite 320 or call 859-572-6373 for more information. Verification of your disability is required in the DSO for you to receive reasonable academic accommodation. Visit our website at http://www.nku.edu/~disability


    The instructor reserves the right to alter the syllabus if circumstances dictate.

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