CIS 412 - Foundations of Artificial Intelligence

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Instructor Information

  • Instructor: Dr. Xiaoqin Zhang (Shelley)
  • Office: 302C Dion
  • Lecture hours: MWF: 12:00 - 12:50 PM
  • Office Hours: MW: 2:30-4:00 PM, Fri., 9:00 - 10:00 AM, or by appointment
  • Phone: (508) 999-8294
  • Email: x2zhang@umassd.edu
  • TA: TBA

    • Prerequisites

    CIS 360 (Algorithms and Data Structures), and familiarity with at least one programming language.

    Course Description

    This course is an introduction to to the field of Artificial Intelligence (AI). We will study the basic, fundamental approaches and techniques and their applications. This course covers the following topics:  state-space search methods, two-player games, knowledge representation, logical reasoning,  machine learning, artificial life and planning. All these topics fit together with a single unifying theme: that AI is the study of how to construct intelligent agents---computer programs that act rationally within some environment to solve problems.

    Course Objectives

    After this course you will be able to...

  • Understand motivation, mechanisms, and potential of artificial intelligence techniques. 
  • Understand how complex AI systems work.
  • Write programs to exemplify some basic techniques.
  • Read and understand the AI literature, evaluate AI-related technology claims.
  • Pursue other advanced AI courses. 
    •  

    Course Resources and Information

  • Textbook: Artificial Intelligence Illuminated By Ben Coppin
  • Course Homepage: http://www.cis.umassd.edu/~xqzhang/courses/CIS412/
  • Syllabus: http://www.cis.umassd.edu/~xqzhang/courses/CIS412/syllabus.html
  • Other links, provides number of links to other useful and interesting resource on web: http://www.cis.umassd.edu/~xqzhang/courses/CIS412/links.html

    • Course Requirements and Grading

    You are expected to take an active role in your learning in this course. This includes regular attendance, paying attention in class, reading the textbook, and completing all course requirements. You are encouraged to study with your classmates outside of class.
    There will be one midterm exam, and one final exam, one class presentation,  projects and homework assignments. The material of all exams will come from either the material covered in class, homework problems, and/or assignment readings. Complete all required work on time. In the event that an exam must be missed, or required work cannot be completed on time, due to illness or other serious and unavoidable circumstance, notify the professor as far in advance as possible by phone or e-mail. Make-up exams will not be given for any reason.

    The evaluation will be based on:
  • 20% Midterm
  • 10% Class presentation, participation
  • 20% Final Exam
  • 50% Projects and Homework Assignments

    • Assignments must be submitted through Learning Portal. Late submission within one week will be accepted with 20% penalty, no submission will be accepted after one week beyond the deadline.

    The letter grades will be assigned using the following approximate scale: (A+,A)[100-90],(A-,B+,B)[90-80], (B-, C+,C)[80-70], (C-,D+,D)[70-60], and F[60-0].

    Academic Honesty

    You are encouraged to discuss assigned problems with other people but you must individually design and write your own solutions / code for all assignments. Furthermore, you should explicitly acknowledge any sources of ideas used that are not your own; this includes other people, books, web pages, etc. "Sharing" of solutions to homework problems and lab exercises is strictly prohibited. Submitting modified versions of other people's work as your own is considered cheating. Academic dishonesty will be "rewarded" with a grade of "F".