MEDGAR EVERS COLLEGE of CUNY
Department of Physical, Environmental and Computer Sciences
Department Office, Carroll 417 - Phone 718-270-6453
ARTIFICIAL INTELLIGENCE
COURSE SYLLABUS – CS 280
Required Text: Artificial Intelligence: Structures and Strategies for Complex Problem Solving
(6th Ed., © 2009), by George F. Luger (Addison-Wesley, Publisher). ISBN-10: 0-321-54589-3
(Available in MEC Bookstore).
Course Description: This course examines the ideas and techniques underlying the design of intelligent computer systems. Topics include knowledge representation, heuristic versus algorithmic search methods, problem solving, game playing, logical inference, planning, reasoning under uncertainty, expert systems, learning, perception, natural language understanding, and intelligent agents. A functional programming language appropriate to Artificial Intelligence will be introduced.
Prerequisites: CS 244 (Data Structures & Algorithms), MTH 201 (Calculus I)
Course Requirements: Assigned readings, problems and programs should be completed before class. The Steel Bank Common Lisp (SBCL), an open source (free software) compiler and runtime system for ANSI Common Lisp, will be used as a tool for demonstrating the AI approach to problem solving. Several small computer projects (using Lisp) will be assigned to reinforce the modeling concepts presented in class. Unless you own or have access to equivalent hardware and software, plan on spending a lot of time on campus.
Project/Presentation: A Lisp-based project covering an application area of AI is also required and must be presented to the class. Use the following guidelines when preparing your presentation: (i) select a topic from one of the AI Application Areas described in your textbook, (ii) show how Lisp programming is used in this area (i.e., fully explain syntax and semantics), and (iii) produce an artifact that implements an AI algorithm in your selected AI Application Area. Specifically, you must use the Lisp programming language to produce your AI artifact. Remember to observe the required presentation format, described in the Computer Science Booklet, when preparing your 2-page presentation proposal. Failure to follow this requirement may result in the return of the proposal without review.
The oral presentation should be no less than fifteen minutes in length. The instructor prior to the midterm examination must approve a detailed outline, along with a project design. Absolutely no project/presentation will be accepted after the last day of class!
Grading Procedure: The final grade will be determined objectively by using a weighted average along with the following weighted areas: computer projects, presentation, chapter examinations, and final examination. Check with the college catalog for information regarding the official grading policy.
Honor Code: Students are required to sign and adhere to the departmental honor pledge. Check with the department for a copy of the pledge.
Final Examination Date: TBA
CUNY Proficiency Examination (CPE)
The CPE is a graduation requirement. All students between 45-60 credits are required to sit for and pass the CPE. You have only three chances to pass this examination. Each missed scheduled examination after the 45 credit mark counts as a failure. For more information about this requirement, contact the Medgar Evers College CPE Liaison.
MEDGAR EVERS COLLEGE of CUNY
Department of Physical, Environmental and Computer Sciences
Department Office, Carroll 417 - Phone 718-270-6453
ARTIFICIAL INTELLIGENCE - CS 280
| WEEK # | TOPICS | CHAPTER |
| PART I: AI: ITS ROOTS AND SCOPE | ||
| 1 | AI: History and Applications | 1.0 - 1.4 |
| PART II: AI: REPRESENTATION AND SEARCH | ||
| 1 | Knowledge Representation, Problem Solving as Search - The Propositional Calculus | 2.0 - 2.1 |
| 2 | The Predicate Calculus | 2.2 - 2.5 |
| PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING | ||
| 3-4 | Weak Method Problem Solving: - Automatic Reasoning (Resolution Proof Procedures) | 14.0 - 14.3 |
| PART V (ii): LANGUAGES AND PROGRAMMING TECHNIQUES FOR AI | ||
| 5 | Languages, Understanding, and Levels of Abstraction: - Introduction to SBCL Lisp | SBCL-Documentation |
PART II (continued): AI: REPRESENTATION AND SEARCH |
||
| 6 | Structures and Strategies for State Space Search | 3.0 - 3.4 |
| 7 | Heuristic Search | 4.0 - 4.6 |
| 8-9 | Stochastic Methods: - Elements of Probability Theory, and Bayes’ Theorem | 5.0 - 5.5 |
| 10-11 | Building Control Algorithms for State Space Search | 6.0 - 6.4 |
| PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE | ||
| 12-13 | Knowledge Representations (Selected Topics): | 7.0 - 7.5 |
| 14 | Strong Method Problem Solving: Rule-Based Expert Systems - Expert System Shells | 8.0 – 8.2 |
| 15 | Final Examination | Comprehensive |
