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De La Salle University • College of Computer Studies Course Syllabus INTROAI / Introduction to Artificial Intelligence Prerequisites Type of Course Faculty : : : Term : DISCTRU Basic course Dr. Raymund Sison [email protected] http://mysite.dlsu.edu.ph/faculty/sisonr/#Teaching Term 1 AY 2010-11 Course Description: Artificial Intelligence (AI) is the study of how machines can exhibit at least one of the following aspects associated with intelligent behavior: (a) problem solving, or the performance of non-trivial, goal-directed cognitive tasks even in the face of inadequate (e.g., incomplete, incorrect, inconsistent, or vague) data; (b) reasoning, or the drawing of logical inferences and conclusions from possibly inadequate evidence; and (c) learning, or the improvement of performance through experience. This 3.0-unit course introduces the CS-ST major to fundamental concepts, principles, and techniques in search-based problem solving, reasoning, and machine learning, and in the representation of the knowledge needed to perform these tasks. Objectives / Values: At the end of the course, students should be able to: 1. Describe the basic approaches to search and knowledge representation; 2. Analyze algorithms and higher-level control regimes for searching problem spaces; 3. Model knowledge from different domains using various knowledge representation schemes; 4. Analyze and compare the two most important approaches to machine learning; and 5. Build small to medium-sized intelligent applications using the AI representations and techniques that they have learned. Throughout the course, students are expected to practice the Lasallian values, particularly the values of zeal and love for one’s community (in this case, one’s class and group), by: 6. Preparing well for, and participating actively, in class; 7. Listening and speaking to others, whether the professor or a classmate, with respect; and 8. Cooperating and collaborating well with one’s group mates. Topics and Calendar: Week Topics 1 Course Requirements AI Definition, History, and Overview 2 Search: Basic Algorithms 3 Search: Heuristics 4 Search: Game Trees 5 Representation: Propositional Logic 6 Representation: Predicate Logic 7 Reasoning: PROLOG (PROgramming in LOGic) 8 Reasoning: Rule-based Expert Systems Readings AIMA2E*Chapter 1 AIMA2E Chapter 3 AIMA2E Chapter 4 AIMA2E Chapter 6 AIMA2E Chapter 7 AIMA2E Chapter 8 (Blackburn et al, 2006) Chapter 8 of (Luger, 2005) Deadlines Student info sheet & ID pic Assignment 1** Exam 1 1 of 3 Week 9 10 11 12 13 Topics Reasoning: Uncertainty*** Machine Learning: Decision Trees Machine Learning: Neural Networks Philosophy of AI Integration Readings Section 9.2 of (Luger, 2005) AIMA2E Chapter 18 AIMA2E Chapter 20 AIMA2E Chapter 26 Deadlines Assignment 2** Exam 2 Assignment 3** Assignment 4** (can be earlier) Notes: * AIMA2E stands for “Artificial Intelligence: A Modern Approach, 2nd Ed” by Russell and Norvig (see Text below). ** A soft copy of this document will be e-mailed to the professor before 9:00 AM of the Monday of the specified week. Late submissions will incur a 1-point deduction per day late (excluding Sundays). The General Report Formatting Guidelines are on a separate document, downloadable from the professor’s website. *** This topic is a reading assignment, for which there will be no lecture. Teaching Methods / Strategies: Lectures Discussions Recitations Written reports Assignments Group projects Requirements: This course has 3 kinds of requirements: class participation, exams, and assignments. Class participation (recitation, snap quizzes, special oral reports) Students may accumulate up to 20 points through class participation. You can view your class participation scores at http://mysite.dlsu.edu.ph/faculty/sisonr/#Teaching. Students who recite must e-mail the professor within the day the number of points and questions answered. The subject of the e-mail must be: INTROAI - Recitation - <section> - <points> - <last name if not on e-mail address> <first name if not on -mail address>. For example: INTROAI - Recitation - S17 - 1 - Sison Raymund. In the body of the e-mail, specify your name (last name first), the question asked by the professor, and the answer that you gave. The slides of any special oral reports must use the masters of the slides of this course, and must be emailed, with corrections, to the professor within 24 hours of the oral presentation. The subject of the email must be: INTROAI - Special Report - <section> - <last_name> <first_name>. For example: INTROAI - Special Report - S17 - Sison Raymund. Exams There will be two exams. The first exam will cover search and representation; the second will cover reasoning and machine learning. The weeks of these exams are specified in the course calendar above. The exact dates and times will be determined by the professor and students on the first week of classes. Assignments The deadlines of the course’s four assignments are specified in the course calendar above. The specs of these assignments will be provided on the second week of classes. Assessment / Evaluation: 2 of 3 To pass this course, one must accumulate at least 60 points through the course requirements discussed above. The maximum points that a student can obtain through each requirement are shown below. Assessment Task Class Participation Exam 1 Exam 2 Assignment 1 (AI Cap’n) Assignment 2 (Knowledge Base) Assignment 3 (Machine Learning) Assignment 4 (AI Applications) TOTAL POINTS Maximum points 20 15 15 10 15 15 10 100 Text / Materials: Russell, S. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach, 2nd Ed. New Jersey: PrenticeHall. This is the AI textbook used by top Computer Science departments in the world. A low price edition (with a green soft cover) is available in Philippine bookstores. There are also a few copies at the DLSU library; two of these have been placed in the Circulation-Reserve section. References: Blackburn, P., Bos, J. & Striegnitz, K. (2006). Learn Prolog Now! UK: College Publications. There is a free online version of this book at: http://www.learnprolognow.org/. Jones, M. T. (2008). Artificial Intelligence: A Systems Approach. Hingham, Massachusetts: Infinity Science. This is the newest book to be titled “Artificial Intelligence”. It is easier to read but is less comprehensive than both (Russell & Norvig, 2003) and (Luger, 2005). It also contains C implementations of the basic AI algorithms. A copy is available at the DLSU library, and has been placed in the Circulation-Reserve section. Luger, G. (2005). Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Ed. Harlow, Essex: Addison-Wesley. This is easier to read than (Russell & Norvig, 2003). The fourth edition of this book is available at the DLSU library and has been placed in the Circulation-Reserve section. Also available in Philippine bookstores. General Report Formatting Guidelines The General Report Formatting Guidelines are on a separate document, downloadable from the professor’s website. When submitting any written documents in this course, these guidelines must be adhered to. Nonadherence to a guideline will merit deductions. 3 of 3