Knowledge without appropriate procedures for its use is [mute], and procedure without suitable knowledge is blind.
-Herbert A. Simon, "Artificial Intelligence Systems that Understand", 1977
Prerequisites: knowledge of differentiation. Computer programming skills are neither required nor taught; hence, 172 is neither a substitute for nor a prerequisite to COMS 99 or COMS 100 in terms of fulfilling any programming requirements.
The course satisfies the introduction to engineering (ENGRI) requirement for students in the College of Engineering. It counts toward the mathematics and quantitative reasoning (MQR) requirement for students in the College of Arts and Sciences, and toward the Information Systems requirement for students completing a minor or concentration in Information Science.
Those who have completed the equivalent of COMS 100 must obtain the permission of the instructor to enroll. The intent is to determine whether prospective students have prior experience with computer science -- we can recommend for such students alternate courses catering to specific interests, and wish to optimize the learning experience for students in the class without such exposure -- or only with computer programming, which is essentially irrelevant to our coursework.
The following four texts, all on reserve at the Engineering Library in Carpenter Hall, are recommended for supplemental reading: they can be consulted for back-up or alternate presentations of the course material, and can be utilized for further exploration of the course topics. The classic artificial-intelligence textbook is Russell and Norvig's Artificial Intelligence: A Modern Approach (second edition, 2003). Belew's Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW (2001) is organized around the theme of identifying documents that help someone learn more about a topic of interest, and thus touches upon classic and Web information retrieval, learning, and natural language processing. Some of our coverage of traditional information-retrieval topics is drawn from Frakes and Baeza-Yates, eds., Information Retrieval: Data Structures and Algorithms (1992). A recent text concentrating on natural language processing is Jurafsky and Martin's Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2000).
Collaboration and reference to outside material: You may discuss homework problems and general solution strategies with other students, as well as consult books, web pages and other sources. As a matter of academic integrity (see below) you must list your collaborators and references consulted. Moreover, to ensure the development of individual mastery of the material, the following policies are in place. Discussions may not include specific solution details (working out the details oneself is an extremely valuable learning experience). Homeworks must be written up independently of other people, without the aid of collaborative notes. Even if you use outside references (including the books on reserve), your write-ups must be in your own words. If ever in doubt about whether a particular situation is permissible, ask beforehand.
Regrade requests: We will grade your work carefully. But should questions about grading arise that are not addressed by the course staff's solutions, then contact the relevant grader within three weeks after the homework was returned and solutions distributed. We reserve the right to make regrade decisions "off-line" (i.e., not immediately at the time requested).
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This document was generated using the LaTeX2HTML translator Version 2002-2-1 (1.70)
Copyright © 1993, 1994, 1995, 1996,
Nikos Drakos,
Computer Based Learning Unit, University of Leeds.
Copyright © 1997, 1998, 1999,
Ross Moore,
Mathematics Department, Macquarie University, Sydney.
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