ENGRI/CS/INFO/COGST 172, SPRING 2007:
COMPUTATION, INFORMATION, AND INTELLIGENCE

"One of the strengths of this course is that computer science is introduced in a different and interesting way. The lectures take a look at the ideas underlying computer science, without actually trying to implement these ideas in a program. This appeals to a larger audience.... Overall I think the course has [achieved] what it was supposed to; it gave us an understanding of [Artificial Intelligence] and unfortunately with that comes illustrating how difficult AI can often be.... But [the staff go] to extraordinary lengths to help students learn *how* to learn."
– excerpts from the Fall 2005 anonymous course-evaluation comments

"Never, ever, underestimate a Cornell student. Remember that."
– "Memorare", Anthony R. Ingraffea, Cornell prof. and Weiss Fellowship recipient

"All satisfied with their seats? O.K. No talking, no smoking, no knitting, no newspaper reading, no sleeping, and for God's sake take notes."
– Lectures on Literature, Vladimir Nabokov, ex-Cornell prof. and Nobel prize non-recipient

Lecture time/place:	MWF 10:10-11:00am, Thurston 203
Instructor:	Prof. Lillian Lee 4152 Upson, , x5-8119, www.cs.cornell.edu/home/llee Office hours: Tuesdays 3-4 and Fridays 11:15-12 except during Spring break or otherwise announced.
TAs:	Jared Cantwell, Rafael Frongillo, Nick Gallo, Selina Lok, Anton Morozov, Ben Pu, Sean Seguin, Mark Yatskar, and Adam Yeh — a truly excellent bunch of people looking forward to helping you succeed in this course! Here is a link to our contact info and office hours.
Exam dates:	in-class prelims Friday March 2 and Friday April  16  6, non-optional final Friday May 18th, 2-4:30pm, Philips 219.
Links:	Course description and policies Course-staff contact info and course calendar. Shows office hours, homework due dates, and exam dates. Learning Strategies Center and the LSC's study skills resources

Syllabus overview: ENGRI/COMS/INFO/COGST 172 ("172") is an introduction to computer science focusing on current methods and examples from the field of artificial intelligence. It is not a programming course; rather, "pencil and paper" problem sets are assigned, for the focus of the class is on algorithmic concepts and mathematical models. Subjects range from classic topics to current research, as indicated by the following (specifics may be subject to change):

1. Problem solving; or, Kasparov's defeat, Deep Blue's feat, and the myth of brute-force search: problem-space design and search; game playing, minimax, and pruning
2. Learning; or, the van that learned to drive itself: neural nets, linear separators, and the perceptron convergence theorem
3. Language; or, a computer that understands you like your mother: Boolean and vector-space approaches to information retrieval; Web structure, PageRank, and hubs and authorities; machine translation; statistical learning in infants; language models, context-free grammars, and hidden Markov models
4. Computability; or, the unexpected hanging: Turing machines; the uncomputability of the halting function; undecidability for continuous paradigms; zero-knowledge protocols
5. The Turing Test; or, the ultimate final exam: Turing's proposal; the Chinese Room; the Loebner prize