"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 courseevaluation 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, exCornell prof. and Nobel prize nonrecipient
Lecture time/place:  MWF 10:1011:00am, Thurston 203 
Instructor:  Prof. Lillian Lee 4152 Upson, , x58119, www.cs.cornell.edu/home/llee Office hours: Tuesdays 34 and Fridays 11:1512 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:  inclass prelims Friday March 2 and Friday April

Links: 
Course description and policies Coursestaff 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):
Lecture 1 1/22 
true programmability; AI successes; the romance of AI  Handouts: lecture aid and course description and policies 
Lecture 2 1/24  problem solving; problemspace specification by explicit enumeration  Handouts: lecture aid 
Lecture 3 1/26  more on completeness; implicit specifications  Handouts: lecture aid 
Lecture 4 1/29  more on implicit specification  Handouts: lecture aid 
Lecture 5 1/31  path trees and depthfirst search  Handouts: (1) lecture aid; (2) Homework One; (3) course staff contact info and weekly office hours 
Lecture 6 2/2  games; minimax  Handouts: lecture aid 
Lecture 7 2/5  pruning  Handouts: lecture aid 
Lecture 8 2/7  perceptrons (beginning of learning)  Handouts: (1) lecture aid (2) vectoroperations reference sheet 
Lecture 9 2/9  perceptrons: geometric characterization  Handouts: lecture aid 
Lecture 10 2/12  formalization of learning; obstacles to perceptron learning  Handouts: lecture aid 
Lecture 11 2/14  the gap condition; the perceptron learning algorithm (PLA)  Handouts: (1) lecture aid (2) solutions to Homework One; (3) Homework Two 
Lecture 12 2/16  length and the perceptron learning algorithm; proof of the perceptron convergence theorem  Handouts: lecture aid 
Lecture 13 2/19  Information retrieval basics  Handouts: lecture aid 
Lecture 14 2/21  end of Btrees; start of the vectorspace model  Handouts: lecture aid 
Lecture 15 2/23  term weighting: tfidf weighting  Handouts: (1) lecture aid; (2) Prelim One info and last year's exam; (3) draft Homework Two solutions 
Lecture 16 2/26  end of the vectorspace model; start of link analysis  Handouts: lecture aid 
Lecture 17 2/28  models of web growth: uniform attachment  Handouts: (1) lecture aid, (2) official solutions to HW2 
Lecture 18 2/28  inclass prelim  
Lecture 19 3/5  preferential attachment  Handouts: (1) lecture aid; (2) Prelim Two solutions and stats 
Lecture 20 3/7  linkbased ranking: PageRank  Handouts: (1) lecture aid; Homework Three 
Lecture 21 3/9  more on PageRank  Handouts: lecture aid 
Lecture 22 3/12  end of randomsurfer model; begin hubs and authorities  Handouts: lecture aid 
Lecture 23 3/14  hubs and authorities  Handouts: (1) lecture aid; (2) Homework Four 
Lecture 24 3/16  more on modern search engines; introduction to natural language procesing  Handouts: lecture aid 
Lecture 25 3/26  challenges in natural language processing  Handouts: lecture aid 
Lecture 26 3/28  modeling syntactic structure: intro to contextfree grammars  Handouts: (1) lecture
aid; (2) solutions to Homework Three Info on the Messenger Lecturer, John Searle: announcement and abstract, poster, possible preview 
Lecture 27 3/30  more on CFGs  Handouts: (1) lecture aid; (2) Prelim Two info and last year's exam 
Lecture 28 4/2  intro to Earley's algorithm  Handouts: (1) lecture aid; (2) Homework Four solutions 
Lecture 29 4/4  more on Earley parsing  Handouts: lecture aid 
Lecture 31 4/9  finishing parsing  Handouts: (1)lecture aid; Prelim Two solutions 
Lecture 32 4/11  intro to grammar learning  Handouts: (1) lecture aid; (2) Homework Five 
Lecture 33 4/13  smoothing; intro to machine translation  Handouts: lecture aid 
Lecture 34 4/16  learning to translate  Handouts: lecture aid 
Lecture 35 4/18  unsupervised Japanese segmentation  Handouts: lecture aid 
Lecture 36 4/20  human statistical learning  Handouts: (1) lecture aid; (2) readings cover sheet; (3) Computing Machinery and Intelligence, Alan Turing (online access enabled through Cornell IP addresses or Cornell library gateway); (4) Minds, Brains, and Programs, John Searle 
Lecture 37 4/23  introduction to Turing machines  Handouts: lecture aid 
Lecture 38 4/25  the halting function  Handouts: (1) lecture aid; (2) Homework Six 
Lecture 39 4/27  limits on computation  Handouts: lecture aid 
Lecture 40 4/30  more limits on computation  Handouts: (1) lecture aid; (2) Solutions to Homework Five 
Lecture 41 5/2  zero knowledge protocols  Handouts: lecture aid 
Lecture 42 5/4  Turing test(s)  Handouts: (1) lecture aid; (2) information regarding the final exam (cover sheet here) 