Computation, Information,

and Intelligence (CS 172/ENGRI 172/INFO 172/COGST 172), Spring 2007 (original) (raw)

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; problem-space 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 depth-first 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) vector-operations 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 B-trees; start of the vector-space model

Handouts: lecture aid

Lecture 15
2/23

term weighting: tf-idf 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 vector-space 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

in-class prelim

Lecture 19
3/5

preferential attachment

Handouts: (1) lecture aid; (2) Prelim Two solutions and stats

Lecture 20
3/7

link-based ranking: PageRank

Handouts: (1) lecture aid; Homework Three

Lecture 21
3/9

more on PageRank

Handouts: lecture aid

Lecture 22
3/12

end of random-surfer 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 context-free 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 infoand 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)