CS 540 Section 1: Introduction to Artificial Intelligence Fall 2017

Lectures: M,W,F 8:50am - 9:40am, Noland 132Instructor: Professor Jerry ZhuOffice Hours(you can come to any one of us) Jerry Zhu: F 2-3pm, CS6391, 608-890-0129, jerryzhu@cs.wisc.edu (section 2 instructors:) Collin Engstrom: Tu 5:30-6:30pm, CS4384, engstrom@cs.wisc.edu Dan Griffin: Mon 4:00-5:00pm, CS4384, dgriffin5@wisc.edu Ross Kleiman: Wed 1:00-2:00pm, CS4384, rkleiman@cs.wisc.edu (TAs) Zhenyu Zhang: Mondays 1-2pm, CS 7370, zhenyu@cs.wisc.edu Wolong Yuan: Mondays 2-2:30 pm, CS 7394, wyuan@cs.wisc.edu Stephen Sheen: Tuesdays 12:00-12:30 pm, CS 1308, sheen2@wisc.edu Ziyun Zeng: Tuesdays 4-5pm, CS 4331, ziyun@cs.wisc.edu Abhanshu Gupta: Wednesdays 5:00~6:00 pm, CS 4394, abhanshu@cs.wisc.edu Kurtis Liu: Thursdays 8-9 am, CS 5384, kliu89@wisc.edu Tananun Songdechakraiwut: Thur 2:30 - 3:30pm, CS 7331, tananun@cs.wisc.edu Aiqing Huang: Thursdays 4:00-5:00 pm, CS 4395, aiqingh@cs.wisc.edu Shiqi Yang: Fridays 3:00-4:00 pm, CS 4394, sqyang@cs.wisc.eduPiazzacombined sections 1+2Canvashttps://canvas.wisc.edu/courses/51965HomeworksExaminationsTentative Topics (reading list)Introduction (Stanford One Hundred Year Study on Artificial Intelligence; optional: textbook ch 1, 2; AAAI) Search Uninformed search: Breadth-first search, uniform-cost search, depth-first search, iterative-deepening (slides, ch 3) Informed search: A* algorithm (slides, ch 3) Greedy and stochastic search: Hill-climbing, Simulated annealing, genetic algorithms (slides, 4.1) Game playing: Minimax, alpha-beta pruning (slides, 5.1 - 5.3) Mathematics for Data Science Probability and statistics basics (slides, 14.1, 14.2, 14.4, Introduction to Probability, Statistics, and Random Processes) Application: Natural language and statistics (notes, Stanford English Tokenizer) Linear algebra and Principal Component Analysis (PCA) (notes, matlab demo PCA.m with its data files vocabulary_stopword_removal.txt and WARC201709BOW.txt, Numerical Algorithms Ch 1, math crib sheet) Logic: Propositional (slides, 7.1, 7.3 - 7.5) and First Order (slides, FOL inference slides) Machine Learning Unsupervised Learning Clustering (slides, Z&G ch 1 [free access from UW IP addresses]) Dimension reduction (we did PCA already) Supervised Learning k-Nearest-Neighbor classifier (knn demo) Linear regression, logistic regression (notes) Neural networks and deep learning (Nature'15, Deep Learning book, slides, Convolutional neural nets, 18.7, hand notes on backprop) Reinforcement Learning (Sutton and Barto 2nd ed., 1.1-1.4, 3, 6.5, Qlearning.m)Prerequisite: (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275)Textbook: Artificial Intelligence: A Modern Approach,3rd edition(blue) Stuart J. Russell and Peter Norvig. Prentice Hall, Englewook Cliffs, N.J., 2010Grading:

- Midterm Exam: about 15%
- Final Exam: about 30%
- Homework Assignments: about 55%