CS 540 Section 1: Introduction to Artificial Intelligence Fall 2018 (T,Th 11am - 12:15pm, Van Vleck B102)

Calendar9/6 Introduction (Stanford One Hundred Year Study on Artificial Intelligence; optional: textbook ch 1, 2; AAAI) Breadth-first search, depth-first search (slides, ch 3) Homework 1 9/11 Uniform-cost search, iterative-deepening 9/13 Informed search: A* algorithm (slides, ch 3) Homework 2 9/18 Hill-climbing (slides, 4.1) 9/20 Simulated annealing, genetic algorithms Homework 3 9/25 Minimax Game playing (slides, 5.1 - 5.3) 9/27 Alpha-beta pruning Homework 4 10/2 Probability and statistics basics (slides, 14.1, 14.2, 14.4, Introduction to Probability, Statistics, and Random Processes Ch 1, 2, 3) 10/4 Probability and statistics basics Homework 5 10/9 Natural language and statistics (notes, NLPdemo.zip, Stanford English Tokenizer) 10/11 Linear Algebra (math crib sheet, Solomon Numerical Algorithms Ch 1) 10/16 Principal Component Analysis (PCA notes, matlab/octave PCA demo, Roughgarden, Valiant notes on PCA vs. SVD) Homework 6 10/18 PCA 10/23 Propositional Logic and First Order Logic (slides, 7.1, 7.3 - 7.5) 10/25 Machine Learning basics, Hierarchical Clustering (slides, Z&G ch 1 [free access from UW IP addresses]) 10/30 Hierarchical Clustering Homework 7 11/1 K-means Clustering 11/6 K-Nearest-Neighbor classifier (knn demo) 11/8 Linear regression (notes) 11/13 Linear regression Homework 8 11/15 Logistic Regression 11/20 Perceptron (slides) Homework 9 11/27 Neural networks, Backpropagation (schema) 11/29 Deep learning (Nature'15, Deep Learning book Ch 6, 9, Convolutional neural nets, 18.7) 12/4 Deep learning Homework 10 12/6 Markov Decision Processes (Sutton and Barto 2nd ed. 1.1-1.4, 3, 6.5) 12/11 Reinforcement Learning (Qlearning.m) Homework 11

HomeworkHomework assignments and solutions are posted here. Homework 1, assigned 9/6, due 9/13 before class [pdf | latex | solution] Homework 2, assigned 9/13, due 9/20 before class [pdf | successor.java | latex source files: hw2.tex, graphc.png, graph.jpg | solution: pdf, java] Homework 3, assigned 9/20, due 9/27 before class [version 2 (updated examples) | solution] Homework 4, assigned 9/27, due 10/4 before class [version 1 | solution] Homework 5, assigned 10/4, due 10/11 before class [pdf | latex | solution] Homework 6, assigned 10/16, due 10/30 before class [version 1] Homework 7, assigned 10/30, due 11/8 before class [pdf | latex] Homework 8, assigned 11/13, due 11/20 before class [pdf | HW8.zip] Homework 9, assigned 11/20, due 12/4 before class [pdf] Homework 10, assigned 12/4, due 12/11 before class [pdf | training, eval, test data] Homework 11, assigned 12/11, due 12/18 before class [pdf | latex]PiazzaThe instructors and TAs will post announcements, clarifications, hints, etc. on Piazza. Hence you must check the CS540 Piazza page frequently throughout the term. If you have a question, your best option is to post a message on Piazza. The staff (instructors and TAs) will check the forum regularly, and if you use the forum, other students will be able to help you too. When using the forum, please do not post answers to homework questions before the homework is due. If your question is personal or not of interest to other students, you may mark your question as private on Piazza, so only the instructors will see it. If you wish to talk with one of us individually, you are welcome to come to our office hours. Please reserve email for the questions you can't get answered in office hours or through the forum. https://piazza.com/class/jlmhmm29tn87jkCanvasHomework must be submitted via the Canvas system. Typically, you hand in a single pdf file. If there is a programming part, electronically hand in files containing the Java code that you wrote for the assignment. You do not need to hand in any class files. We provide specific instructions for each homework. https://canvas.wisc.edu/courses/106860

Course Staff and Office HoursInstructor: Professor Jerry Zhu, jerryzhu@cs.wisc.edu, T 4-5, CS 6391TAs: hold office hours, grade your submissions (head TA) Ara Vartanian, aravart@cs.wisc.edu, W 5-6, CS 1207 Yunang Chen, ychen459@wisc.edu, M 4-5, CS 7331 Siddhant Garg, sgarg33@wisc.edu, Th 3-4, CS 1307 Yufei Wang, ywang2395@wisc.edu, Th 4-5, CS 7351 Shuo Yang, syang439@wisc.edu, F 4-5, CS 6378 Zhanpeng Zeng, zzeng38@wisc.edu, W 4-5, CS 4360Peer Mentors: hold office hours Shrehit Goel, sgoel22@wisc.edu Pengyu Kan, pkan2@wisc.edu Nicholas Mandal, nmandal2@wisc.edu Tushar Narang, tnarang@wisc.edu Yash Shah, yshah2@wisc.edu Shaoheng Zhou, szhou228@wisc.edu All peer mentor office hours are in CS 1304: Mon: 10:30 - 12:30 (Yash), 12:00 - 4:00 (Nicholas), 4:15 - 5:45 (Tushar) Tue: 1 - 2:30 (Tushar), 1:00 - 3:00 (Yash), 4-5:30 (Tushar), 4:30 - 7:30 (Pengyu), 4:30 - 8:30 (Shaoheng) Wed: 11:00 - 1:00 (Nicholas), 2:30 - 4:30 (Nicholas) Thu: 12:45 - 4:00 (Shrehit), 1:00 - 5:00 (Yash), 5:00 - 9:00 (Pengyu, Shaoheng) Fri: 12:15 - 3:00 (Shrehit), 4:00 - 5:30 (Tushar) Sat: 10 - 2 (Shaoheng)Graders: grade your submissions Swati Mishra, smishra33@wisc.edu Prerak Mall, pmall@wisc.edu Varun Batra vbatra@wisc.eduRecommended Textbook: Artificial Intelligence: A Modern Approach,3rd edition(blue) Stuart J. Russell and Peter Norvig. Prentice Hall, Englewook Cliffs, N.J., 2010Prerequisite: (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275)Grading:

- Midterm Exam: about 20%
- Final Exam: about 30%
- Homework Assignments: about 50%

- Midterm Exam: Wed Oct 24 7:15-9:15pm. Social Sciences 5208 (last name A-J, capacity 218), Social Sciences 6210 (last name K-Z, capacity 452) Topics covered: all topics in lectures up to the exam; related slides and notes.
- Final Exam: Wed Dec 19 12:25-2:25. SOC SCI 5208 (last name A-J), 6210 (last name K-Z). Cumulative. Topics: everything on the course webpage, including slides, notes, selected readings (but not whole books)