University of Wisconsin-Madison CS 540 Section 1: Introduction to Artificial Intelligence Spring 2020 (T,Th 9:30-10:45, Humanities 3650)

In response to the Covid-19 pandemic, this is how we plan to teach CS540 starting the week of March 23, 2020: 1. [During lecture hours]: Each lecture will be a series of short pre-recorded videos posted to Youtube. Video links will be posted to both Canvas/Pages (View All Pages/Video Lectures) and Piazza @10 (Lecture doodles) before class. We don't have any planned VIDEO interaction happening specifically during lecture hours (but see Piazza below) and that all lecture content will be delivered asynchronously. We suggest having a routine that involves watching the content during class time, but acknowledge that your time constraints will likely have changed. As a test, we already posted two short Youtube videos on HAC single linkage and complete linkage clustering, please try them out. 2. [Also during lecture hours]: We will use Piazza for real-time Q&A. Please follow these rules: a. Please check if someone has posted the same / similar question before you; it's much easier if we build on the thread. b. Use an informative "Summary" line to help others. To summarize: In class you watch Youtube videos and ask / discuss questions on Piazza. 3. [Office hours]: Instructors / TAs / peer mentors will hold office hours (same schedule as before) in BBCollaborate Ultra, where the 250-student limit should not be an issue. In Canvas, go to the CS540 course, on the left menu you will find BBCollaborate Ultra. We set up an "Office hour test" session, you can join that session at any time to get familiar with the system. That session is not monitored, though, so don't expect someone to chat with you -- use the actual sessions that will be set up later. 4. Homework projects will be submitted the same way as before. 5. The final exam will be online. More details later.

ScheduleHomework Posted|Date|Lecture Topic (P0 AI100) 1/21 Python for Java Gurus (slides); Uninformed search (slides, ch 3) 1/23 Search: state space (P1 python) 1/28 Search: A* (slides, ch 3) 1/30 Search: hill climbing (slides, 4.1) (P2 search) 2/04 Search: genetic algorithm 2/06 Search: summary (P3 search) 2/11 Math: Probability (slides, 14.1, 14.2, 14.4, Introduction to Probability, Statistics, and Random Processes Ch 1, 2, 3) 2/13 Math: Statistics (Natural language and statistics notes, NLPdemo.zip, Stanford English Tokenizer) 2/18 Math: Decision theory: Naive Bayes Text classifier (notes) (P4 math) 2/20 Math: Linear algebra (math crib sheet, Solomon Numerical Algorithms Ch 1) Eigenface, Principal Component Analysis (notes, matlab/octave PCA demo, Roughgarden, Valiant notes on PCA vs. SVD) 2/25 Math: Logic (slides, 7.1, 7.3 - 7.5) 2/27 Math: summary 3/03 Game: Minimax (slides, 5.1 - 5.3) (P5 PCA) 3/05 Game: in-class activity 3/10 Game: summary 3/12 Machine learning: intro, hierarchical clustering (slides, Z&G ch 1 [free access from UW IP addresses]) 3/24 Machine learning: k-means clustering (P6 game) 3/26 Machine learning: linear regression (notes) 3/31 Machine learning: kNN classifier (knn demo) (P7 cluster) 4/02 Machine learning: perceptron (slides) 4/07 Machine learning: neural network (P8 regression) 4/09 Machine learning: neural network 4/14 Machine learning: neural network (schema) (P9 NN) 4/16 Machine learning: deep learning 1 (Nature'15, Deep Learning book Ch 6, 9, Convolutional neural nets, 18.7) 4/21 Machine learning: deep learning 2 (P10 DL) 4/23 Machine learning: reinforcement learning 1 (Sutton and Barto 2nd ed. 1.1-1.4, 3, 6.5) 4/28 Machine learning: reinforcement learning 2 (Qlearning.m) 4/30 Machine learning: summary (CCC AI Roadmap)

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.CanvasHomework assignments are posted in Canvas. Homework must be submitted via the Canvas system. If there is a programming part, electronically hand in files containing the python code that you wrote for the assignment. We provide specific instructions for each homework.

Course Staff and Office HoursInstructors: Hobbes LeGault, legault@wisc.edu, W 1-2 and by appointment, CS 4388 Professor Jerry Zhu, jerryzhu@cs.wisc.edu, M 3-4 and by appointment, CS 6391TAs: hold office hours, grade your submissions Apurbaa Bhattacharjee, bhattachar26@wisc.edu : R 2-4pm in 4293 CS Brian Chang, brian.chang@wisc.edu : TW 3-4pm in 4294 CS Dandi Chen, dchen282@wisc.edu : T 1-2pm, W 3:30-4:30pm in 1302 CS Bastin Joseph, bjoseph5@wisc.edu : T 4-6pm in 4297 CS Akshaya Kalyanaraman, kalyanarama3@wisc.edu : M 2:30-3:30pm, F 12:30-1:30pm in 1302 CS Rohit Sharma, rsharma54@wisc.edu : RF 4-5pm in 4235 CS Tuan Dinh, dinh5@wisc.edu : M 4-6pm in 1302 CSPeer Mentors: hold office hours Tanmay Bagaria, tbagaria@wisc.edu : T 1:15-5:15pm, R 1-6pm Steven (Pengyu) Kan, pkan2@wisc.edu : T 5:30-7pm, W 4-5pm, R 5:30-9pm, F 5-7pm Atharva Kulkarni, akulkarni23@wisc.edu : Su 10-12pm, T 1-3pm, W 10a-12pm, F 10a-12pm Daniel (Xuri) Li, Xli892@wisc.edu : W 2-5pm, F 2-5pm Tushar Narang, tnarang@wisc.edu : M 3-4pm, T 12-2pm, W 3-4pm, R 12-3:30pm, F 1:30-3pm Suyan Qu, squ27@wisc.edu : M 3-6pm, T 7-9pm, W 7-9pm Mentors will be available in 1304 CS at the following times each week: Su: 10-12pm Mo: 3-6:30pm Tu: noon-9pm We: 10am-9pm (break 12-2pm, 6:30-7pm) Th: noon-9pm Fr: 10am-7pm (break 1-1:30pm)Graders: grade your submissions Erik Bjorklund, ebjorklund@wisc.edu Yi-Shiun Chang, chang242@wisc.edu Chia-Wei Chen, cchen562@wisc.edu Grishma Gupta, ggupta7@wisc.edu Phanindra Moganti, moganti@wisc.edu Aashish Richhariya, richhariya@wisc.edu Abhash Singh, singh234@wisc.eduOptional 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), mathematical maturityGrading:

- Midterm Exam: about 15%
- Final Exam: about 15%
- Homework Assignments: about 70%

- Midterm Exam: 3/11/2020, Wednesday 5-7pm. Van Vleck B102 and Ingraham B10 Topics: all topics in lectures up to the exam, related slides and notes, except: Path Checking DFS, Simulated Annealing, (logic) Resolution.
- Final Exam: 5/5/2020, Tuesday 12:25PM - 2:25PM. Topics: Cumulative. everything on the course webpage, including slides, notes, selected readings (but not whole books)