CS540, Spring 2019
Department of Computer Sciences
University of Wisconsin–Madison
This schedule is tentative and subject to change. Please check back often. In particular, the deadlines for the homework sets/project are tentative, please see Canvas for the actual deadlines.
The homework problem sets can be found on the Coursework page.
Those explicitly noted as optional are for students interested in that specific topic. "A, B; C; D" means to read (A OR B) AND C AND D. Text in red means a link to the reading material.
Please view the pdf slides using Adobe PDF reader. Some notations do not show up correctly in say Chrome.
| Date | Lecture | Readings | Homework Released | Homework Due |
|---|---|---|---|---|
| Jan 22, 24 | Course overview [Slides] | Stanford One Hundred Year Study on Artificial Intelligence | ||
| Jan 29 | Uninformed search [Slides] | Textbook chapter 3 | Homework 1 released. See the coursework webpage. | |
| Jan 31 | Canceled due to weather | |||
| Feb 5, 7 | Informed search part 1 [Slides] | Textbook chapter 3 | Homework 1 due. See the coursework webpage. | |
| Feb 12, 14 |
Informed search part 2 [Slides]; Advanced search part 1: hill climbing [Slides] |
Textbook chapter 4.1; Optional: visualization [need to download Wolfram CDF Player] |
Homework 2 released. See the coursework webpage. | |
| Feb 19, 21 |
Advanced search part 2: simulated annealing [Slides]; Advanced search part 3: genetic algorithms [Slides]; Probability and statistics basics [Slides] |
Textbook Chapter 14.1, 14.2, 14.4; Optional for advanced search: Science'83 paper; Java applet for experiments with simulated annealing; In-depth tutorial to genetic algorithms; Optional for probability: 1. Introduction to Probability, Statistics, and Random Processes 2. Textbook for randomized methods (with elegant examples) Probability and Computing: Randomized Algorithms and Probabilistic Analysis 3. Textbook with rigorous treatment of probability theory: Probability: Theory and Examples |
Homework 3 release. See the coursework webpage. | Homework 2 due. See the coursework webpage. |
| Feb 26, 28 |
Game playing part 1: minimax search [Slides]; Game playing part 2: alpha-beta pruning [Slides]; Game playing part 3: large games [Slides] |
Textbook chapter 5.1-5.3; Optional reading: AlphaGo, Nature article; Report on AI and Video Games |
Homework 4 released. See the coursework webpage. | Homework 3 due. See the coursework webpage. |
| Mar 5, 7 | Natural language and statistics [Slides] | [Jerry Zhu's notes]; Optional: 1. Stanford NLP softwares, including English Tokenizer; 2. Textbook for Statistical Natural Language Processing: Foundations of Statistical Natural Language Processing |
Homework 5 released. See the coursework webpage. | Homework 4 due. See the coursework webpage. |
| Mar 8 Midterm Makeup Exam |
5:30pm-7pm, CS 1240 | |||
| Mar 12, 14 | Linear algebra and Principal Component Analysis (PCA) [Slides] | [Jerry Zhu's notes]; Jerry Zhu's note on math background; matlab demo PCA.m with its data files vocabulary_stopword_removal.txt and WARC201709BOW.txt; Numerical Algorithms, Chapter 1; | Homework 6 released. See the coursework webpage. | Homework 5 due. See the coursework webpage. |
| Mar 14 Midterm Exam |
General session: 5:30pm-7:00pm, Ingraham B10; McBurney session: 5:30pm-8:30pm, Social Science 4322 |
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| Mar 16 - Mar 24 | Spring recess | |||
| Mar 26, 28 |
Propositional logic part 1 [Slides]; Propositional logic part 2 [Slides] |
Textbook Chapter 7.1, 7.3 - 7.5 | Homework 7 released. See the coursework webpage. | |
| Apr 2, 4 |
First order logic part 1 [Slides]; First order logic part 2 [Slides] |
Textbook Chapter 8.1-8.3; Optional: Textbook Chapter 9 |
Homework 6 due. See the coursework webpage. | |
| Apr 9, 11 | Introduction to Machine Learning part 1 and part 2: basics and clustering [Slides] |
Introduction to Semi-Supervised Learning, Xiaojin Zhu and Andrew B. Goldberg Chapter 1 [free access from UW IP addresses]; visualizing representations |
Homework 8 released. See the coursework webpage. | Homework 7 due. See the coursework webpage. |
| Apr 16, 18 |
Introduction to Machine Learning part 3: k-nearest neighbor and linear regression [Slides]; Introduction to Machine Learning part 4: linear classification [Slides] |
[Jerry Zhu's notes]; Optional: machine learning textbook Pattern Recognition and Machine Learning by Chris Bishop. |
Homework 9 released. See the coursework webpage. | Homework 8 due. See the coursework webpage. |
| Apr 23, 25 | Neural networks part 1: perceptron [Slides]; Neural networks part 2: multiple layer neural networks [Slides] |
Textbook Chapter 18.7; Nature'15 Review; visualizing backpropagation; visualizing convolutional neural networks; Optional: Deep learning book |
Homework 10 released. See the coursework webpage. | Homework 9 due. See the coursework webpage. |
| Apr 30, May 2 |
Neural networks part 3: convolutional neural networks [Slides]; Introduction to Reinforcement Learning [Slides] |
Reinforcement learning: an introduction, 2nd ed., Sutton and Barto, Chapter 1, 3, 6.5; |
Homework 10 due. See the coursework webpage. | |
| May 7, Final Exam |
General session: 10:05AM - 12:05PM, VAN VLECK B102; McBurney session: 11:00AM - 2:00PM, CS 7331. |