CS540, Spring 2018
Department of Computer Sciences
University of Wisconsin–Madison
This schedule is tentative and subject to change. Please check back often.
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.
| Date | Lecture | Readings | Homework Released | Homework Due | |
|---|---|---|---|---|---|
| Wed Jan 24, Fri Jan 26 | course overview [Slides] | Stanford One Hundred Year Study on Artificial Intelligence | |||
| Mon Jan 29, Wed Jan 31, Fri Feb 2 | uninformed search [Slides] | Textbook chapter 3 | Homework 1 released. See the coursework webpage. | ||
| Mon Feb 5 | informed search part 1 [Slides] | Textbook chapter 3 | |||
| Wed Feb 7 | informed search part 2 [Slides] | Homework 1 due. See the coursework webpage. | |||
| Fri Feb 9 | search review | ||||
| Mon Feb 12 | 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. | ||
| Wed Feb 14 | advanced search part 2: simulated annealing [Slides] | Optional: Science'83 paper; Java applet for experiments with simulated annealing |
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| Fri Feb 16 | advanced search part 3: genetic algorithms [Slides] | Optional: In-depth tutorial to genetic algorithms | |||
| Mon Feb 19 | game playing part 1: minimax search [Slides] | Textbook chapter 5.1-5.3 | Homework 3 release. See the coursework webpage. | Homework 2 due. See the coursework webpage. | |
| Wed Feb 21 | game playing part 2: alpha-beta pruning [Slides] | Optional reading: AlphaGo, Nature article; Report on AI and Video Games |
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| Fri Feb 23 | game playing part 3: large games [Slides] | ||||
| Mon Feb 26 | Probability and statistics basics [Slides] | Textbook Chapter 14.1, 14.2, 14.4; Optional: Introduction to Probability, Statistics, and Random Processes |
Homework 4 released. See the coursework webpage. | Homework 3 due. See the coursework webpage. | |
| Wed Feb 28 | Probability and statistics basics: continued | Optional: 1. Textbook for randomized methods (with elegant examples) Probability and Computing: Randomized Algorithms and Probabilistic Analysis 2. Textbook with rigorous treatment of probability theory: Probability: Theory and Examples |
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| Fri Mar 2 | Probability and statistics basics: continued | ||||
| Mon Mar 5 | 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. | |
| Wed Mar 7 | Natural language and statistics: continued | ||||
| Fri Mar 9 | 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; | |||
| Mon Mar 12 | Review [Slides] | Homework 5 due. See the coursework webpage. | |||
| Wed Mar 14 | Review continued | ||||
| Wed Mar 14, 7:15 pm - 8:15 pm Midterm Exam |
Room: Ingraham Hall Room B10 | Homework 6 released. See the coursework webpage. | |||
| Fri Mar 16 | Propositional logic part 1 [Slides] | Textbook Chapter 7.1, 7.3 - 7.5 | |||
| Mon Mar 19 | Propositional logic part 1 continued | Homework 7 released. See the coursework webpage. | |||
| Wed Mar 21 | Propositional logic part 2 [Slides] | ||||
| Fri Mar 23 | First order logic part 1 [Slides] | Textbook Chapter 8.1-8.3 | Homework 6 due. See the coursework webpage. | ||
| Mar 24 - Apr 1 | Spring recess | ||||
| Mon Apr 2 | First order logic part 2 [Slides] | Optional: Textbook Chapter 9 | |||
| Wed Apr 4 | Introduction to Machine Learning part 1: hierarchical clustering [Slides] |
Introduction to Semi-Supervised Learning, Xiaojin Zhu and Andrew B. Goldberg Chapter 1 [free access from UW IP addresses]; visualizing representations |
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| Fri Apr 6 | Introduction to Machine Learning part 2: k-means clustering [Slides] | ||||
| Mon Apr 9 | Introduction to Machine Learning part 3: k-nearest neighbor and linear regression [Slides] | [Jerry Zhu's notes] | Homework 8 released. See the coursework webpage. | Homework 7 due. See the coursework webpage. | |
| Wed Apr 11 | Introduction to Machine Learning part 4: linear classification [Slides] | [Jerry Zhu's notes] | |||
| Fri Apr 13 | Review [Slides] | Optional: machine learning textbook Pattern Recognition and Machine Learning by Chris Bishop. | |||
| Mon Apr 16 | Review continued | Homework 9 released. See the coursework webpage. | Homework 8 due. See the coursework webpage. | ||
| Wed Apr 18 | Neural networks part 1: perceptron [Slides] | Textbook Chapter 18.7; Nature'15 Review; Optional: Deep learning book |
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| Fri Apr 20 | Neural networks part 2: multiple layer neural networks [Slides] | visualizing backpropagation | |||
| Mon Apr 23 | Neural networks part 3: convolutional neural networks [Slides] | [Vamsi Ithapu's slides]; visualizing convolutional neural networks |
Homework 10 released. See the coursework webpage. | Homework 9 due. See the coursework webpage. | |
| Wed Apr 25 | Reinforcement learning part 1: introduction and Markov Decision Process [Slides] | Reinforcement learning: an introduction, 2nd ed., Sutton and Barto, Chapter 1, 3, 6.5 |
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| Fri Apr 27 | Reinforcement learning part 2: Q-learning [Slides] | Matlab demo code for Qleanring | Exercise for reinforcement learning and solution | ||
| Mon Apr 30 | Review [Slides] | ||||
| Wed May 2 | Homework 10 due. See the coursework webpage. | ||||
| Sunday May 6, 10:05AM - 12:05PM Final Exam |
room VAN VLECK B102, VAN VLECK B105 |