Unless explicitly called out on the schedule page, these readings are optional. But they may help you understand some of the concepts or dig into them in more detail. Additional pointers for specific topics are in the lecture notes.
To access some of the the electronic readings (i.e. without paying money), get onto UIUC's network (directly or via the VPN). With the VPN, you may need to select "tunnel all" rather than "split tunnel." (External folks looking at this page: your university may also have a suitable site license.)
Useful textbooks covering part or all of our material:
| Topic | Misc | Jurafsky and Martin 3rd edition draft |
Poole and Mackworth 3rd edition |
Russell and Norvig 4th edition |
Conferences |
|---|---|---|---|---|---|
| Intro | |||||
| Probability and Naive Bayes | Forsyth PS | App. B
Ch 2 |
9.1,9.2, 10.2.2 | Ch 12 | |
| Search | Ch. 3 | 3.1-3.6 | |||
| Robot Motion | 26.1-26.6 | ICRA, IROS | |||
| Natural Language | ch 15, ch 18 | Ch 23 | ACL Anthology ICASSP, Interspeech |
||
| Hidden Markov Model | Forsyth PS, chapter 14 | ch 17 | 14.1-14.3 | ||
| Computer Vision | Ch 25 | CVPR, ICCV, ECCV |
|||
| Classification |
Forsyth AML, 1.2 and 2.2
Forsyth PS, ch 11 |
7.1-7.4 | 19.1-19.4 | ||
| Linear Classifiers | ch 4 | 19.6 | |||
| Neural Nets |
Forsyth AML, ch 16 Goldberg ch. 4-5 Karpathy notes |
ch 6 | 8.1-8.4 | Ch. 21 | |
| Vector semantics | ch 5 | 8.5.1 | Ch 24 | ||
| Sequential Neural Nets | Vaswani et al
Goldberg ch. 4-5, 13-15 |
sec 2.4, ch 3, ch 7, ch 8 | 8.5 | ||
| Markov Decision Processes Reinforcement Learning |
12.5, ch 13 | 17.1-17.2 | |||
| Constraint Satisfaction | 4.1-4.3 | Ch 6, 4.1 | |||
| Classical Planning | Ch 6 | 11.1-11.3 | |||
| Games | 14.1-14.3 | 5.1-5.6 | |||
| Bayes Nets | Charniak Bayes Nets | 9.3 | Ch 13 | ||
| Core AI (general) | AAAI, IJCAI AKBC |
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| Machine Learning (general) | NIPS/NeurIPS ICML, ICLR |