CSCI 373: Artificial Intelligence

Lectures and Readings


The following is a list of lecture topics to be covered along with the relevant readings. All readings are from Russell and Norvig, unless indicated otherwise. The Sutton and Barto readings are quite consistent over the two book editions. Notation has changed, as has the formatting of some examples, but the content of those specific chapters and sections is essentially the same.

Note that this schedule is subject to change, so please consult the online version regularly. For instance, we might want to extend the time spent on certain topics if the class finds them to be especially interesting.

While I anticipate some changes, I have made every attempt to design the schedule in such a way that important milestones (assignments, project, exam) can remain as originally scheduled. If we finish a topic early, I might post the corresponding assignment earlier. Due dates will not change, however, unless the entire class agrees. This should help you in planning your calendars for the semester.

Date Topic Reading
2/3 Introduction Ch. 1
Week of 2/6 Agents and Search Part 1(pdf) Part 2(pdf) Part 3(pdf) Ch. 2 and 3
2/13, 15 Heuristics; Intro to Games (pdf) Games: Minimax (pdf) Ch. 5.1-5
2/17 Winter Carnival. Andrea in Minneapolis to give a talk.
2/20 Games: Alpha-Beta (pdf)
2/22 Finish Games; Utility Theory (pdf) Ch. 16.1-3, Ch. 17.1, Sutton and Barto Ch. 3
2/24 MDPs; Value Iteration (pdf) Sutton and Barto Ch. 4
Week of 2/27 TD and Q-Learning Policy Iteration (pdf) Temporal Difference Learning (pdf) Q-Learning (pdf) Sutton and Barto Ch. 5.1-3, Ch. 6.1, 2, 5
3/6 Q-Learning Wrap-Up (pdf) Discussion (See Holte paper) Bidirectional Search That Is Guaranteed to Meet in the Middle, Holte et al., AAAI-16.
3/8-10 Self-scheduled 2-hour midterm exam. Andrea at SIGCSE.
3/13 Finish discussion of Holte paper
3/15 Probability Intro (pdf) Ch. 13.1-5
3/17 Markov Models Intro (pdf) Ch. 15.1, 2, 5, 6
Spring Break
4/3 Filtering HMMs (pdf) Ch. 15.1, 2, 5, 6
4/5 Andrea in DC for CRA-W Board Meeting.
4/7 Particle Filtering (pdf)
4/10, 12 Learning from Examples Intro to Classifier Learning and Decision Trees (pdf) More Decision Trees (pdf) Ch. 18.1-4, 7
4/14 Finishing Trees; Neural Netowrks (pdf)
4/17 Self-Driving Cars: 30 years of progress and Williams roots (+ finishing up backprop) ALVINN: An Autonomous Land Vehicle In A Neural Network, Pomerleau '87, NIPS-88, Rapidly Adapting Artificial Neural Networks for Autonomous Navigation, Pomerleau '87, NIPS-90
4/19, 21 Deep Learning (part 1), (part 2) A Tutorial on Deep Learning: Part 1, Part 2
4/24 Discussion: Deep Learning Do Deep Convolutional Nets Really Need to be Deep and Convolutional?, Urban et al., ICLR 2017.
4/26, 4/28 Discussions: Ethics
5/1 Constraints CSP (pdf) Ch. 6
5/3 Discussion "Mastering the game of Go with deep neural networks and tree search", Silver et al., Nature, Vol 529, January 2016
5/5 Discussion: Turing Test for Today's AI "Computing Machinery and Intelligence", A.M. Turing, Mind, Vol LIX, No 236, October 1950
5/8 Wrap Up
5/10-12 Project Presentations and Demos