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Fall 2015 Syllabus

This syllabus is subject to change. In particular, the midterm dates will not be finalized until a week or so into the course.

Day Topic Reading Slides Discussion Assignment Due
Th 8/27 Introduction to AI: Past, Present, Future

Ch. 1, 26.3, 27.4

pdf pptx

P0: Tutorial

F 9/4, 5pm

Tu 9/1 Agents and Environments Ch. 2 pdf pptx Dis0 (no handout) HW1 M 9/7, 11:59pm
Th 9/3 Uninformed Search Ch. 3.1-4 pdf pptx SBS-1

Tu 9/8 Informed Search and Heuristics Ch. 3.5-6 pdf pptx

Dis1 (sol)

HW2 M 9/14, 11:59pm
Th 9/10 Local Search and Agents Ch. 4 pdf pptx P1: Search and Games F 9/25, 5pm

Tu 9/15 Game Playing Ch. 5.1-5 pdf pptx Dis2 (sol)
Prob1 (sol)
HW3 T 9/22, 11:59pm
Th 9/17 Constraint Satisfaction Problems Ch. 6.1, 6.3-5 pdf pptx  SBS-3

Tu 9/22 Propositional Logic: Semantics and Inference Ch. 7.1-5, 7.6.1 pdf pptx

Dis3 (sol)
Prob2 (sol)

HW4  M 9/28, 11:59pm
Th 9/24 Logical Agents Ch. 7.7 pdf pptx P2: A Logical Planning Agent F 10/16, 5pm 

Tu 9/29 First Order Logic Ch. 8.1-3, 9.1 pdf pptx Dis4 (sol) No HW
Th 10/1 MIDTERM Practice Midterm (solutions) T 9/29, 12:30pm

Tu 10/6 Probability Ch. 13.1-5 pdf pptx

Dis5 (sol)

Prob3 (sol)

HW5 W 10/14, 11:59pm
Th 10/8 Bayes Nets: Syntax and Semantics Ch. 14.1-3 pdf pptx      

Tu 10/13 Bayes Nets: Exact Inference  Ch. 14.3 pdf pptx

Dis6 (sol)

Prob4 (sol)


HW6 W 10/21, 11:59pm
Th 10/15 Bayes Nets: Approximate Inference Ch. 14.4 pdf pptx


P3: An HMM-based Agent F 10/30, 5pm

Tu 10/20 Markov Models Ch. 15.1-3, 22.1 pdf pptx Dis7 (sol) HW7 W 10/28, 11:59pm
Th 10/22 Dynamic Bayes Nets and Particle Filtering Ch. 15.5 pdf pptx  

Tu 10/27 Decision Theory Ch. 16.1-3, 16.5-6 pdf pptx Dis8 (sol) HW8 W 11/4, 11:59pm
Th 10/29 Markov Decision Processes I Ch. 17.1 pdf pptx P4: Decision-making and
Learning Agent
F 11/13, 5pm

Tu 11/3 Markov Decision Processes II Ch. 17.2-3 pdf pptx Dis9 (sol) HW9 W 11/11, 11:59pm
Th 11/5 Reinforcement Learning I Ch. 21.1-3 pdf pptx

Tu 11/10 ML: Classification and Regression Ch. 18.1-4, 18.6 pdf pptx No Discussion


F 11/20, 11:59pm
Th 11/12 ML: Classification and Neural Networks Ch. 18.7 pdf pptx



P5: Learning and Classification F 12/4, 5pm

Tu 11/17 ML: Classification and Neural Networks (cont.) Dis10 (sol)
HW11 W 12/02, 11:59pm
Th 11/19 ML: Statistical Learning Ch. 20 pdf pptx

Tu 11/24 Reinforcement Learning II Ch. 21.4-5 pdf pptx No Discussion No HW
Th 11/26 Thanksgiving Break        

Tu 12/1 Advanced Applications: Natural Language Processing and Computer Vision Optional: Ch. 23, 24 pdf pptx Dis11 (sol) No HW  
Th 12/3 Advanced Applications: Robotics & Conclusion Optional: Ch. 25 pdf pptx

F 12/18

FINAL EXAM (8-11am)
230,234,251 HEARST GYM