Assignments

Date Topic
Week 1
Aug 27, 29
Introduction, AI Ethics and Society

Reading: due Monday 8/26, 5:00 pm
Reading: due Wednesday 8/28, 5:00 pm
  • Textbook Chapter 1 through the end of Section 1.1 (Rest of Chapter 1 is optional - we recommend skimming it at least)
  • Textbook Chapter 2.1-2.3
Programming: due Tuesday 8/27, 11:59 pm
Programming: due Thursday 8/29, 11:59 pm
Week 2
Sept 3, 5
Search

Reading: due Monday 9/2, 5:00 pm
  • Textbook Chapter 3
Homework: due Monday 9/16, 11:59 pm
Programming: due Monday 9/23, 11:59 pm
Week 3
Sept 10, 12
Constraint Satisfaction Problems and Local Search

Reading: due Monday 9/9, 5:00 pm
  • Textbook Chapter 6
  • Textbook Sections 4.1 and 4.2 (Rest of Chapter 4 is optional - we recommend skimming it at least)
Homework: due Monday 9/30, 11:59 pm
Week 4
Sept 17, 19
Adversarial Search, Utilities

Reading: due Monday 9/16, 5:00 pm
  • Textbook Chapter 5 through the end of Section 5.5 (Rest of chapter 5 is optional - we recommend at least skimming it)
  • Textbook Chapter 16 through the end of Section 16.3
Programming: due Wednesday 10/2, 11:59 pm
Week 5
Sept 24, 26
Markov Decision Processes

Reading: due Monday 9/23, 5:00 pm
  • Textbook Chapter 17 through the end of Section 17.3
  • Sutton and Barto Chapters 3 and 4
Homework: due Monday 10/7, 11:59 pm
Week 6
Oct 1, 3
Midterm I, Reinforcement Learning I

Midterm I exam:
  • 2-hour exam on Gradescope which can be completed in a 40-hour window
Programming: due Wednesday 10/16, 11:59 pm
Week 7
Oct 8, 10
Reinforcement Learning II, Probability

Reading: due Monday 10/7, 5:00 pm
Homework: due Monday 10/21, 11:59 pm
Week 8
Oct 15, 17
Bayes Nets: Representation, Independence

Reading: due Monday 10/14, 5:00 pm
  • Textbook Chapter 14 through the end of Section 14.5
Programming: due Wednesday 10/30, 11:59 pm
Week 9
Oct 22, 24
Bayes Nets: Inference, Sampling

Reading: No reading due this week.
Programming: due Wednesday 11/13, 11:59 pm
Week 10
Oct 29, 31
Hidden Markov Models, Particle Filters

Reading: due Monday 10/28, 5:00 pm
  • Textbook Chapter 15 through the end of Section 15.3.
  • Textbook Section 15.4 is optional (we recommend skimming it at least)
  • Textbook Chapter 15.5
Homework: due Monday 11/11, 11:59 pm
  • Homework 5: HMMs, Particle Filtering, Naive Bayes, ML Concepts
Programming: due Wednesday 11/20, 11:59 pm
Week 11
Nov 5, 7
Midterm II, CoRL week (no class)

Midterm II exam:
  • 2-hour exam on Gradescope which can be completed in a 40-hour window
Week 12
Nov 12, 14
Decision Networks, Naive Bayes

Reading: due Monday 11/11, 5:00 pm
  • Textbook Sections 16.5 and 16.6
  • Textbook Chapter 18 through the end of Section 18.2
  • Skim Sections 18.3, but pay attention to the definition of overfitting in 18.3.5.
  • Section 18.4
    • Section 18.5 is optional
  • Textbook Chapter 20 through the end of Section 20.2
    • You can skim 20.2.3 if you're not comfortable with reasoning about continuous distributions (e.g. Gaussians).
    • 20.2.5 is just an overview without all the details - do your best.
    • Textbook Section 20.3 is optional
Homework: due Monday 12/2, 11:59 pm
  • Homework 6: Perceptons, Neural Networks, Gradient Descent
Programming: due Wednesday 12/4, 11:59 pm
Week 13
Nov 19, 21
Perceptrons, Clustering, Deep Learning

Reading: due Monday 11/18, 5:00 pm
  • Section 18.6 except Sections 18.6.2
    • Sections 18.6.2 is optional
  • Sections 18.7, 18.8, 18.9, and 18.11.
    • Section 18.10 is optional.
Week 14
Nov 26, 28
Thanksgiving (no class)

Week 15
Dec 3, 5
Guest Lecture, Conclusion

Guest speaker: [TBD]

Reading: due Monday 12/2, 5:00 pm
  • Textbook Chapters 26 and 27
Final Exam
Date/Time: [TBD]
Final Exam
Location: [TBD]