Introduction, AI Ethics

• Read the 2021 AI100 Study Panel Report (esp. page 1-3, 7-11)
• Write a 500-word response to one or more of the sections indicating your reactions (e.g. agreement, disagreement, musings, etc.).
• [Week 1a response form]

• 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
• For your response, select one real or imaginable agent not discussed in class or in the readings, give a PEAS description of the task environment, and characterize it in terms of the properties listed in Section 2.3.2.
• [Week 1b response form]

• If you don't have a CS UNIX account (e.g. because you're a non-CS major), sign up for a temporary account IMMEDIATELY!

• Project 0: Unix/Python/Autograder tutorial

Search

• Textbook Chapter 3
• [Week 2 response form]

• Homework 1: Search, CSPs

• Project 1: Search in Pacman

Constraint Satisfaction Problems and Local Search

• Textbook Chapter 6
• Textbook Sections 4.1 and 4.2 (Rest of Chapter 4 is optional - we recommend skimming it at least)
• [Week 3 response form]

• Homework 2: CSPs, Games, Utilities

Adversarial Search, Utilities

• 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
• [Week 4 response form]

• Homework 3: MDPs

• Project 2: Multi-Agent Pacman

Markov Decision Processes

• Textbook Chapter 17 through the end of Section 17.3
• Sutton and Barto Chapters 3 and 4
• [Week 5 response form]

• Homework 4: MDPs & Bayes Nets

Reinforcement Learning

• Textbook Chapter 21
• Sutton and Barto Sections 6.1, 6.2, and 6.5
• [Week 6 response form]

• Homework 5: Bayes Nets & HMMs

• Project 3: Reinforcement Learning

Probability and Bayes Nets: Representation

• Textbook Chapter 14 through the end of Section 14.5
• [Week 7 response form]

• Homework 6: HMMs, Naive Bayes, Particle Filtering

Bayes Nets: Independence, Midterm

• [Survey form]

Bayes Nets: Inference and Sampling

• Project 4: Bayes Nets

(Hidden) Markov Models, Particle Filters

• 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
• [Week 10 response form]

• Capture the Flag Contest (optional)

Decision Networks and Naive Bayes

• 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
• [Week 11 response form]

• Homework 7: Perceptons

• Project 5: Ghostbusters

Perceptrons, Kernels, Clustering

• Section 18.6 except Sections 18.6.2 and 18.6.4
  • Sections 18.6.2 is optional (18.6.4 is assigned next week)
• Sections 18.8, 18.9, and 18.11.
  • Section 18.10 is optional.
• [Week 12 response form]

• Homework 8: Neural Network, Gradient Descent

• Project 6: Classification

Deep Learning

• Textbook Section 18.6.4
• Textbook Section 18.7
• [Week 13 response form]

Advanced Topics: Robotics

AI Applications and Conclusion

• Textbook Chapters 26 and 27
• Please include in your response if/how your thoughts about any of the questions considered in the chapters have changed since before you took the course.
• [Week 15 response form]

Final Exam