Schedule
This schedule is a draft, and it will be updated as the semester progresses.
My notes for each day are in Markdown format; you can open them in a text editor if you don’t have another program you prefer.
Tuesday, September 02
Before class: Read Chapter 1, 26, 27
Topics:
Class overview, mechanics, goals.
What is AI? History and Milestones. Weak vs. Strong AI.
Assignment 00 assigned, due 09-05.
Thursday, September 04
Before class: Read Chapter 2, 3.1–3.4
Topics:
Intelligent Agents.
Introduction to Search: Search problems. Toy and real-world examples. About search algorithms. Uninformed search strategies.
Assignment 01 assigned, due 09-12.
Tuesday, September 09
Before class: Read Chapter 3.4–3.6
Topics:
Search, continued: Informed search strategies. Heuristics.
Optional reading: http://www.redblobgames.com/pathfinding/a-star/introduction.html, http://theory.stanford.edu/~amitp/GameProgramming/
Thursday, September 11
Before class: Read Chapter 4.
Topics:
Local search: Hill climbing, simulated annealing. Beam search and genetic algorithms.
Local search in continuous spaces.
Assignment 02 assigned, due 09-19.
Optional reading:
Simulated Annealing and the TSP
Genetic Algorithms for Starcraft 2
Tuesday, September 16
Before class: Read Chapter 5.
Topics:
A simple hill climber.
Adversarial Search.
Optional reading: https://en.wikipedia.org/wiki/Negamax
Thursday, September 18
Topics:
Adversarial Search, continued.
Assignment 03 assigned, due 09-26 10-01.
Optional reading: Where the really hard problems are. [ACM DL] or [pdf] (the pdf is an OCRed scan)
Tuesday, September 23
Before class: Read Chapter 6.
Topics:
Constraint Satisfaction Problems.
Thursday, September 25
Before class: Read Chapter 13.
Topics:
Quantifying Uncertainty.
Assignment 04 assigned, due 10-10 10-17.
Optional reading: Think Bayes: Bayesian Statistics Made Simple provides an introduction to Bayesian methods using code (Python), rather than mathematical notation. The text is available for free online. Though not all the material is directly relevant to this course, you might find the first few chapters useful.
Exam 1 at 1900 in HAS 134.
Tuesday, September 30
Before class: Read Chapter 14.1 – 14.2
Topics:
Uncertainty, continued.
Bayesian Networks.
Thursday, October 02
Before class: Read Chapter 14.4
Topics:
Exact Inference in Bayesian Networks
Tuesday, October 07
Before class: Read Chapter 14.5
Topics:
Exam Results
A Java Minimax Solver for 3x3 Tic-Tac-Toe
Thursday, October 09
Topics:
CSP Review
Simple Backtracking Solver for Kakuro
Tuesday, October 14
No class meeting, Monday class schedule followed today.
Thursday, October 16
Class canceled. No class meeting.
Assignment 05 assigned, due 10-24.
Tuesday, October 21
Before class: Read Chapter 14.5
Topics:
Approximate Inference in Bayesian Networks
Assignment 06 assigned, due 11-05.
Thursday, October 23
Before class: Read Chapter 18.1, 18.2, 19.1 – 19.3; skim remainder of 19.
Topics:
Types of Inductive Learning. Supervised Learning. Simple Rule Learning. Evaluation Functions and Search.
Optional reading:
Machine Learning,
Supervised Learning
Tuesday, October 28
Topics:
Simple Rule Learning. Evaluation Functions and Search.
Optional reading:
Association Rules
Thursday, October 30
Before class: Read Chapter 20.1, 20.2.1, 20.2.2
Topics:
Classification as probability estimation. Naive Bayes classifiers.
Optional reading:
On the optimality of the Naive Bayes classifier
Exam 2 at 1900 in AEBN 119.
Tuesday, November 04
Election Day Remember to vote.
Before class: Read Chapter 18.3, 18.4
Topics:
Naive Bayes classifiers. Bias and Variance. Decision Trees.
Assignment 07 assigned, due 11-12.
Thursday, November 06
Topics:
Overfitting and other pathologies of learning algorithms.
Assignment 08 assigned, due 11-19.
Tuesday, November 11
Veteran’s Day, no class meeting.
Wednesday, November 12
Tuesday class schedule followed.
Before class: Read Chapter 20.2.6, 18.6, 14.3
Topics:
Learning with continuous variables. Kernel density estimation. Linear regression (and classification).
Thursday, November 13
Before class: Read Chapter 18.7
Topics:
Artificial neural networks.
Optional reading:
A gentle introduction to backpropagation [html] [pdf]
The Flaw Lurking In Every Deep Neural Net
So you wanna try deep learning?
Neural Networks, Manifolds, and Topology (thanks, David!)
Tuesday, November 18
Before class: Read Chapter 18.8, 18.10, 20.2.6, 20.3.1
Topics:
Non-parametric classification (k-nearest-neighbors). Ensemble learning. Unsupervised learning. K-means clustering. (A very brief introduction to) expectation-maximization.
Assignment 09 assigned, due 11-26.
Thursday, November 20
Before class: Read Chapter 15.1, 15.2.1, 15.3.
Topics:
Probabilistic reasoning over time. Markov models. Stationary distributions. Hidden Markov Models. Filtering.
Assignment 10 assigned, due 12-05.
Assignment 11 assigned, due 12-11.
Tuesday, November 25
Before class: Read Chapter 15.2.2, 15.2.3, 15.5
Topics: Filtering. The Forward Algorithm. MLE in HMMs.
Optional reading: The Wikipedia entry and associated graphic on Viterbi may be helpful. The Python code on that page may also be helpful if you understand Python.
Thursday, November 27
Thanksgiving break, no class meeting.
Tuesday, December 02
Topics:
Course evaluations. Viterbi. Approximate temporal inference.
Wednesday, December 03
Exam 3 at 1900 in AEBN 119.
Thursday, December 04
Topics:
Course wrap-up and review. Final exam review.
Thursday, December 11
Final Exam