Artificial Intelligence
Fall 2024
Administrivia
- Instructor: Nate Phillips
- Office hours: Tues/Thurs 3:15-4:15 PM & Wed 1-3 PM.
Also available by appointment and over Slack or Zoom. - Canvas page: Use for grades, online assignment submissions, and assignment solutions.
- Syllabus and additional policies.
Resources
- Textbooks and tutorials: Artificial Intelligence: A Modern Approach, 4th edition, by Russell and Norvig, Pearson, 2021. (You may also use the older 3rd edition.);
- Java in the browser: Repl.it, CodeHS;
- Official Java documentation
Calendar
- Thu, Aug 29
- Introduction, go over syllabus, agents.
- Paper 1 due Oct. 8
- Project 0 (50 point project) due Sep. 10
- Reading AIMA chapters 1 and 2
- Paper 1 due Oct. 8
- Tue, Sep 3
- State Space Search I
- Slides
- Terminology, problem setup
- Reading AIMA 3.1-3.3
- Slides
- Thu, Sep 5
- State Space Search II
- Uninformed search
- Example (Universal Cost Search)
- Reading AIMA 3.4
- Uninformed search
- Tue, Sep 10
- State Space Search III
- Informed search, A* algorithm
- Informed Search
- Example (A*)
- Project 1 due Sep. 24
- Homework 1 due Sep. 17
- Reading AIMA 3.5
- Informed search, A* algorithm
- Thu, Sep 12
- Adverserial Search I
- Minimax algorithm
- Minimax Slides
- Reading AIMA 5.1-5.2 (through minimax)
- Minimax algorithm
- Tue, Sep 17
- Adverserial Search II
- Alpha-beta pruning
- Create your own minimax with alpha-beta pruning example!
- Minimax with Alpha-Beta Pruning Example
- Reading AIMA 5.2 (alpha-beta pruning)
- Alpha-beta pruning
- Thu, Sep 19
- Adverserial Search III
- Alpha-beta with heuristics
- Homework 2 due Sep. 26
- Paper topic discussion on Sep. 24
- Reading AIMA 5.3
- Alpha-beta with heuristics
- Tue, Sep 24
- Probability I and Paper Topic Discussion
- Terms and definitions
- Project 2 due Oct. 8
- Reading AIMA 12.1-12.3
- Terms and definitions
- Thu, Sep 26
- Probability II
- Rules of probability
- Reading AIMA 12.4-12.5
- Rules of probability
- Tue, Oct 1
- Bayesian Networks I
- Homework 3 due Oct. 10
- Reading AIMA 13.1-13.2
- Homework 3 due Oct. 10
- Thu, Oct 3
- Bayesian Networks II
- Exact interference
- Reading AIMA 13.3
- Exact interference
- Tue, Oct 8
- Bayesian Networks III
- Project 3 due Oct. 24
- Thu, Oct 10
- Bayesian Networks, cont
- Tue, Oct 15
- Statistical Inference I
- Slides
- Paper 2 due Dec. 5
- Homework 4 due Nov. 5
- Reading (use slides)
- Slides
- Thu, Oct 17
- Statistical Inference II
- Slides
- Reading (use slides)
- Slides
- Tue, Oct 22
- No class, Fall break
- Thu, Oct 24
- Naive Bayes Classifiers and review
- Review Problems
- Review Solutions
- Slides
- Homework 1 Solutions
- Homework 2 Solutions
- Homework 3 Solutions
- Reading (use slides)
- Review Problems
- Tue, Oct 29
- Exam I
- Thu, Oct 31
- Markov Chains
- Happy Halloween!
- Slides
- Happy Halloween!
- Tue, Nov 5
- Hidden Markov Models I
- Project 4 due Nov. 19
- Thu, Nov 7
- Hidden Markov Models II
- Tue, Nov 12
- Hidden Markov Model Wrap-up & Reinforcement Learning I
- The Squirrel Problem
- Homework 5 due Nov. 14
- The Squirrel Problem
- Thu, Nov 14
- Reinforcement Learning II
- Reinforcement Learning Summary
- Tue, Nov 19
- Reinforcement Learning III
- Reinforcement Learning Slides
- Example Problems for Reinforcement Learning
- Solutions
- Reinforcement Learning Slides
- Thu, Nov 21
- Neural Networks I
- Neural net mini-project (50 point project) due Dec. 10
- Tue, Nov 26
- Neural Networks II
- Neural Network Playground
- Homework 6 due Dec. 7
- Neural Network Playground
- Thu, Nov 28
- No class, Thanksgiving break
- Tue, Dec 3
- Neural Networks III
- Neural Network Learning Slides
- Thu, Dec 5
- Wrap-up lecture, questions, and exam review
- Final Review
- Final Review Solutions
- Homework 5 Solutions
- Homework 6 Solutions
- Final Review
- Tue, Dec 10
- Final Exam Review
- Thu, Dec 12
- Reading Day
