The following is a schedule of the lectures and presentations for CS 4013/5013 Artificial Intelligence for Spring 2018. This schedule will be updated throughout the semester. All readings are from Artificial Intelligence by Russell & Norvig third edition (blue book) unless otherwise noted.
| Date | Topic | Assigned Reading | Assigned | Due |
|---|---|---|---|---|
| Jan 15 (Week 1: Intro and Agent Design) | Martin Luther King day - holiday | |||
| Jan 17 | Introduction to AI & intelligent agents |
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| Jan 22 (Week 2: Problem formulation) | Search and problem formulation, Agents and environments | Ch 2 through 2.3, 3.1 | Project 0: setting up your account | |
| Jan 24 | Problem formulation, Effective knowledge representations |
3.2-3.3 | ||
| Jan 29 (Week 3: Search) | Uninformed search (BFS, DFS, etc) | 3.4 | Project 1: Reflex agents and knowledge representations | Project 0 |
| Jan 31 | Informed search, Heuristic search, A* |
3.5 | Homework 1: problem formulation | |
| Feb 5 (Week 4: Search) | A*, Constructing admissible heuristics |
3.6 | ||
| Feb 7 | Local search and search in the real world, Adversarial search | 4.1-4.5, 5.1-5.2 |
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| Feb 12 (Week 5: Adversaries) | Adversarial search, Alpha-beta pruning | 5.3 | Project 2: Intelligent search | Project 1 |
| Feb 14 | Project 1 demos | Homework 2: Uninformed and informed search | Homework 1 | |
| Feb 19 (Week 6: Adversaries) | Alpha-beta pruning, Large scale games, Advanced topic: Multi-Carlo Tree Search, Upper Confidence Bounds/UCT search | 5.4-5.5, Introduction to UCT and Introduction to MCTS | ||
| Feb 21 | No class: SNOW/ICE DAY | |||
| Feb 26 (Week 7:MCTS, Learning) | Advanced topic: Multi-Carlo Tree Search, Upper Confidence Bounds/UCT search | Homework 2 | ||
| Feb 28 | Introduction to learning, Clustering | 18.1 | Homework 3: Adversarial search and MCTS | Project 2 |
| Mar 5 (Week 8: Learning) | Project 2 demos, Clustering, Nearest neighbor, Kernel regression | 18.6, 18.8 | ||
| Mar 7 | Linear regression | |||
| Mar 12 (Week 9: Learning) | Evolutionary Computation/Genetic Algorithms | 4.1.4 | Project 3: Learning | |
| Mar 14 | Decision trees, Regression trees | 18.3 | Homework 3 | |
| Mar 17-25 | Spring Break! |
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| Mar 26 (Week 10: Learning) | Decision trees |
Homework 4: Regression and clustering and nearest neighbor | ||
| Mar 28 | Evaluating learning models, Overfitting. Maybe: Random Forests, Gradient boosted forests
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| Apr 2 (Week 11: CSPs) | Constraint satisfaction problems |
6.1-6.5 | ||
| Apr 4 | Constraint satisfaction problems |
Project 3 | ||
| Apr 9 (Week 12: Planning) | Project 3 demos, Introduction to logic, Introduction to classical planning, | Ch 7, 8, 12, 10 | Project 4: Planning, Multi-agent systems and tying it all together Homework 5: Trees and CSPs |
Homework 4 |
| Apr 11 | Classical planning: STRIPS and derived representations | 12, 10.1 |
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| Apr 16 (Week 13: Multi-agent systems and Planning) | STRIPS and PDDL examples, Forward and backwards search, |
10 | ||
| Apr 18 | Planning and acting in the real-world | 11 | Homework 6: Planning | Homework 5 |
| Apr 23 (Week 14: Multi-agent systems and Planning) | Introduction to multi-agent systems | 11.4 | ||
| Apr 25 | Advanced topics, Tying it all together | Alpha Go Zero paper on canvas | ||
| Apr 30 (Week 15: Multi-agent systems and Planning) | Help session for final projects |
Homework 6 | ||
| May 2 (Course wrapup) | Project 4 demos (drones and spacesettlers) | Project 4 demos | ||
| May 10 | Final project writeups due 4:30-6:30pm |
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