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Lecture videos are available from the Canvas site (in the menu on the left) by selecting Echo 360.

To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:

export PATH=/usr/local/anaconda/bin:$PATH

This is a tentative schedule of CS440 topics for Fall, 2018. This will be updated during the summer and as the fall semester continues.

August

Week Topic Material Reading Assignments
Week 1:
Aug 20 - Aug 24
What is AI? Promises and fears.
Python review.
Problem-Solving Agents.
01 Introduction to AI
02 Introduction to Python
Chapters 1, 2, 3.1 of Russell and Norvig.
AI, People, and Society, by Eric Horvitz.
Automated Ethics, by Tom Chatfield.
The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016.
"Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots, by Julia Conley
Section 1 of Scipy Lecture Notes
Week 2:
Aug 27 - Aug 31
Problem-solving search and how to measure performance.
Iterative deepening and other uninformed search methods.
03 Problem-Solving Agents
04 Measuring Search Performance
05 Iterative Deepening and Other Uninformed Search Methods
06 Python Implementation of Iterative Deepening
Sections 3.1 - 3.4 of Russell and Norvig

September

Week Topic Material Reading Assignments
Week 3:
Sept 4 - Sept 7
No class on the Sept 3 (University Holiday) and Sept 5(instructor traveling). Sept 7 is optional. GTAs will answer assignment questions.
Informed search. A* search. Python classes, sorting, numpy arrays. Rest of Chapter 3 A1 Uninformed Search due Friday, Sept. 7, 10:00 PM. Submit your notebook in Canvas.
Here are good solutions from your classmates
Week 4:
Sept 10 - Sept 14
Informed search. A* search. Python classes, sorting, numpy arrays. A* optimality, admissible heuristics, effective branching factor.
Local search and optimization.
07 Informed Search
08 Python Classes
09 Heuristic Functions
10 Local Search
Chapter 4 A2 Iterative-Deepening Search due Friday, Sept. 14, 10:00 PM. Submit your notebook in Canvas.
Here are good solutions from your classmates
Week 5:
Sept 17 - Sept 21
Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. 11 Adversarial Search Chapter 5
Week 6:
Sept 24 - Sept 28
Negamax, with pruning. Introduction to Reinforcement Learning. 12 Negamax
13 Modern Game Playing
14 Introduction to Reinforcement Learning
Chapter 21
Reinforcement Learning: An Introduction
A3 A*, IDS, and Effective Branching Factor due Wednesday, Sept. 26, 10:00 PM. Submit your notebook in Canvas.

October

Week Topic Material Reading Assignments
Week 7:
Oct 1 - Oct 5
Reinforcement Learning for Two-Player Games. 14 Introduction to Reinforcement Learning
15 Reinforcement Learning for Two-Player Games
Chapter 21
Reinforcement Learning: An Introduction
Week 8:
Oct 8 - Oct 12
Introduction to Neural Networks 16 Introduction to Neural Networks
17 More Introduction to Neural Networks
Sections 18.6 and 18.7
Week 9:
Oct 15 - Oct 19
More Neural Networks. Autoencoders. 17 More Introduction to Neural Networks
22 Autoencoder Neural Networks
Week 10:
Oct 22 - Oct 26
Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. 18 Introduction to Classification A4 Reinforcement Learning Solution to Towers of Hanoi due Monday, Oct. 22, 10:00 PM. Submit your notebook in Canvas.

November

Week Topic Material Reading Assignments
Week 11:
Oct 29 - Nov 2
Classification with Neural Networks. Reinforcement Learning with Neural Networks. 19 Classification with Linear Logistic Regression
20 Classification with Nonlinear Logistic Regression Using Neural Networks
21 Reinforcement Learning with a Neural Network as the Q Function
Week 12:
Nov 5 - Nov 9
Introduction to Pytorch.
Constraint satisfaction.
Min-conflicts.
23 Introduction to Pytorch
24 Constraint Satisfaction Problems
25 Min-Conflicts in Python with Examples
Chapter 6
A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks
A5 Neural Networks due Monday, Nov. 5, 10:00 PM.
Project Proposal due Friday, November 9th, at 10:00 PM.
Week 13:
Nov 12 - Nov 16
Natural language understanding and translation. 26 Natural Language
27 Word Embeddings
Word2Vec and FastText Word Embedding with Gensim
Nov 19 - Nov 23 Fall Recess
Week 14:
Nov 26 - Nov 30
Faster Reinforcement Learning Slides for Faster Reinforcement Learning After Pretraining Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics by Anderson, Lee and Elliott A6 Min-Conflicts due Wednesday, Nov. 28, 10:00 PM.

December

Week Topic Material Reading Assignments
Week 15:
Dec 3 - Dec 7
Voluntary in-class project presentations. Dec 3:
Tom Cavey: Image Classification and Object Detection of Things Around CSU
Jason Stock: Classification of Data from the Sloan Digital Sky Survey
Marylou Nash: Physical Routing on ICs or PCBs with A*
Dec 5:
Jake Walker: Legal, Ethical, and Security Concerns for Autonomous Driving Technologies
Andy Dolan: Using Machine Learning Methods to Classify BGP Messages
Miles Wood: Using Q-Learning to Learn to Play Chad, a Chess Variant
Apoorv Pandey: Using Q-Learning to Learn to Play 2×2 Dots and Boxes
Dec 7:
Markus Dabell: Classification of Handwritten Digits from the MNIST Dataset
Sajeeb Roy Chowdhury: Searching for Optimal Schreier Trees in the Field of Combinatorics
Mike Hamilton: The Amazon AWS DeepRacer Platform for Reinforcement Learning Experimentation
Final Exam Week:
Dec 10 - Dec 14
Final Project notebook is due Tuesday, Dec 11th, 10:00 pm. Here is an notebook explaining what is expected for your final report.
start.txt · Last modified: 2018/12/03 11:22 by anderson