Course: Introduction to Artificial Intelligence

COSC 4550 / COSC 5550
M/W/F 1:10-2:00pm
Room: Engineering 3076

Professor Nick Cheney
Office hours: M 2-3pm, F 12-1pm (Engineering 4081B)

TA: Joost Huizinga
TA office hours: Tu 2-5pm, Th 1:30-3:30pm (Engineering 4086)

Syllabus (pdf)

Assigned Readings

Assigned readings should be completed before the class to which they are listed. Having a primer on the topics before going into the lectures will allow you to be more engaged with the subtler points in lectures and be able to ask more questions about the parts of the readings that you did not fully grasp. You are fully capable of reading the material in the textbook on your own -- so the lectures are not simply a time for me to show you that material again, but rather are a time for me to compliment the readings you do with real time help and feedback for your questions, to emphasize the parts of the readings that I find most relevant to our class, and to solidify the concepts in the readings with additional examples and hands-on exercises. In this spirit, all of the material from the readings will NOT be covered in the lectures. You are still responsible for the material not covered in class (e.g. for exams)!
(Note: "RN 1-1.5" translates to: In the Russell and Norvig textbook, please read from the start of chapter 1 to the end of section 1.5)

Day Date Topic Assigned Reading Assignments Slides
W 8/30 Course Logistics and Motivation -- pdf
F 9/1 What is AI? Brief History of AI RN 1-1.5 (30 pages) pdf
M 9/4 NO CLASS: Labor Day -- -- --
W 9/6 The Structure of Intelligent Agents RN 2-2.5 (25 pages) pdf
F 9/8 Problem Solving Agents and RN 3-3.4.2 (21 pages) pdf
M 9/11 Problem Solving with Basic Search Strategies RN 3.4.3-3.7 (25 pages) AIC 1 Due 9/10 pdf
W 9/13 Heuristic Search, Local Search and Optimization Algorithms RN 4-4.1 (9 pages) pdf
F 9/15 Optimization Algorithms and Search in Continuous Spaces RN 4.2 (4 pages) pdf
M 9/18 Optimal Decisions in Game Playing RN 5-5.3 (10 pages) AIC 2 Due 9/17 pdf
W 9/20 Refresher on Probabilty (guest lecture) RN 13-13.4 (15 pages) pdf
F 9/22 Evaluation Functions and Games of Chance RN 5.4-5.9 (not 5.6.1) (18 pages)
M 9/25 Bayes Rule RN 13.5-13.7 (8 pages) AIC 3 Due 9/24
W 9/27 The Semantics of Bayesian Networks RN 14-14.3 (12 pages)
F 9/29 Exact Inference in Bayesian Networks RN 14.4 (8 pages)
M 10/2 Approximate Inference in Bayesian Networks RN 14.5 (9 pages)
W 10/4 Relational Probability Models RN 14.6-14.8 (13 pages)
F 10/6 Probabilistic Reasoning Over Time RN 15-15.2 (12 pages)
M 10/9 Hidden Markov Models RN 15.3 (5 pages) AIC 4 Due 10/8
W 10/11 Kalman Filters RN 15.4 (6 pages)
F 10/13 Dynamic Bayesian Networks RN 15.5-15.7 (13 pages)
M 10/16 Value Iteration in Sequential Decision Making RN 17-17.2 (11 pages)
W 10/18 Policy Iteration and POMDPs RN 17.3-17.4 (10 pages)
F 10/20 Decision Trees RN 18-18.3 (14 pages)
M 10/23 Machine Learning Theory RN 18.4-18.5 (9 pages) AIC 5 Due 10/22
W 10/25 Regression and Classification RN 18.6 (10 pages) Project Outline Due
F 10/27 Artificial Neural Networks RN 18.7-18.7.3 (5 pages)
M 10/30 NO CLASS: Advising Week -- -- --
W 11/1 NO CLASS: Advising Week -- -- --
F 11/3 NO CLASS: Advising Week -- -- --
M 11/6 Learning in Multilayer Networks RN 18.7.4-18.7.5 (4 pages)
W 11/8 Non-Parametric Models RN 18.8 (7 pages)
F 11/10 Support Vector Machines RN 18.9-18.12 (14 pages)
M 11/13 Unsupervised Learning and Clustering papers
W 11/15 Catch Up and Exam Review --
F 11/17 Exam Day! --
M 11/20 Machine Learning Examples in Python -- -- --
W 11/22 NO CLASS: Thanksgiving Break -- -- --
F 11/24 NO CLASS: Thanksgiving Break -- -- --
M 11/27 Deep Learning for Image Classification papers
W 11/29 Generative Adversarial Networks papers
F 12/1 Deep Reinforcement Learning papers
M 12/4 Embodied Intelligence papers
W 12/6 The Ethics of Artificial Intelligence RN 26-26.4 (20 pages)
F 12/8 Final Project Presentations -- Project Video Due
M 12/11 Final Project Presentations --
M 12/18 Final Project Presentations Time: 1:15-3:15pm

AI Challenges (AIC)

The AI Challenges are hands-on opportunities to demonstrate your knowledge of the concepts covered in class as well as your ability to think creatively and code-up effective and cool programs! The assigned algorithm/agent implementations are meant to test your grasp of these concepts, and thus are to be done independently. This means to code sharing between students, and no usage of existing AI or machine learning packages -- though imports of basic data structures (e.g. NumPy arrays) are completely acceptable.
(Note: all assignments are due by 11:59pm on the day listed below)

Day Due Date Assignment Topics Covered Link
M 9/11 AI Challenge #1 Building Your First Intelligent Agent zip
Su 9/17 AI Challenge #2 Basic Search Strategies zip
Su 9/24 AI Challenge #3 Advanced Search Strategies zip
Su 10/8 AI Challenge #4 Game Playing Agents
Su 10/22 AI Challenge #5 Optimal Behavior in Uncertain Environments