**COSC 4550 / COSC 5550**

M/W/F 1:10-2:00pm

Room: Engineering 3076

Professor Nick Cheney

ncheney@uwyo.edu

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

TA: Joost Huizinga

jhuizing@uwyo.edu

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

Syllabus (pdf)

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

(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 | -- | ||

F | 9/1 | What is AI? Brief History of AI | RN 1-1.5 (30 pages) | ||

M | 9/4 | NO CLASS: Labor Day | -- | -- | -- |

W | 9/6 | The Structure of Intelligent Agents | RN 2-2.5 (25 pages) | ||

F | 9/8 | Problem Solving Agents and | RN 3-3.4.2 (21 pages) | ||

M | 9/11 | Problem Solving with Basic Search Strategies | RN 3.4.3-3.7 (25 pages) | AIC 1 Due 9/10 | |

W | 9/13 | Heuristic Search, Local Search and Optimization Algorithms | RN 4-4.1 (9 pages) | ||

F | 9/15 | Optimization Algorithms and Search in Continuous Spaces | RN 4.2 (4 pages) | ||

M | 9/18 | Optimal Decisions in Game Playing | RN 5-5.3 (10 pages) | AIC 2 Due 9/17 | |

W | 9/20 | Refresher on Probabilty (guest lecture) | RN 13-13.4 (15 pages) | ||

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 |

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 |