Graduate Artificial Intelligence

This course is targeted at graduate students who are interested in learning about artificial intelligence. The focus is on modern AI techniques. The course also covers techniques from the intersection of AI and other disciplines such as integer programming, continuous optimization, and game theory. The course content is profiled so as to not have too much overlap with narrower specialized AI courses offered at CMU.

No formal pre-requisites. But, substantial programming background is required (assignments will be in Python). Additional background in data structures and algorithms, linear algebra, and probability will all be helpful, but not required.

Name | Hours | Location | |
---|---|---|---|

Tuomas Sandholm | sandholm@cs | Mon. 12-1pm. Exception: none on 2/15. | GHC 9205 |

Zhaohan (Daniel) Guo | zguo@cs | Mon. 3-4pm. | GHC 8127 |

Christian Kroer | ckroer@cs | Tue. 1-2pm. Exception: 26/1: 3:30-4:30pm. | GHC 9221 |

J. Zico Kolter | zkolter@cs | Wed. 12-1pm. Exception: none on 2/17. | GHC 7115 |

Guillermo Cidre | gcidre@andrew | Thurs. 7-8pm. | GHC 5th Floor Common Area |

Wennie Tabib | wtabib@andrew | TBD | GHC 8129 |

Date | Topic | Readings | Due Dates | |
---|---|---|---|---|

1/11 | Introduction slides | RN Chapers 1 & 2 | ||

1/13 | Uninformed Search slides, Constraint Satisfaction slides | RN Chapters 3.1-3.4, begin reading Chapter 6 | ||

1/18 | MLK Day |
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1/20 | Constraint Satisfaction, SAT | RN Chapter 6 | ||

1/25 | Constraint Satisfaction, SAT | |||

1/27 | Informed Search slides | RN Chapters 3.5-3.7 | ||

2/1 | Linear Programming slides | |||

2/3 | Linear Programming slides | |||

2/8 | Integer Programming | |||

2/10 | Integer Programming | |||

2/15 | (Guest lecture) Willem-Jan van Hoeve: Binary Decision Diagrams (BDDs) in Search/MIP | |||

2/17 | (Guest lecture) Michael Trick: Benders' Decomposition in Search/MIP | |||

2/22 | Probabilistic Graph Models | |||

2/24 | Probabilistic Inference | |||

2/29 | Markov Decision Processes | |||

3/2 | Reinforcement Learning | |||

3/7 | Spring break |
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3/9 | Spring break |
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3/14 | Continuous Optimization | |||

3/16 | Machine Learning | |||

3/21 | Machine Learning | Project Proposal Due | ||

3/23 | Deep Learning | |||

3/28 | Midterm |
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3/30 | Deep Reinforcement Learning | |||

4/4 | Deep Learning Applications | |||

4/6 | Game Solving (Game Classes, Representations, and Solution Concepts) | |||

4/11 | Game Solving (Algorithms for Sequential Complete-Information Games) | |||

4/13 | Game Solving (Algorithms for Tree Games of Incomplete Information) | |||

4/18 | Game Solving (Algorithms for Tree Games of Incomplete Information) | |||

4/20 | Game Solving (Algorithms for Tree Games of Incomplete Information) | |||

4/25 | Opponent Modeling & Exploitation | |||

4/27 | Future Directions of AI and Q&A | |||

5/2 | Project Presentations |
Project Writeups Due 5/4 |

Topic | Files | Due Dates |
---|---|---|

Homework 1 | hw1.pdf (files: problems.py, sample.py) | 2/4 |

Homework 2 | hw2.pdf (files: hw2_handout.tar) | 2/16 |

Homework 3 | 2/25 | |

Homework 4 | 3/3 | |

Homework 5 | 3/17 | |

Homework 6 | 4/19 | |

Homework 7 | 4/28 |

The class has a midterm but no final exam. The midterm will take place during the regular class time on 3/28. A list of the material to be covered during the midterm will be posted here prior to the exam.