This course provides an overview of modern statistical AI, including probability and statistics, graphical models, and machine learning. The course also includes applications to computer vision, natural language processing, information retrieval, and speech processing.

Details

- Instructor: Alan Ritter (ritter.1492@osu.edu)
- Time: Tuesday, Thursday 11:10 - 12:30
- Place: Dreese Lab 305
- Office Hours: Wednesdays 3:00-4:00pm, Dreese 595

Topics:

- Background in probability and statistics
- Basics of Bayesian and Markov Networks
- Inference in Graphical Models
- Linear Regression
- Classification Algorithms
- Clustering
- Computer Vision Applications
- Information Retrieval and NLP applications
- Speech Applications
- Other topics, time permitting

Textbook:

There is no official textbook, however, the following books are relevant to the material.
The Russell and Norvig book is the one traditionally used for the class.

- Stuart Russell and Peter Norvig.
*Artificial Intelligence: A Modern Approach (3rd Edition)*, Prentice Hall, 2009. - Daphne Koller and Nir Friedman.
*Probabilistic Graphical Models: Principles and Techniques*, MIT Press, 2009. - Murphy, Kevin P.
*Machine learning: a probabilistic perspective*, MIT press, 2012. - Christopher M. Bishop.
*Pattern Recognition and Machine Learning*, Springer, 2006.

Grading

Grading will be based on:

Homeworks

Schedule

Date | Topic | Reading |
---|---|---|

1/13 | Course Overview | No Reading |

1/15 | Probability Theory Review | (R+N Ch 13, K+F Ch 2) |

1/20 | Statistical Estimation | (K+F Ch 17, R+N Ch 20, Notes on Parameter Estimation (Sections 1-3 are relevant)) |

1/22 | Mixture Models and the EM Algorithm | (R+N Chapter 20, Murphy Ch 11, K+F Chapter 19) |

1/27 | Hidden Markov Models (PDF) | (R+N Chapter 15, Murphy Ch 17, Notes on HMMs) |

1/29 | No Class (AAAI) | |

2/10 | Bayesian Networks (PDF) | (R+N Chapter 14, Murphy Chapter 10, K+F Chapter 3) |

2/12 | Markov Networks (PDF) | (Murphy Chapter 19, K+F Chapter 4) |

2/16 | Exact Inference (PDF) | (Murphy Chapter 20, R+N Chapter 14 |

2/16 | Junction Trees (PDF) | (Murphy Chapter 20, R+N Chapter 14 |

3/3 | Midterm Review | |

3/10 | Sampling-Based Inference | R+N Chapter 14, K+F Chapter 11 |

3/12 | Learning Bayesian Networks | Murphy Chapter 10+26, K+F Chapters 17+18 |

3/24 | Learning Markov Networks | Murphy Chapter 19, K+F Chapter 20 |

3/26 | Learning Markov Networks | Murphy Chapter 19, K+F Chapter 20 |

3/31 | Decision Theory | R+N Chapter 16, K+F Chapter 22 |

4/2 | Markov Decision Processes | R+N Chapter 17 and 21, K+F Chapter 23 |

4/7 | No Class | |

4/9 | Speech and Language Processing | Russel + Norvig Chapters 22+23 |

4/14 | Speech and Language Processing | |

4/16 | Guest Lectures | Orton Hall 110 |

4/21 | Guest Lectures | Schoenbaum Hall 330 |

4/23 | Guest Lectures | Orton Hall 110 |

5/4 | Final (10-11:45am) | Final Review |