CS 780/880: Probabilistic AI and Machine Learning

Who: Professor Amato
When: Tuesday, Thursday  9:40am - 11:00am
Where: Kingsbury N113



In this class, students will learn about important areas of artificial intelligence and machine learning. These areas include inference, graphical models, and planning under uncertainty as well as unsupervised, supervised and reinforcement learning. Coursework includes a mix of problem sets to understand the theory behind these approaches and implementation and experimental analysis of different methods. Through these assignments and the course project, students will learn how the different methods work and be able to apply them on a range of realistic data sets.

Prerequisites: Previous exposure to discrete math, probability and statistics is very helpful

TA: Sammie Katt (sk1059 at wildcats.unh.edu)

Piazza (please check for announcements and to post questions)

Required Texts

Decision Making Under Uncertainty: Theory and Application
Machine Learning: A Probabilistic Perspective

Optional Text

Artificial Intelligence: A Modern Approach (3rd Edition)


Homeworks (written and coding): 60%
Midterm: 20%
Final project (presentation and paper): 20%

All work must be typed and handed in before class on Tuesdays.
No credit for late work and all work must be done by individual students!

Office hours

Chris: 1-2pm on Wednesday in N215C
Sam: ? on Friday

If you can't make these office hours, feel free to email one of us to meet another time.