CS 780/880: Probabilistic AI and Machine Learning
Who: Professor Amato
When: Tuesday, Thursday 9:40am - 11:00am
Where: Kingsbury N113
Recitation:
Overview:
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)
Syllabus
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)
Evaluation
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.