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Tue & Thu at WEB 2230 03:40PM-05:00PM
Office | MEB 3466 |
zhe at cs dot utah dot edu | |
TA: | Su-wei Yang (u1429034 at utah.edu) |
Office Hours | Instructor: Tue/Thu, 12:30-1:30pm (MEB 3466) |
Su-wei Yang: Mon/Wed, 10:00-11:00am, Friday, 9:00-11:00am (MEB 3115) | |
Note: | We have office hours EVERY weekday! |
The course introduces basic knowledge of probabilistic modeling and learning. Topics cover fundamental concepts of Bayesian statistics, probabilistic graphical models, general linear models, approximate inference (e.g., variational inference, expectation propagation and Markov-Chain Monte-Carlo), Bayesian (deep) neural networks, Gaussian process regression, etc. Through this class, we expect that you will
1. understand the principles and paradigms of probabilistic learning,
2. be able to explore relevant literature, exploit existing and/or create new probabilistic modeling/learning tools for your own research or work interests,
3. be well prepared to dive into the cutting-edge research in probabilistic machine learning.
The grades are based on the following components:
The grades will NOT be curved.
Assignments must be electronically submitted through Canvas by midnight of the due date. Instructions about submission will be given in each assignment. Hand written versions or scans will not be accepted.
You are required to use MATLAB, Python and/or R for the programming portion of the assignments or projects. Other programming languages, however, are NOT accepted. Some programming tasks may require you to use TensorFlow or PyTorch. That means, you have to use Python for those tasks.
Please include a README.txt
file in your submission so that the TAs or the instructor can follow the instructions to test your code. Absence of the readme file will result in 0 grades for the submission.
All assignments should be submitted by the deadline. If the deadline is missed, the late submissions will have 10% penalty. In every subsequent 24 hours, the late submissions will loose another 10% credicts. For example, a 10 points assignment will have 2 points penalty, if it is submitted 30 hours late. However, if the assignment is not turned in until the other assignment have been graded and returned or 48 hours after the deadline, 0 grade will be given.