CS 6190 Probabilistic Machine Learning, Spring 2022

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Schedule

Tue & Thu at WEB 2230 03:40PM-05:00PM

Instructor: Shandian Zhe

Office MEB 3466
Email zhe at cs dot utah dot edu
TM: Shikai Fang (u1265561 at utah dot edu)
TA: Zhimeng Pan (z.pan at utah dot edu)
Office Hours       Instructor: Wed, 2-3pm (Zoom)
Shikai Fang: Mon&Fri, 4-5pm (Zoom)
Zhimeng Pan: Tue&Thu, 1-2pm (Zoom)
Note:We have office hours EVERY weekday!

Syllabus

Overview

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.

Grading

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.

Instructions for programming assignments

  1. 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.

  2. 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.

Late policy

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.