CS 5350/6350 Machine Learning, Fall 2022

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Tue & Thu  9:10AM-10:30AM, MEK 3550

Final Exam: Thursday, Dec 15, 2022, 08:00am – 10:00am, MEK 3550 Official Page

Instructor: Shandian Zhe

Office MEB 3466
Email zhe at cs dot utah dot edu
Teaching Assistant: Caleb Johnson (calebdeejohnson at gmail.com)
Teaching Mentee: Da Long (u1368737 at utah.edu)
Teaching Mentee: Tushar Gautam (tushar.gautam at utah.edu)
Note:We have office hours EVERY weekday!
Office Hours       Instructor: Tue & Thu 12:30pm - 1:50 pm        MEB 3466
Caleb Johnson: Wed 11am - 1pm & Fri 9am-10am   MEB 3515
Da Long: Mon 12pm - 2pm & Fri 8am-9am  MEB 3515
Tushar Gautam: Fri 10am-1pm  MEB 3485



This course introduces basic knowledge of machine learning. Topics consist of several fundamental, and widely successful supervised/unsupervised learning algorithms, such as decision trees, perceptrons, (deep) neural networks, kernel methods, support vector machines and Bayesian methods. Through this class, we hope that you will

1. understand machine learning ideas and paradigms,

2. be able to design identify or formulate appropriate machine learning problems for your research or applications,

3. be able to design machine learning models and/or use existing tools to implement learning algorithms. 


The grades are based on the following components:

The grades will be curved separately for undergraduate and graduate students. Extra problems may be included (merely) for graduate students.

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 highly suggested to use Python, MATLAB and/or R for the programming portion of the assignments or projects. However, you can choose any other programming language. But you should guarantee that your programs can be compiled and run on the  CADE machines; otherwise you will NOT receive credits from the programming parts.

  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.