Math 3180: Mathematics for Machine Learning

Spring 2025



Instructor Information
  • Instructor: Kyu-Hwan Lee
  • E-mail: kyu-hwan.lee@uconn.edu
  • Office: Monteith 327
  • Office Hours: TuTh 11:00 - 12:00

Course Materials
  • Textbook: Lecture notes and computer code will be posted.
  • Platform: We will use Google Classroom and Colaboratory for coding environment. An access code will be provided at the beginning of the semester. It is optional for students to install necessary software and run code on their personal computers.
  • References:
    • James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning (with Applications in R).
    • Bass, Alonso-Ruiz, Baudoin, et. al. UConn’s Open Undergraduate Probability Text.
    • Boyd, S. and Vandenberghe, L. Introduction to Applied Linear Algebra.
    • Bishop, C. Pattern Recognition and Machine Learning.

Course Description
The interdisciplinary field known as Machine Learning or Data Science draws together techniques from computer science, mathematics, and statistics to extract meaning from data. In this course, we will discuss some of the essential mathematical ideas in this field. While our focus will be on the role of Calculus, Probability, and Linear Algebra, we will introduce computational techniques using Python and the Jupyter notebook environment, and some ideas from statistics, in order to closely link theory and practice.


Course Outline
  1. Python Basics and Jupyter Notebook
  2. Linear Regression
  3. Binary Classification and Gradient Descent
  4. Probability and Naive Bayes
  5. Logistic Regression
  6. Principal Component Analysis
  7. Bayesian Regression and Linear Discriminant Analysis
  8. Support Vector Machines
  9. Neural Networks

Course Requirements and Grading
  • Grading Scheme: Homework --- 20%,   Quizzes --- 20%,   Project --- 20%,   1st exam --- 20%,   2nd exam --- 20%
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  • Homework: Homework will be assigned through Google Classroom.
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  • Quizzes: A quiz will be given every Tuesday from the second week.
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  • Project: You may work on a project either individually or in a group of up to three students. You are free to choose any dataset and apply any machine learning algorithms. The project requires a 10-15 minute in-class presentation of your results, along with submitting the notebook and dataset to the instructor. A great resource for datasets is Kaggle. Class presentations will begin on March 4th, with two groups presenting per class, and will continue until every student has participated. Please email the instructor with your project plan and preferred presentation date as soon as you are ready. (Dates will be assigned on a first-come, first-served basis.)
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  • Exams: Exams will be take-home, available starting at 8:00 AM or earlier, and closing at 11:59 PM. They will be distributed through Google Classroom. The first exam will be on March 6th, and the second on May 1st. There will be no class meetings on exam days.

Student Responsibilities and Resources
As a member of the University of Connecticut student community, you are held to certain standards and academic policies. In addition, there are numerous resources available to help you succeed in your academic work. Review these important standards, policies and resources.

Students with Disabilities
The University of Connecticut is committed to protecting the rights of individuals with disabilities and assuring that the learning environment is accessible. If you anticipate or experience physical or academic barriers based on disability or pregnancy, please let me know immediately so that we can discuss options. Students who require accommodations should contact the Center for Students with Disabilities, Wilbur Cross Building Room 204, (860) 486-2020 or https://csd.uconn.edu/.