Instructor Information

• Instructor: Kyu-Hwan Lee
• E-mail: kyu-hwan.lee@uconn.edu
• Office: Monteith 327
• Office Hours: TuTh 1:30 p.m. - 2:30 p.m. (virtual) or by appointment

Course Materials

• Textbook: Lecture notes and computer code will be posted. We will more or less follow Lectures on Machine Learning by Jeremy Teitelbaum.
• 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 codes 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%,   1st project --- 40%,   2nd project --- 40%
 
• Homework: Homework will be assigned through Google Classroom.
 
• 1st Project: The first project is a group project with each group consisting of, preferably, three students. You may choose any dataset you want and apply any machine-learning algorithms. You present your results in class and submit the notebook and dataset to the instructor. A good resource for datasets is Kaggle. Email the instructor about your plan for the first project by February 19th. Class presentations will take place during the week of March 4th.
 
• 2nd Project: The second project is also a group project. You choose any dataset and apply any dimensionality reduction algorithm. For example, you can apply PCA, LDA, t-SNE or UMAP. You present your results in class and submit the notebook and dataset to the instructor. It will be very important to correctly interpret your pictures during your presentation. Email the instructor about your plan for the second project by April 5th. Class presentations will begin on April 18th.

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/.