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. or by appointment

Course Materials

• Textbook: There is no textbook. Instead, lecture notes and computer codes will be posted.
• Platform: We will use Google Classroom and Colaboratory for coding environment. A class code for access will be emailed to you at the beginning of the semester. Students are encouraged to install necessary software 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

• Goals
Learn the mathematics of linear regression using linear algebra
• Lab
get a working installation of anaconda, python, and jupyter on your computer. a basic introduction to working with the Jupyter notebook fundamentals of Python calculations and plotting examples of linear regression
• References
Linear Regression html pdf Linear Regression lab - includes datafiles and ipynb file. zip tgz See also the Lab Resources page for help.

3. Gradient Descent

• Goals
Learn the basic theory of gradient descent and how it is applied to find maxima and minima of functions
• Lab
• References
Gradient Descent lab - zip

4. Probability

• Goals
Get an introduction to the key ideas from probability that play a role in machine learning; Learn about mean, variance, independence, conditional probability, and Bayes theorem; Try out the Naive Bayes classification method; Introduce the ideas of statistical models and maximum likelihood.
• Lab
• References
Probability Notes html pdf Naive Bayes Notes html pdf Naive Bayes Lab - includes datafiles and ipynb file. zip tgz

5. Logistic Regression

• Goals
Understand the statistical model underlying logistic regression; See how the ideas of likelihood and gradient descent combine to solve the logistic regression problem; Do some sample computations to see Logistic Regression in action; Generalize binary logistic regression to multi-class logistic regression Logistic Regression.
• Lab
• References
Logistic Regression lab - zip

6. Principal Component Analysis

• Goals
Learn about how the covariance matrix encodes variation in linear combinations of features; Learn the correlation coefficient; Understand principal components and their relationship to eigenvectors of the covariance matrix; See how to use principal components for dimension reduction; Do some examples.
• Lab
• References
Principal Component Analysis html pdf PCA Lab – includes notebook(s) and data zip tgz

7. Bayesian Regression

• Goals
Learn the process of Bayesian inference (see also the notes on probability above). Intro to Bayesian Inference Bayesian Coin Flipping Understand over-fitting in linear regression Study Bayesian linear regression Understand the ideas of linear discriminant analysis Bayesian Linear Regression Linear Discriminant Analysis Dimensionality Reduction via LDA
• Lab
• References
Bayesian Regression Lab – includes notebook(s) and data zip tgz

8. Support Vector Machines

• Goals
Learn the ideas behind support vector machine classifiers Introduction Optimal Margins Understand the relationship between convex hulls, supporting hyperplanes, and support vector machines Convexity, Convex Hulls, and Supporting Hyperplanes Formulate the convex optimization problem yielding the optimal margin classifier Closest Points and Optimal Margins 1 Closest Points and Optimal Margins 2 Formulating the Optimization Problem Learn the sequential minimum optimization algorithm The SMO Algorithm Further ideas Kernels, inseparable sets, multiclass classification
• Lab
• References
Notes on support vector machines html pdf Support Vector Machines Lab (includes jupyter notebook and data files) zip tgz

9. Neural Networks

• Goals
Learn the ideas of neural networks and understand forward-propagation and back-propagation Derive the back-propagation formula Learn the mechanisms of basic convolutional neural networks Basic Neural Networks Convolutional Neural Networks
• Lab
• References
Neural Networks Lab – includes notebook(s) and data zip tgz

Course Requirements and Grading

• Grading Scheme: 1st project --- 50%,   2nd project --- 50%

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/.