ML_2019
Labs for MATH 728 D
Machine Learning and Data Science
Spring 2019


ML_2019 is the home page for a set of MATLAB laboratory exercises associated with the class MATH 728 D: "Machine Learning and Data Science", taught in Spring 2019 by Professor Wolfgang Dahmen, of the Mathematics Department at the University of South Carolina. The labs were written and presented by John Burkardt.

The class meets for lectures on Tuesdays and Thursdays, from 1:15-2:30 pm in LeConte 121.

John Burkardt will be available in LeConte 401, on Mondays, from 1:10-2:00pm, for students who wish to work on any of the lab exercises, or who have questions about the assigned projects.


Note that Professor Dahmen is sponsoring a "Spring School" workshop, March 17th - 20th. The Math 728D class for March 18th will be cancelled. Students are urged to attend any of the Spring School lectures that attract them, as described on the web site: http://people.math.sc.edu/imi/dasiv/SpringSchool/.

Class information and lecture notes:

Homework exercises:
(Homework is intended as exercises for you to familiarize yourself with the course material. It will not be collected or graded. If you have questions about the exercises, these can be answered through email or at office hours.)


Project #1: Regression with Linear Least Squares (Due March 19)

Project #2: High Dimensional Sampling and Ranking (Due April 11)

Project #3: Perceptron, SVM, Multilinear Regression, Clustering (Due April 25)


LAB #1: MATLAB

LAB #2: Linear Algebra

LAB #3: Plotting

LAB #4: Probability

LAB #5: Optimization

LAB #6: Linear Regression

LAB #7: Multilinear Regression

LAB #8: Logistic Regression

LAB #9: Clustering

LAB #10: Gaussian Mixture Models

LAB #11: Principal Component Analysis

LAB #12: Naive Bayes Classification

LAB #13: Markov Methods

LAB #14: Facial Recognition

LAB #15: Vector and Matrix Norms

LAB #16: Curve Fitting

LAB #17: Projection

LAB #18: Expected Values


Last revised on 18 April 2019.