MA 326 - Mathematical Foundations of Data Science I

You can download the course information here.
More information can be found on the course Moodle page.

References:
No required textbook. Sources used will be provided for each topic covered.

Fall 2025 Tentative Course Schedule

Week 1

08/19 Lecture 1: Welcome and Course Overview; Intro to Python
08/21 Lecture 2: Intro to data science

Week 2

08/26 Lecture 3: Polynomial regression
08/28 Lecture 4: Review of multivariable calculus

Week 3

09/02 Lecture 5: Review of multivariable calculus II
09/04 Lecture 6: Review of multivariable calculus III

** Homework 1 due Thursday

Week 4

09/09 Lecture 7: Review of linear algebra I
09/11 Lecture 8: Review of linear algebra II

Week 5

09/16 Wellness Day. No Class.
09/18 Lecture 9: Generalized linear models

** Homework 2 due Friday

Week 6

09/23 Lecture 10: Regularizers
09/25 Lecture 11: Classification

Week 7

09/30 Lecture 12: Logistic regression
10/02 Lecture 13: Logistic regression II; Review

** Homework 3 due Friday

Week 8

10/07 Midterm Exam
10/09 Lecture 14: Review on probability

Week 9

10/14 Fall Break. No Class.
10/16 Lecture 15: Linear discriminant analysis

** Project Proposal due Thursday

Week 10

10/21 Lecture 16: Support vector machines
10/23 Lecture 17: Clustering methods: K-means

** Homework 4 due Thursday

Week 11

10/28 Lecture 18: Hierarchical clustering
10/30 Lecture 19: E-M Algorithm

Week 12

11/04 Lecture 20: Singular Value Decomposition
11/06 Lecture 21: Singular Value Decomposition II

** Homework 5 due Thursday

Week 13

11/11 Lecture 22: Dimension reduction: PCA
11/13 Lecture 23: Dimension reduction: MDS

Week 14

11/18 Lecture 24: MDS II
11/20 Lecture 25: Decision trees I

** Homework 6 due Friday

Week 15

11/25 Lecture 26: Decision trees II
11/27 Thanksgiving Holiday. No Class.

Week 16

12/02 Lecture 27: Review (Group Project due)

** 12/03: Reading Day