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: Linear regression
08/28 Lecture 4: Polynomial regression I

Week 3

09/02 Lecture 5: Polynomial regression II
09/04 Lecture 6: Review of multivariable calculus and linear algebra I

** Homework 1 due Thursday

Week 4

09/09 Lecture 7: Review of multivariable calculus and linear algebra II
09/11 Lecture 8: Probability and statistics: error analysis for regression I

Week 5

09/16 Wellness Day. No Class.
09/18 Lecture 9: Probability and statistics: error analysis for regression II

** Homework 2 due Friday

Week 6

09/23 Lecture 10: Data classification: linear methods
09/25 Lecture 11: linear methods (logistic)

Week 7

09/30 Lecture 12: LDA I
10/02 Lecture 13: LDA II; Review

** Homework 3 due Thursday

Week 8

10/07 Midterm Exam
10/09 Lecture 14: SVM

Week 9

10/14 Fall Break. No Class.
10/16 Lecture 15: Optimization

** Project Proposal due Thursday

Week 10

10/21 Lecture 16: Classification performance: hyperparameters, accuracy, training
10/23 Lecture 17: Classification performance II

** Homework 4 due Thursday

Week 11

10/28 Lecture 18: Decision trees I
10/30 Lecture 19: Decision trees II

Week 12

11/04 Lecture 20: Dimension reduction: PCA I
11/06 Lecture 21: PCA II

** Homework 5 due Thursday

Week 13

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

Week 14

11/18 Lecture 24: Clustering methods: K-means
11/20 Lecture 25: Hierarchical clustering

** Homework 6 due Friday

Week 15

11/25 Lecture 26: EM
11/27 Thanksgiving Holiday. No Class.

Week 16

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

** 12/03: Reading Day