MA 326 - Mathematical Foundations of Data Science

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

Spring 2024 Tentative Course Schedule

Week 1

01/09 Lecture 1: Welcome and Course Overview; Intro to Python; fundamentals of learning
01/11 Lecture 2: fundamentals of learning

Week 2

01/16 Lecture 3: Loss functions, model selection, goals of learning
01/18 Lecture 4: Linear regression

Week 3

01/23 Lecture 5: Linear Regression II
01/25 Lecture 6: K-means clustering

Week 4

01/30 Lecture 7: SVD, PCA, and dimensionality reduction
02/01 Lecture 8: PCA and low rank approximation

** Homework 1 due Monday

Week 5

02/06 Lecture 9: linear classification I
02/08 Lecture 10: linear classification II

** Homework 2 due Friday

Week 6

02/13 Wellness Day. No Class.
02/15 Lecture 11: Optimization I

Week 7

02/20 Lecture 12: Optimization II
02/22 Lecture 13: Optimization III - Convexity

** Homework 3 due Friday

Week 8

02/27 Midterm Exam
02/29 Lecture 14: Optimization IV - Newton’s method and gradient descent

Week 9

03/05 Lecture 15: Optimization V - Derivative free optimization
03/07 Lecture 16: Neural Networks I

** Project Proposal due Wednesday ** Homework 4 due Friday

Week 10

03/12 Spring Break; no class
03/15 Spring Break; no class

Week 11

03/19 Lecture 17: Neural Networks II
03/21 Lecture 18: Neural Networks III

Week 12

03/26 Lecture 19: Neural Networks IV
03/28 Lecture 20: Neural Networks V

Week 13

04/02 Lecture 21: Parameter Estimation & Sensitivity Analysis I
04/04 Lecture 22: Parameter Estimation & Sensitivity Analysis II

** Homework 5 due Friday

Week 14

04/09 Lecture 23: Topological Data Analysis
04/11 Lecture 24: Topological Data Analysis II

Week 15

04/16 Lecture 25: Machine learning Interpretability
04/18 Lecture 26: Machine learning Interpretability II

** Homework 6 due Friday

Week 16

04/23 Lecture 27: Review (Group Project due)

** 04/24: Reading Day