Mathematics for Machine Learning 🧠

EECS 245, Spring 2026 🌸 at the University of Michigan

Suraj Rampure he/him

rampure@umich.edu

Lecture: TuTh 1-4PM, 1690 BBB

Office Hours: Before/after lecture and by appointment

Welcome to EECS 245! Our first lecture is on Tuesday, May 5th at 1PM in 1690 BBB. Make sure to read the Syllabus and join our Slack workspace.

Jump to the current week

Week 1: Introduction and Regression

Tue May 5

LEC 1 Introduction, Models, and Loss Functions

Advice, Ch. 1.1, Ch. 1.2, Ch. 1.3

Wed May 6

Lab 1 Math Foundations and Environment Setup

Thu May 7

LEC 2 Empirical Risk Minimization and Simple Linear Regression

Ch. 1.3, Ch. 1.4, Ch. 2.1, Ch. 2.2, Ch. 2.3, Ch. 2.4

Sun May 10

SUR Welcome Survey

Ch. 1.3, Ch. 1.4, Ch. 2.1, Ch. 2.2, Ch. 2.3, Ch. 2.4

HW 1 Means, Sums, and Calculus

Week 2: Vectors and Linear Independence

Mon May 11

Lab 2 Empirical Risk and Simple Linear Regression

Tue May 12

LEC 3 Vectors, Orthogonality, and the Dot Product

Ch. 3.1, Ch. 3.2, Ch. 3.3

Wed May 13

Lab 3 Projections, Span, and Linear Independence

HW 2 Empirical Risk and Simple Linear Regression

Thu May 14

LEC 4 Projections, Span, and Linear Independence

Ch. 3.4, Ch. 4.1, Ch. 4.2, Ch. 4.4

Sun May 17

HW 3 Vectors and the Dot Product

Week 3: Vector Spaces and Matrices

Mon May 18

Lab 4 Projections, Span, and Linear Independence

Tue May 19

LEC 5 Vector Spaces, Subspaces, Bases, and Dimension

Ch. 4.2, Ch. 4.3

Wed May 20

Lab 5 Vector Spaces, Subspaces, Bases, and Dimension

HW 4 Projections and Spans

Thu May 21

LEC 6 Matrices

Ch. 5.1, Ch. 5.2

Sun May 24

HW 5 Linear Independence and Subspaces

Week 4: Midterm 1 and Linear Transformations

Mon May 25

EXAM Midterm 1 (12-2PM)

Tue May 26

LEC 7 Rank, Column Space, Null Space, and Inverses

Ch. 5.3, Ch. 5.4, Ch. 6.2, Ch. 6.4

Wed May 27

Lab 6 Vector Spaces, Subspaces, and Bases

HW 6 Matrices

Thu May 28

LEC 8 Linear Transformations, Inverses, and Projections

Ch. 6.1, Ch. 6.2, Ch. 6.3, Ch. 6.4

Sun May 31

HW 7 Rank and Inverses

Week 5: Regression and Optimization

Mon Jun 1

Lab 7 Rank, Column Space, Null Space, and Inverses

Tue Jun 2

LEC 9 Multiple Linear Regression and the Gradient Vector

Ch. 7.1, Ch. 7.2, Ch. 8.1, Ch. 8.2

Wed Jun 3

Lab 8 Inverses and Projections

HW 8 Projections; Regression using Linear Algebra

Thu Jun 4

LEC 10 Gradient Descent and Convexity

Ch. 8.3, Ch. 8.4, Ch. 8.5, Ch. 8.6

Sun Jun 7

HW 9 Multiple Linear Regression, Gradients

Week 6: Midterm 2 and Eigenvalues

Mon Jun 8

Lab 9 Multiple Linear Regression; The Gradient Vector

Tue Jun 9

EXAM Midterm 2 (1-3PM)

Wed Jun 10

HW 10 Eigenvalues and Eigenvectors

Thu Jun 11

LEC 11 Eigenvalues, Eigenvectors, and Diagonalization

Ch. 9.1, Ch. 9.2, Ch. 9.3, Ch. 9.4

Week 7: SVD and PCA

Mon Jun 15

Lab 10 Gradient Descent and Convexity

Tue Jun 16

LEC 12 Spectral Theorem, Singular Value Decomposition

Ch. 9.5, Ch. 10.1, Ch. 10.2, Ch. 10.3

Wed Jun 17

Lab 11 Convexity, Eigenvalues and Eigenvectors

HW 11 Singular Value Decomposition

Thu Jun 18

LEC 13 Principal Components Analysis and Applications

Ch. 10.4

Week 8: Review and Final Exam

Mon Jun 22

Lab 12 Adjacency Matrices and Diagonalization

Tue Jun 23

LEC 14 Review

Wed Jun 24

EXAM Final Exam (8-10AM)