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! Make sure to read the Syllabus and join our Slack workspace. Note that the date of Midterm 1 has been moved; scroll down for the latest date.

Jump to the current week

Week 1: Introduction and Regression

Tue May 5

LEC 1 Introduction, Models, and Loss Functions

📝 In-Class Notes📺 Recording (Part 1)📺 Recording (Part 2)

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

The posted recordings are from last semester, though ignore the syllabus details discussed, as those are different this semester.

Wed May 6

Lab 1 Math Foundations and Environment Setup

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

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

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

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 Simple Linear Regression and Partial Derivatives

Ch. 3.1, Ch. 3.2, Ch. 3.3

HW 2 Empirical Risk and Simple Linear Regression

Ch. 3.1, Ch. 3.2, Ch. 3.3

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

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

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

Ch. 4.2, Ch. 4.3

HW 4 Projections and Spans

Ch. 4.2, Ch. 4.3

Thu May 21

LEC 6 Matrices, Exam Review

Ch. 5.1, Ch. 5.2

Fri May 22

EXAM Midterm 1 (1-3PM)

Note that this is the updated date/time.

Sun May 24

HW 5 Linear Independence and Subspaces

Week 4: Linear Transformations

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 Rank, Column Space, Null Space, and Inverses

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

HW 6 Matrices

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

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

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

Week 5: Regression and Optimization

Mon Jun 1

Lab 7 Inverses and Projections

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 Multiple Linear Regression; The Gradient Vector

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

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 8 Projections; Regression using Linear Algebra

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

Week 6: Midterm 2 and Eigenvalues

Mon Jun 8

Lab 9 Gradient Descent and Convexity

Tue Jun 9

EXAM Midterm 2 (1-3PM)

Thu Jun 11

LEC 11 Eigenvalues, Eigenvectors, and Diagonalization

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

Sun Jun 14

HW 9 Multiple Linear Regression, Gradients

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

Week 7: SVD and PCA

Mon Jun 15

Lab 10 Convexity, Eigenvalues and Eigenvectors

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 Adjacency Matrices and Diagonalization

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

HW 10 Eigenvalues and Eigenvectors

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

Thu Jun 18

LEC 13 Principal Components Analysis and Applications

Ch. 10.4

Week 8: Review and Final Exam

Sun Jun 21

HW 11 Singular Value Decomposition

Mon Jun 22

Lab 12 Singular Value Decomposition

Tue Jun 23

LEC 14 Review

Wed Jun 24

EXAM Final Exam (8-10AM)