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

The Final Exam is on Wednesday, June 24th from 8-10AM in 1018 DOW. See logistics here.

Fill out both the End-of-Semester Survey and Official Evaluations by Tuesday, June 23rd for 1% of extra credit to your overall grade.

Jump to the current week

Week 1: Introduction and Regression

Tue May 5

LEC 1 Introduction, Models, and Loss Functions

📝 In-Class Notes📺 Recording📺 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

📝 In-Class Notes📺 Recording

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 and the Dot Product

📝 In-Class Notes📺 Recording

Ch. 3.1, Ch. 3.2, Ch. 3.3

Wed May 13

Lab 3 Vectors and the Dot Product

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

📝 In-Class Notes📺 Recording

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; Midterm 1

Mon May 18

Lab 4 Projections, Span, and Linear Independence

Tue May 19

LEC 5 Vector Spaces and Subspaces

📝 In-Class Notes📺 Recording

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, Span, and Linear Independence

Ch. 4.2, Ch. 4.3

Thu May 21

LEC 6 Matrices, Exam Review

📝 In-Class Notes📺 Recording

Ch. 5.1, Ch. 5.2

Fri May 22

EXAM Midterm 1 (1-3PM, 1690 BBB; see logistics here)

🧠 Problems

Week 4: Linear Transformations

Tue May 26

LEC 7 Rank, Column Space, Null Space, and Rank-Nullity

📝 In-Class Notes📺 Recording⏯️ Videos

Ch. 5.2, Ch. 5.3, Ch. 5.4, Ch. 6.2

Watch the supplemental videos!

Wed May 27

Lab 6 Rank, Column Space, Null Space, and Inverses

Ch. 5.2, Ch. 5.3, Ch. 5.4, Ch. 6.2

Thu May 28

LEC 8 Linear Transformations, Inverses, and Projections

📝 In-Class Notes📺 Recording⏯️ Videos: Determinants and Invertibility⏯️ Videos: Projections

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

Watch the supplemental videos!

HW 5 Matrices

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

Sun May 31

HW 6 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 Regression using Linear Algebra; The Gradient Vector

📝 In-Class Notes📺 Recording

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 Gradients and Gradient Descent

📝 In-Class Notes📺 Recording

Ch. 8.3, Ch. 8.4, Ch. 8.5

Convexity will not appear on Midterm 2, but will be on the Final Exam.

HW 7 Projections; Regression using Linear Algebra

Ch. 8.3, Ch. 8.4, Ch. 8.5

Sun Jun 7

HW 8 Multiple Linear Regression, Gradients

Ch. 8.3, Ch. 8.4, Ch. 8.5

No slip days allowed!

Week 6: Midterm 2 and Eigenvalues

Mon Jun 8

Lab 9 Gradient Descent and Convexity

Tue Jun 9

EXAM Midterm 2 (1-3PM, 1690 BBB; see logistics here)

🧠 Problems

Thu Jun 11

LEC 11 Eigenvalues, Eigenvectors, and Diagonalization

📝 In-Class Notes📺 Recording

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 Gradient Descent and Convexity

Tue Jun 16

LEC 12 Diagonalization, Spectral Theorem, SVD

📝 In-Class Notes

Ch. 9.4, Ch. 9.5, Ch. 10.1, Ch. 10.2

Wed Jun 17

Lab 11 Adjacency Matrices and Diagonalization

Ch. 9.4, Ch. 9.5, Ch. 10.1, Ch. 10.2

Thu Jun 18

LEC 13 SVD and PCA

Ch. 10.2, Ch. 10.3, Ch. 10.4

HW 10 Eigenvalues and Eigenvectors

Ch. 10.2, Ch. 10.3, Ch. 10.4

Sun Jun 21

HW 11 Singular Value Decomposition

Ch. 10.2, Ch. 10.3, Ch. 10.4

Week 8: Review and Final Exam

Mon Jun 22

Lab 12 Singular Value Decomposition

Tue Jun 23

LEC 14 PCA Applications; Review

Ch. 10.4

REV Post-Midterm 2 Practice Problems

SUR End-of-Semester Survey and Official Evaluations

Fill out both surveys by Tuesday, June 23rd for 1% of extra credit to your overall grade.

Wed Jun 24

EXAM Final Exam (8-10AM, 1018 DOW; see logistics here)