COS 302 / SML 305 / ECE 305: Mathematics for Numerical Computing and Machine Learning

Lecture: Mon/Weds 10:40am-12:00pm in Commons D121

Prof. Ryan Adams (OH: Wed 3-4pm in COS 411)

TA: Boya Zeng (OH: Tues 12-1pm and 4:30-5:30pm; Thurs 4:30-5:30pm; COS 003)

Precept 01: Th 9:35 am – 10:25 am, Friend Cen 008
Precept 03: Th 12:15 pm – 1:05 pm, Friend Cen 111


syllabus | schedule | assignments | quizzes & exams | gradescope | grading | books | faq

Announcements


Schedule

Subject to change.

Linear Algebra

Week 1: 2-4 September (short week, first lecture Weds 2 Sept)

Assignment 1 Out

Course Logistics

Vectors

Matrices


Week 2: 7-11 September

No lecture Mon 7 Sept (Labor Day).
Assignment 1 Due 6pm ET Wednesday 9 Sept 2026
Assignment 2 Out

Systems of Linear Equations

Matrix Inversion


Week 3: 14-18 September

Quiz 1 in class Wednesday 16 Sept 2026

Vector Spaces

Linear Independence, Basis and Rank


Week 4: 21-25 September

Assignment 2 Due 6pm ET Wednesday 23 Sept 2026
Assignment 3 Out

Linear Maps

Change of Basis

Norms and Inner Products


Week 5: 28 September to 2 October

Quiz 2 in class Wednesday 30 Sept 2026

Orthogonality

Gram-Schmidt Orthogonalization

Determinant and Trace


Week 6: 5-9 October

Assignment 3 Due 6pm ET Wednesday 7 Oct 2026
Assignment 4 Out

Eigenvectors and Eigenvalues

Matrix Factorization


Week 7: 12-16 October

Midterm Exam in class Weds Oct 14
No precept Thurs/Fri.

Singular Value Decomposition

Fall Recess: 17-25 October

Probability

Week 8: 26-30 October

Assignment 4 Due 6pm ET Wednesday 28 Oct 2026
Assignment 5 Out

Random Variables

Probability Density and Mass Functions

Expectations


Week 9: 2-6 November

Quiz 3 in class Wednesday 4 Nov 2026

Joint Probability

Independence and Dependence

The Gaussian Distribution


Week 10: 9-13 November

Assignment 5 Due 6pm ET Wednesday 11 Nov 2026
Assignment 6 Out

Inequalities and Limits

Pseudo-Random Numbers

Monte Carlo

Information Theory

Optimization

Week 11: 16-20 November

Quiz 4 in class Wednesday 18 Nov 2026

Differentiation


Week 12: 23-24 November (short week)

No lecture Weds (Thanksgiving), no precept Thurs/Fri.

Optimization Basics


Week 13: 30 November to 4 December

Assignment 6 Due 6pm ET Wednesday 2 Dec 2026
Assignment 7 Out

Stochastic Gradient Descent

Automatic Differentiation

Lagrange Multipliers


Week 14: 7 December (last day of classes Mon 7 Dec)

Quiz 5 in class Monday 7 Dec 2026

Higher-order Optimization

Convex Optimization


Reading Period: 8-15 December

Assignment 7 Due 6pm ET Tuesday 15 Dec 2026 (Dean's Date)


Assignments


Quizzes and Exams

Quizzes are short and will happen at the start of lecture. They are meant to be easy if you did the homework and are keeping up with the material. The lowest quiz score is dropped and there are no makeup quizzes; if you must miss one, that becomes your dropped quiz. There is no quiz in a week with an exam.

Midterm information

The midterm exam will be during class time (10:40am-12:00pm) on Wednesday 14 October, in Commons D301 — note that this is not our usual lecture room.

Everything through the Wednesday 7 October lecture is fair game: linear systems, vector spaces and bases, linear maps, change of basis, norms and inner products, positive definite matrices, orthogonality and projections, least squares, eigenvalues and eigenvectors, diagonalization and the spectral theorem, determinant and trace, and the characteristic polynomial. The SVD (Monday 12 October lecture) will not be on the midterm. The exam is generally conceptual, but may also ask a question or two about Python/NumPy in line with what you've been doing in the homework assignments.

You may bring one two-sided cheatsheet. The sheet may be typeset, but you must make it yourself; copying someone else's cheatsheet is not allowed. Otherwise the exam is closed book, with no electronic devices.

A selection of practice problems is available. These problems are reflective of the types of questions you can expect, although there are more practice questions than you will have to answer on the midterm itself. The TAs will run a review session on the evening of Monday 12 October (time and location to be announced), and solutions to the practice problems will be released after that session.


Grading


Required Book

Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press. 2020.
Freely available online.

Frequently Asked Questions