MATH5110

MATH 5110: Applied Linear Algebra and Matrix Analysis

Welcome to the course page for MATH 5110: Applied Linear Algebra and Matrix Analysis.

Lecture Notes as an ebook: MATH 5110 E-Book


Course Overview

Linear algebra is one of the central languages of modern applied mathematics, data science, machine learning, optimization, scientific computing, and engineering.

In this course, we study both the theoretical foundations and computational tools of linear algebra, with emphasis on applications and mathematical understanding.

Interactive HTML Pages

Topics include:


Repository Structure

MATH5110/
│
├── notes/          # Lecture notes and reading materials
├── homework/       # Homework assignments
├── labs/           # Python or MATLAB computer labs
├── projects/       # Course projects and project instructions
├── examples/       # Worked examples and supplementary files
└── README.md       # Course front page

The structure may be updated as the course develops.


Lecture Notes

Lecture notes will be posted regularly. They are intended to support classroom discussion and should be read together with the lectures.

Students are encouraged to:


Homework

Homework assignments are designed to strengthen both theory and computation.

A typical homework problem may ask students to:

Unless otherwise stated, students should show enough work to explain their reasoning clearly.


Computer Labs

Computer labs are used to explore linear algebra through computation.

Possible tools include:

Labs may include applications such as:


Projects

Course projects connect linear algebra with real-world or research-inspired applications.

Possible project areas include:

Project instructions and expectations will be posted in the projects/ folder.


Suggested Background

Students should be comfortable with:

Students who are less familiar with programming are encouraged to start early and ask questions.


How to Use This Repository

Students should regularly check this repository for updates.

Recommended workflow:

  1. Read the lecture notes before or after class.
  2. Work through examples independently.
  3. Complete homework problems carefully.
  4. Use labs to test computational ideas.
  5. Review feedback and revise your understanding.

Academic Integrity

Students are encouraged to discuss ideas and learn from one another.

However, submitted work should reflect each student’s own understanding. Any collaboration, code, external resource, or AI assistance should be acknowledged according to course and university policies.


Instructor

He Wang
Department of Mathematics
Northeastern University


Updates

Course materials will be updated throughout the semester.

Please check this page and the relevant folders regularly.