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Applied Computational Linear Algebra for Everyone

About this course

The Continuum Jumpstart Course Applied Computational Linear Algebra for Everyone course is designed to equip you with the knowledge you need to link the math of linear algebra to code with a few “must know” applications centered around different ways of casting and fitting a system of equations.

You’ll learn by programming from scratch in a hands-on manner using a one-of-a-kind cloud-based interactive computational textbook that will guide you, and check your progress, step-by-step. Using real-world datasets and datasets of your choosing, you will understand, and we will discuss, via computational discovery and critical reasoning, the strengths and limitations of the algorithms and how they can or cannot be overcome. 

By the end of the course, you will be able to recognize and use linear algebra concepts as they arise in machine learning and data science. 

Since you’ll learn by doing (via coding), you’ll spend quite a bit of time coding and debugging not-working code. So a basic facility with (language agnostic) programming syntax and computational reasoning is invaluable. The rest you will learn in the course itself, i.e., you don’t have to be a Java whiz but you do need to have used Python, MATLAB or R. 

This course offers the opportunity to work in groups, remotely, or completely on your own. The choice is yours.

Important dates

The course will run from October 15 – December 15, 2020

  • Application deadline: August 31, 2020
  • Acceptance and waitlist notification: September 3, 2020
  • Deadline for submitting coding module: September 10, 2020
  • Payment and registration deadline: September 16, 2020

Cost

This first “soft-launch” will be discounted price of $99/person — the discount is relative to the full price of $149/per person.

Syllabus

This offering has evolved from many years of the instructor teaching Computational Data Science and Machine Learning at the University of Michigan, MIT Lincoln Laboratory and the Air Force Research Laboratory (AFRL). 

The syllabus distills elements the linear algebra elements necessary so that one may take more advanced courses in computational science and engineering that require linear algebra as a pre-requisite. 

Over the years of teaching this course at U-M, the instructor has derived tremendous satisfaction from seeing students from a wide range of disciplines seeing how the beautiful math leads to beautiful code and applications that seem magical the first time the math and code come together to do something remarkable, as in the many applications we will showcase. That’s a bit part of the fun of the underlying subject matter and we hope you leave with that sense of wonder, too.

Instructor

Rajesh Rao Nadakuditi

Rajesh Rao Nadakuditi

Prof. Nadakuditi is an award-winning researcher and teacher dedicated to making machine learning accessible to individuals from all disciplines.

His graduate level course, Computational Data Science, attracts hundreds of students from dozens of disciplines.

In addition to receiving the Jon R. and Beverly S. Holt Award for Excellence in Teaching, Prof. Nadakuditi has received the DARPA Directors Award, DARPA Young Faculty AwardIEEE Signal Processing Society Best Young Author Paper AwardOffice of Naval Research Young Investigator Award, and the Air Force Research Laboratory Young Faculty Award.

Learn more about the course, Computational Data Science, that Prof. Nadakuditi designed for individuals from all walks of life who want to learn how to work with data. This is a more advanced version of the Continuum course, Computational Machine Learning for Scientists and Engineers