Mynerva

Continuum | Computational Machine Learning for Scientists and Engineers

september 1 – november 24, 2020

about the course

The Continuum Jumpstart Course Computational Machine Learning (ML) for Scientists and Engineers is designed to equip you with the knowledge you need to understand, train, design and machine learning algorithms, particularly deep neural networks, and even deploy them on the cloud.

You’ll learn by programming machine learning algorithms 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. You will understand how machine learning algorithms do what they claim to do so you can reproduce these while being able to reason about and spot wild, unsupported claims of their efficacy.

By the end of the course, you will be ready to harness the power of machine learning in your daily job and prototype, we hope, innovative new ML applications for your company with datasets you alone have access to.

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.

how can I apply?

Visit the Continuum Jumpstart page to learn more about the logistics of the course. Click the Apply link in the About this course section to officially apply for the course.

about the instructor

Raj Rao Nadakuditi

Raj is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He received his Masters and PhD in Electrical Engineering and Computer Science at MIT as part of the MIT/WHOI Joint Program in Ocean Science and Engineering.

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 Award, IEEE Signal Processing Society Best Young Author Paper Award, Office of Naval Research Young Investigator Award, and the Air Force Research Laboratory Young Faculty Award.

His graduate level course, Computational Data Science and Machine Learning, attracts hundreds of students from 80+ disciplines across the University. He loves making machine learning accessible to learners from all disciplines and enjoys seeing how students adapt the underlying ideas and develop creative, new applications in their own scientific of engineering area of expertise.

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 computational tools at the heart of the Machine Learning (ML) revolution have only recently become as accessible as they now are. This allows scientists and engineers to harness their power without needing to become experts.

The syllabus distills elements from more advanced courses taught at the University of Michigan to give you just what you need to be able to understand, design and train a machine learning system from scratch and to deploy a working ML prototype on the cloud.

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 take these ideas and adapt them to their own application. Our sincerest hope is that scientists and engineers taking this course will do the same in their own areas of expertise and in doing so will usher in the next wave of the ML revolution.

Algorithmic and Mathematical Foundations


Algorithms for Machine Learning


Deep Neural Networks: Architectures and Applications


End-to-end case studies


Deploying ML algorithms on the cloud


frequently asked questions