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Computational Machine Learning for Scientists and Engineers

About this 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.

Important dates

The course will run from September 1 – November 24, 2020

  • Application deadline: August 1, 2020
  • Acceptance and waitlist notification: August 5, 2020
  • Deadline for submitting coding module: August 13, 2020
  • Payment and registration deadline: August 14, 2020

Cost

This first “soft-launch” ECE alumni-only offering will be for a discounted price of $249/person — the discount is relative to the full price of $895/per person.

If you complete the 13 week course by completing and turning-in the weekly assignments then you will get your course fee refunded.

Why would we do this? We really want you to learn and ECE is committed to supporting you in your (machine) learning journey.

Course expectations

The course will run for 13 weeks and will require 5-6 hours of coding work from you each week.

You’ll learn by doing and we (the instructor and the instructional staff) are here for you.

You will get stuck at various points. Math stars get stuck programming the code. Programming stars get stuck linking math to code. Everyone gets stuck somewhere because there are a lot of subtle concepts being linked together. We’re here for you and we commit to working with you to helping you get unstuck so you can deepen your understanding and master the material.

If you are able to commit to the course, including and especially by reaching out when you get stuck, we know that we can get you to the point where you can leave the course armed with a set of ML tools and solutions that you can immediately benefit from.

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.

Fall 2020 support staff and instructional leads

Travis DePrato

Travis DePrato
Mynerva platform support lead

Degree: Computer Science & Engineering
Specialty: Mathematical Sciences

Favorite application of ML: Image style transfer

About: Just moved to Seattle, spend my free time playing with my cats, contributing to open source software, and eating all the tacos.

Anzhelika Iugai

Anzhelika Iugai
Coding Q&A support staff

Degree: Computer Science

Favorite application of ML: Forage is a machine learning algorithm that considers what you have in the fridge or pantry and generates an innovative recipe that utilizes those available ingredients

About: Hobbies: cooking, gardening, playing board games, traveling.

Steve Lee

Steve Lee
AWS Deployment Support lead

Degree: Computer Science,
Specialty: ML application in Mobile and AWS

Favorite application of ML: Data compression and NLP for language translation

About: I like to learn and perform magic tricks 🙂

Rajeev Nag

Rajeev Nag
Coding Q&A support staff

Degree: Computer Science

Favorite application of ML: Image processing/computer vision

About: I love hanging out with friends and watching movies.

Will Soltas

Will Soltas
Coding Q&A support staff

Degree: Computer Science

Favorite application of ML: Style Transferring and GAN artwork

About: Mountain Biking, playlist curation, rowing in Argo Pond.

Nithin Weerasinghe

Nithin Weerasinghe
Coding Q&A support staff

Degrees: Computer Science; Cognitive Science

Favorite thing about ML: The immense potential for it to guide scientific breakthroughs

About: I enjoy board games, anime, podcasts, cooking, and spending time with friends. I’m also a big fan of the Detroit Lions. My favorite thing about Michigan is being surrounded by so many unique and hardworking people.

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