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, and design 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.
The course will run from August 11 – November 17, 2021
- Application deadline: July 11, 2021
- Deadline for submitting coding module and payment: July 25, 2021
Alumni / individual: $895, includes direct email support, computational textbook, informal feedback on assignments and LinkedIn certificate
Corporate / government: $1950, includes direct email support, computational textbook, formal grading and detailed personalized feedback, priority support on weekends and weeknights, group support sessions (if requested), and LinkedIn certificate of completion
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.
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 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.
Winter 2021 support staff and instructional leads
ML and Coding coach
Degree: Aerospace engineering
Favorite application of ML: Being able to modify images and videos with minimal side-effects by identifying their underlying features
About: I like to play board games and watch sports such as Formula 1 and football. I also love traveling, and trying new and unusual street food in each country!
Mynerva platform support lead
Degrees: Computer Science and Mathematics
Favorite application of ML: Using GANs to generate fake images/people/memes is really cool! My favorite is thiscatdoesnotexist.com
About: I finished my undergrad in 2019 and am now working at UMich and at Mynerva to try to do cool things around CS education for everyone (like this course!). In my free time, you can find me learning how to cook, eating tacos, volunteering with oSTEM, and annoying my lovely kitties.
ML and Coding coach
Specialty: Math proof and modeling
Favorite application of ML: Searching trends prediction and scissor rock paper recognition. Fun to implement and get good practical usage!
About: Drama acting amateur/ enthusiastic runner.
ML and Coding coach
Degree: Electrical and Computer Engineering
Favorite thing about ML: Deep learning for computer vision and its application in autonomous driving
About: I’m fond of watching movies and listening to various music during leisure time. Love cooperating with friends to turn innovative ideas into practical applications.