Computational Machine Learning for Scientists and Engineers





13 weeks

5-6 hours per week

Application Deadline

Next cohort begins in January 2025.

Please email [email protected] with any questions.

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Harness the power of machine learning.

Machine learning can change the way that we tackle problems and see the world. The scientists and engineers who work in machine learning and AI require a very specific skill set. In these roles, experts need to understand, train, design, and deploy machine learning algorithms.

In this online course, you will learn by programming machine learning algorithms from scratch using a one-of-a-kind cloud-based interactive computational textbook. Using real-world datasets, you will understand the strengths and limitations of the algorithms and how they can or cannot be overcome. 

Although the course is online and at your own pace, the instructors are supportive and readily available. Lead instructor Raj Rao Nadakuditi, PhD, acknowledges that this is a complex topic and is dedicated to ensuring learner success. 

By the end of the course, you will be ready to harness the power of machine learning in your daily job. Your new knowledge will allow you to prototype new machine learning applications for your organization.


  • Understand, design, and train a machine learning system from scratch
  • Understand the strengths and limitations of algorithms
  • Learn how machine learning algorithms function and how to reproduce them
  • Deploy a working machine learning prototype


This course is fully online and self-paced. Instructors are available and willing to support learners through the learning management system. You will have access to the course for 13 weeks and will participate in an average of 5-6 hours of coursework and instruction per week. 

Because this course is highly technical, an application that includes a pre-test is required before registration.

  • Intro to Julia and Python for data science and machine learning
  • Matrix math 101
  • Binary supervised classification
  • Multi-class supervised classification
  • Classification via logistic regression
  • Decision theory
  • Intro to deep feedforward networks
  • Intro to convolutional neural nets
  • Deep generative neural networks and autoencoders
  • Adversarial machine learning
  • Transfer learning
  • Data augmentation for deep learning
  • Fruit classification
  • Rock, paper, scissor classification
  • Wine classification
  • Data set of your choosing
  • Deploying a model on AWS Lambda Service

Who Should Attend

This is a highly technical course designed for scientists and engineers who need to harness the power of machine learning.

Testimonials & Advice

Get ready to be amazed. Even with the beginning topics / assignments, there was something really intriguing that was revealed.

I would definitely take it! It is a great crash course. I feel ready to start deploying ML at work!

Professor Raj was unbelievable during this course. It was clear throughout how excited he is about ML *and* in helping us learn its power and techniques. He was incredibly responsive to our questions and always encouraged us and guided us when needed. Students are lucky to have him as a professor.

The level of interaction was absolutely phenomenal. I feel like I’m good at memorizing and so I do great in a classroom setting, but sometimes I would rely on my memory instead of fully understanding the material. In this course’s case, I found it easier to learn by writing the code and interacting with the codex.

I started out with a decent coding foundation but absolutely no understanding of ML outside of buzz and articles meant for the general public. I was hoping to learn details about several different classes of ML algorithms and the problems that they can be applied to, and how to implement them. I feel confident that I can now apply various forms of neural nets to solve many different problems.

This course helped me advance on my journey towards developing practical machine learning solutions to business problems – including core concepts, tools, and model design details.

I liked the pace of the course and the freedom to bring in so many different aspects of ML from the real software development world … such as COLAB, Nvidia, PyTorch, Julia, TensorFlow and Keras to learning environment.

I truly appreciate the opportunity to take part in this course, and the effort put forth by the instructor/staff to make it worthwhile. This was a great way for me to learn about machine learning and it has strongly increased my interest in the topic. I am excited to apply it to my own work/interests in the future!

It was beyond what I have expected, and it boils down to the codices. It was such a great interactive environment.

I wanted to learn how machine learning works and how it could be applied in my sphere of influence to improve my work products and how it could be used at home as well. I can honestly say that I can see potential for ML all around me! I would often find myself saying, “This would be a great use for ML. How would I build the network?” I understand now how deep networks work, how generative networks work, and how people are using ML in all areas of business, science, and technology.

My primary goal in this course was twofold: to obtain an overview of the extensive field of deep neural networks and to gain hands-on experience designing and evaluating some of the particular techniques. This course has enabled me to achieve my goal and more.

“Guided hands-on” learning via codices is excellent. Faculty is knowledgeable and engaged. What opportunities do you have that look better?

The Mynerva style of learning is remarkably effective. Maybe more so than traditional lectures/textbook/homework. I can move at my own pace, get instant feedback, revise, and try again. I look forward to seeing this evolve.

The course would be extremely beneficial to understanding machine learning and for career development in related areas.

The pacing is excellent. I felt like I understood each nugget before moving on to the next, and then before I knew it, bam, ML magic. This really speaks to the quality of course and lesson construction.

I was hoping to learn the basics of ML/AI. The class exceeded my expectations as I learned much more than that. In addition to the foundation of ML/AI I also learned a number of tools such as Piazza, Loom, etc. and the great Mynerva environment. I also learned (somewhat) how to set up the local environment on my PC so I can use the GPU on my own computer!

I would advise someone to put forth their maximum effort. The additional exercises and optional codices were a great opportunity to learn more about specific applications or other topics, so I’m glad I took the time to complete most of them.

I would definitely recommend other folks to consider taking this course, and take the time to not only doing the codices but also going beyond and implement the algorithms on their desired application or even on any other programming languages. This way one can gain a great confidence.

If you keep up with the codices by working through all of the details at least two to three days each week, you will develop a thorough understanding of the material. The subsequent codices build on the previous codices so that, over time, your knowledge builds. The instructor and team are happy to answer your questions–they provided help within hours every time I emailed them.

Don’t give up on a question too quickly. Don’t be afraid to ask for help. Several times as I formulated my question to submit an e-mail, I was able to solve my issue.

If you set aside the time to go through the material and try to learn it and understand it, you will! But don’t put it off or procrastinate.

Definitely set aside about 30 minutes to 1 hour each day to work on it. Some weeks I was so busy that I had to do the codexes on the weekends, but I feel that I understood it best when I was able to progress a little bit each day.

The course may take more than the 5-6 hours per week estimate.

Reach out to classmates on Piazza when stuck, and don’t be surprised if the calculations take a long time. That is part of the game, and why learned weights are worth their weight (hah) in gold.


Raj Rao Nadakuditi, PhD

  • Associate Professor, Electrical Engineering and Computer Science (EECS)

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