Applied Deep Learning at UofT. A New Course for Graduate Students at…
Tags: Machine Learning, Data Science, Public Health, Deep Learning

The Real World Is Messy: Why Students Need To Learn Applied Deep Learning

A short interview with Jodie Zhu about the course by Alyssa Kuhnert, Communications Lead at Dessa.

Can you talk a bit more about the way the course at UofT differs from other courses that are currently available on machine learning?

JZ: There’s this huge gap that exists right now between the level of experience graduate students have with machine learning and the experience that’s required if you want to actually make a difference with it in industry.

So far, we think that’s because there are skills that to date could have only been acquired by working on industry problems, versus just on research. We have noticed this when reviewing applications for machine learning positions from recent graduates. They get the theory, but they just haven’t had the experience working with messier types of data, as an example. They don’t know what the machine learning workflow is like from end to end.

After finishing our course, students will be ready to tackle the kinds of machine learning problems they’ll encounter in real-world settings.

What the curriculum will look like?

The course will be half lectures, half workshops. We’ll teach students how to translate a real-world problem into the language of machine learning. And talk about the unique kinds of infrastructure they’ll encounter when working on practical applications. Students will also learn the basics of putting practical applications into production, and how there is a lot more to real-world machine learning that just writing code.

To make sure all participants have the right foundation, we’ll also devote time in the first few weeks of the course to go through the fundamentals of neural networks and deep learning in a more theoretical way.

The biggest way we’ll be evaluating the course is with a final project, where students will work in small groups to develop a real-world application for deep learning related to public health.

It’s exciting that the course is being offered at the Dalla Lana School for Public Health, since there are so many applications for deep learning in healthcare. What are your thoughts on the applications in this field?

One of the reasons Ragavan and I chose to approach this faculty about the course is because of this very reason. We both see how much of an impact deep learning could have on improving healthcare around the world.

When we talk about using AI for health, I think a lot of people immediately jump to thinking about glamorous applications like using image recognition to diagnose rare diseases, or for drug development. These applications obviously have amazing potential. But then there are also these less glamorous applications which are more behind the scenes, and I see tremendous value there too. Things like reducing waiting times in hospitals.

For me, these kinds of practical applications are actually some of the things that I’m most fascinated by. I love being able to see the impact of my work and how it can improve existing processes. Lately at Dessa we’ve been focussed on how deep learning can make an impact on healthcare, and working with these students is definitely one way to do that.