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Instructor Sergey Fogelson on the Importance of Grasping Machine Learning & AI Principles

By Emily Wilson • February 01, 2018

Sergey Fogelson is the VP of Analytics and Measurement Sciences at Viacom and an instructor our Live Online Machine Learning and Artificial Intelligence Principles part-time professional development course. Check out an on-demand sample class with Sergey here.


As Viacom's Vice President of Analytics and Measurement Sciences, Sergey Fogelson uses machine learning and artificial intelligence techniques almost every day to predict tv ratings, forecast future demand for shows, and recommend new content to acquire. In our upcoming Live Online Machine Learning and Artificial Intelligence course, he'll teach students the principles of these techniques, which he views as crucial to know and practice in order to stay competitive in the data science field. 

Sergey began his own career as an academic at Dartmouth College, where he earned his Ph.D. in Cognitive Neuroscience before diving into the NYC startup scene, working as a data scientist in alternative energy analytics, digital advertising, cybersecurity, finance, and media. He's been consistently involved in the NYC-area teaching community, tackling multiple courses with us and others around town. We're excited to have him back to teach this project-focused, hands-on course. Read on for a Q&A with Sergey, in which he discusses his career, what inspires him, and what he looks forward to most about the course. 

Our course page provides a lot of detail, but if you were to run into someone randomly who wanted to know about the course, what highlights would you note off the top of your head?
I would tell them that you get to actually understand how two of the most common machine learning algorithms, linear and logistic regression, work at a fairly deep level. Furthermore, you get to learn about other, more advanced ML algorithms by developing intuitions about how they work based on what you learned from the simpler, more "interpretable" models. Finally, you'll learn that using a powerful algorithm isn't usually the most important aspect of building a successful ML model and that the data you feed the model is almost always a more important problem that you must solve. So, we cover a variety of what are called "feature engineering" strategies for making sure that the data you feed any model increases your chances of building a good machine learning model.

What makes you excited to teach a course on Machine Learning and AI?
I'm really excited about teaching this course because I've been using machine learning and AI techniques for over a decade and want to bring their transformative potential to people who are curious to learn about how they work. Machine learning algorithms are now used in so many industries – they undergird most of our day-to-day experience online – that it will soon be difficult to stay competitive in them without having some basic understanding of how they work. This course is meant to provide some intermediate-level, hands-on training for people interested in ML and AI who find it difficult to learn on their own. Ultimately, I want to bring ML to as many people as are interested in the topic, who want a collaborative learning environment that they can reach from the comfort of their own home, and this course is an excellent way for me to do that.

This course has a project-based curriculum. How important do you think projects are when teaching and learning machine learning and AI? 
Experiential learning is absolutely critical for becoming better at any skill. If you want to learn to snowboard, you strap a snowboard to your feet, and you fall over and over again until you learn to balance on it. The same certainly applies to learning anything in machine learning or any subfield of computer science. You need to build models in order to learn how they work, what their quirks are, and how/when they are prone to fail. Reading about ML will only get you so far. You might understand theoretical bounds on complexity, or how some parameter affects the complexity of the specific model you're working with, but without building your own model, you won't have a very good understanding of why this specific model isn't performing well on this specific set of data. Being able to build ML models is absolutely critical to diagnosing why the fail, and knowing why a model fails is an essential skill to have for any ML practitioner.

Of the projects on the schedule, do you have a favorite?
I'm really excited about what we will be working on in the last week of the course. I've taken some publicly available data for a visual task and for a natural language processing task and have created some content around using Keras, a powerful neural network (AI) package written in Python. This will be a sort of culmination of what we cover over the first 4 weeks of the course and will involve some significant coding. I'm excited about this because it covers some cutting-edge AI material, but at a level that I think is pretty approachable. It'll also give students the ammunition they need to continue developing their ML/AI skills going forward.

How often are you using machine learning in your current role at Viacom?
I use machine learning and AI techniques almost daily at Viacom. We build ML models to predict tv ratings, to forecast future demand for our shows, and to recommend what new content to acquire. Without using ML techniques, these would be much more difficult problems to tackle.

What work inspires your own?
I'm a big fan of primary research. This is probably a vestige of my grad school days, when I would read lots and lots of academic papers on whatever was happening in Neuroscience. Now I try to do the same thing but as it pertains to interesting new data science methods developments. I don't really have people who inspire me other than those who are working as researchers in basic scientific fields (whether that's biology, physics, or what have you) that also create new analytical tools that can be used outside their fields. 

How do you stay up-to-date in the quickly evolving field?
I used to be a huge fan of but that website has been overrun by spammers recently (if there's a problem that could totally use an ML solution, it's this one). I'm also a big fan of several subreddits - r/MachineLearning, r/BigData, r/Statistics, to name a few. Scrolling through those, you can stay pretty up to date in terms of whats happening in the broader field.

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