Matt Eland

Using ML.NET to Predict Video Game ESRB Ratings with C#

Did you know you can train and use machine learning inside of your .NET applications without needing detailed knowledge of machine learning algorithms? In this talk we'll explore ML.NET's AutoML capabilities and how accessible machine learning in C# really is.

We'll focus heavily on the automatic algorithm selection features of AutoML in ML.NET and talk about the various tasks it can achieve before drilling deeper to apply AutoML to solve a multi-class classification problem. We'll train AutoML with an open-source dataset and have it predict video game ESRB ratings for a few hypothetical games provided by the audience.

We'll also explore evaluating model performance, ideal training times, and how trained models can be saved and loaded for use in production applications, as well as some places you can go to learn more about ML.NET and machine learning in general.

I believe I can do this without an internet connection if my starter project already has ML.NET dependencies installed and the dataset downloaded.

Stand Back; I'm going to try Data Science!

Curious about data science and its relation to software engineering? Want to know how to dabble in artificial intelligence or machine learning side projects before taking the plunge? Come check out this session.

In this session I'll highlight my own journey in layering data science skills on top of a software engineering background. I'll teach you the terms, roles, languages, libraries, and technologies you'll encounter and help you understand what aspects of math and programming are helpful in setting down this journey.

You'll discover easy ways to get started with Python, R, and get connected to the data science community. I'll show you how to discover public datasets and visualizations to help inspire your own journey. By the time the session is finished, you'll know how to find out if data science is a good fit for you and how to take it to the next level if you discover you like it.

Is Die Hard a Christmas Movie? Let's ask Azure!

When it comes to popular Christmas movies there's a recurring debate as to whether or not that list of movies should include the 1988 film Die Hard. This talk explores the use of Azure Automated Machine Learning (Auto ML) to build a classification algorithm that can evaluate movies to determine if they should be considered a Christmas movie. We'll explore taking our raw data, cleaning and preparing it without introducing bias, building, training, and evaluating a machine learning model. By the time we're done you'll understand the basics of automated machine learning on Azure and see what it thinks about Die Hard.

Using Genetic Algorithms to Breed Killer Squirrels

Can a computer program truly surprise its creator?

In this highly unusual session, you'll get a high-level introduction to genetic algorithms as we look at a simple scenario involving training a virtual squirrel to find an acorn and return to its tree in a 2D world without being eaten by the neighborhood dog.

Not exactly a common business problem, but this code-free overview lets us explore the basics of genetic algorithms, representing potential solutions as genes, evolving solutions over time, the role of crossover and mutation, and the importance of a well-thought-out fitness function.

All of this will be done to see if a computer can evolve a gene capable of solving a specific problem (spoilers: it can), and see if a computer can find solutions its programmer didn't even think were possible.

This is an interesting combination of a talk on what genetic algorithms are as well as a discussion of the role of side projects. It is intended for a shorter time slot, but a longer abstract could be made for a more in-depth talk covering more technical details.

Automating my Dog with Azure Cognitive Services

Like many terrier owners, I have a problem. My dog is overworked from the constant need to monitor multiple streets to bark at squirrels or passers by. I'd like to free up more of his time and energy for snuggling and play but the outdoors must still be monitored. Thankfully, it turns out that much of what my dog does, Azure Cognitive Services can help with.

In this talk we'll use this absurd premise to explore progressively enhancing applications through the Azure Cognitive Services speech, vision, and text APIs. We'll look at object detection, facial APIs, text to speech, speech to text, and language understanding.

By the end of this session you'll have more of an understanding of what Azure Cognitive Services can do and the basics of how to interact with them from code so that you, like my dog, can take advantage of pre-trained machine learning models to enhance devote more of your energy to other areas.

This will require an internet connection

Using ML.NET and C# to Predict Away Team Deaths

Details coming soon, but uses ML.NET's AutoML features and C# to help beginners get started with C# machine learning, within the context of the Star Trek universe.

I have not yet found an appropriate dataset for this. Full abstract coming once I have that dataset identified.