I love speaking! I love seeing people's eyes light up when they acquire an appreciation for the things I'm excited about.


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Podcast Appearances

Recorded Talks

Talk Abstracts

  • Machine Learning – Supervised Learning - This session will delve into three important supervised learning algorithms, Perceptron, Decision Trees, and Naïve Bayes, designed to aid in the prediction process. Attendees will walk away with a firm grasp of how these algorithms function in the context of the nuML Machine Learning Library, together with application methods for their current software of choice. (This talk is more on the theoretical side.)
  • Machine Learning – Unsupervised Learning - This session will delve into three important unsupervised learning algorithms, K-Means, Hierarchical Clustering, and Gaussian Mixture Model, which are designed to aid in the clustering process. Attendees will walk away with a firm grasp of how these algorithms function in the context of the nuML Machine Learning Library and how they can apply these algorithms in their current software of choice. (This talk is more on the theoretical side.)
  • Practical Machine Learning – Predicting Things - Machine learning is one of the newest tools in a Data Scientist’s arsenal. In this session attendees will learn practical approaches to solving the prediction problem through an interactive demonstration of various popular machine learning algorithms.
  • Practical Machine Learning – Recommender Engine and Anomaly Detection - Machine learning is often perceived to be a daunting topic, when in fact its concepts are fairly intuitive and easy to use. This session will introduce nuML machine learning library basics, and will thus address the clustering issue in .NET applications by focusing on the practical real-world applications of recommendation engines and anomaly detection. At the conclusion of this session, attendees will be able to immediately use their unlabeled data to create powerful models for predicting the future based on the past.
  • Principles of Modern Data Visualization - Data visualizations are intended for summarizing complex information into visually digestible bites in an aesthetically pleasing way; getting them right is difficult work. This session will introduce developers to certain important principles that govern great data visualizations through an example-based collaborative discussion.
  • Faking it With Data - In this session attendees will learn how to auto-generate realistic data as a basis for visualizing and testing their applications. Topics will include random number generation, probability, and distributions all as a backbone to the data generation process.
  • The Social Developer - Do you have a hard time in social situations? Find it hard to talk in front of a crowd? This session is designed to give developers concrete techniques for overcoming these fears in order to succeed in social situations. Principles discussed will include how to appropriately meet new people, strike up conversations, and be comfortable in social situations. There will also be specific discussions for dealing with difficult situations in a graceful manner. The session will be highly collaborative, include demonstrations and audience participation.
  • An Introduction to Artificial Intelligence - Ever want to know how computers think? In this session attendees will learn the foundations of artificial intelligence through a collaborative discussion centered around the creation of an intelligent game. Attendees will also learn how to use advanced search techniques to solve complex problems using specialized heuristics. In short, attendees will understand how to make intelligent programs by learning how to pose an AI problem in order to maximize desired outcomes.

Bio (for conferences etc.)

Seth Juarez holds a Master’s Degree in Computer Science where his field of research was Artificial Intelligence, specifically in the realm of Machine Learning. Seth is a Microsoft Evangelist working with the Channel 9 team. When he is not working in that area, Seth devotes his time to an open source Machine Learning Library, specifically for .NET, intended to simplify the use of popular machine learning models, as well as complex statistics and linear algebra.

One Comment, RSS

  1. Nilay Sangani August 25, 2015 @ 7:39 am

    Great work, Seth.

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