Name: Practical Methods for Overcoming the Machine Learning Data Bottleneck
Start: 2018-10-05T09:00:00-05:00
End: 2018-10-05T13:00:00-05:00
Location: AT&T Executive Education and Conference Center

This is a paid class. Registration is at Eventbrite:
https://overcoming-ml-bottlenecks.eventbrite.com

Practical Methods for Overcoming the Machine Learning Data Bottleneck
Course Abstract

Machine learning is powerful, but it can be hard to reap its benefits without large amounts of labeled training data. Labeling data by hand can be time consuming, expensive, and impractical; and sometimes you don’t even have sufficient examples to label, especially of the rare events that are most important. This class will provide practical methods to overcome this data bottleneck. You will learn how to use heuristics to label data automatically, and you will learn how to generate synthetic training examples of rare events using generative adversarial networks (GANs). You will also learn other data augmentation approaches and methods for training models when the training data is imbalanced. The class will also cover how to use machine learning when you only have one or a few examples.

Requirements

The only prerequisite for this course is some programming or scripting experience. We will be using Python with Jupyter Notebook.

Details and RSVP at Eventbrite:
https://overcoming-ml-bottlenecks.eventbrite.com

About the Instructor

Jonathan Mugan (Linkedin) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.

Details and RSVP at Eventbrite:
https://overcoming-ml-bottlenecks.eventbrite.com

Lynn Bender

Austin Artificial Intelligence

Technology

Open Source

Big Data

Data Science

Machine Learning

Data Mining

Internet of Things (IOT)

Artificial Intelligence

Deep Learning

Neural Networks

Natural Language Processing

Artificial Intelligence Programming

DataOps

Data Engineering

Practical Methods for Overcoming the Machine Learning Data Bottleneck 

AT&T Executive Education and Conference Center

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Practical Methods for Overcoming the Machine Learning Data Bottleneck

Austin Artificial Intelligence

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