Thursday, April 21, 2016
01:15 PM - 04:30 PM
|Level: ||Technical (may include code)|
While many people are excited to help computers acquire new knowledge through machine learning strategies, first we need to help the humans learn how to help the machine learn! This is a complex, vast and overwhelming topic that benefits from a gentle and deep introduction for new explorers. This will be a challenging but accessible workshop involving a guided overview to the goals, topics, algorithms, programming languages, tools and results of machine learning.
This workshop won’t make many assumptions about the attendees except that they have an open mind and curiosity about the topic. We will show coding examples but you won’t suffer without a programmatic background.
Attendees will learn about:
- The history and successes of machine learning so far
- Where machine learning fits among other analytical techniques
- Goals for current and future systems
- An introduction to the major programming languages and tools that are used
- A walkthrough of the most common algorithms and what they provide
- A discussion about the impacts of machine learning
- A path to take for further study
Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. He is a system architect, a developer, a mentor, and a trainer. Brian has been speaking about REST, the Semantic Web, and other technologies around the world as part of conferences such as NoFluffJustStuff, JavaOne, Jazoon, the Spring Experience, the Rich Web Experience, and Museums on the Web. Brian has written for DevX's Semantic Web Zone, JavaWorld, and has contributed chapters to O'Reilly Media's "Beautiful Architecture" and "97 Things Every Project Manager Should Know." He has a B.S. in Computer Science from the College of William and Mary and currently lives in northern California. He consults and speaks frequently about next generation technologies around the world.