*Remember: expedite your check-in by registering via Eventbrite (https://www.eventbrite.com/e/predictive-analytics-with-numenta-machine-intelligence-tickets-26153281168)
*Note: space limited to 150 people. First come, first served.
As sensors integrate with our daily lives, driven largely by the internet of things (IoT), there is demand for streaming analytics algorithms to provide insight from this data. Factories, farms, homes, people, and more are being outfitted with sensors that produce streaming data, but traditional batch-processing analytics methods don't suffice. Algorithms must be able to learn and predict online, in real-time. They also must continuously learn and adapt to changing statistics of the environment while simultaneously making predictions.
At Numenta we've developed Hierarchical Temporal Memory (HTM), a theory of neocortex implemented in software for machine learning applications. HTM runs online and unsupervised, performing anomaly detection, prediction, and classification on streaming data. HTM can run on wide variety of data streams, from IT server metrics to GPS coordinates. In this talk, Alex will discuss HTM in the context of predictive analytics, presenting real-world use cases.
6:00 pm - Doors open & food/drinks
6:50 pm - Announcements
7:00 pm - Talks Start
8:30 pm - Networking
*Expedite your check in at Galvanize; register here (https://www.eventbrite.com/e/predictive-analytics-with-numenta-machine-intelligence-tickets-26153281168)
What to Bring:
Laptop not required.
Attendees should have some background in data analytics and machine learning. Familiarity with HTM would be great, but certainly not required.
Meet the Instructor:
Alex Lavin (https://www.linkedin.com/in/alexander-lavin-b9644322), Numenta
Alex is a researcher at the intersection of artificial intelligence and neuroscience. As a Software Engineer with Numenta, he's working to build intelligent machines by reverse engineering the neocortex. Alex's focus is in natural language processing, or developing algorithms that enable computers to process and understand human language. Alex studied mechanical and aerospace engineering at Cornell, and robotics at Carnegie Mellon. He was recently selected to the Forbes 30 Under 30 List for Science.
Twitter: @theAlexLavin (https://twitter.com/theAlexLavin)
What You Will Learn:
Attendees should expect to learn about the unique challenges of streaming data, the requirements of predictive analytics algorithms, and how HTM can be used for anomaly detection, prediction, and classification problems.
Where to learn more:
*To expedite check in at Galvanize; register here (https://www.eventbrite.com/e/predictive-analytics-with-numenta-machine-intelligence-tickets-26153281168)
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