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Pythology Lecture Series: Machine Learning, AI, and Genetic Programming
September 22, 2017 @ 8:30 am - 5:00 pm$60
The next Pythology event is coming on Friday, September 22nd! Come learn about Machine Learning, AI, and Genetic Programming. Space is limited, so don't miss out on this awesome event!
The event will include talks from several pythonistas in the Indianapolis area:
Check Out The Hay Before Diving After The Needle: Intro to Pattern Discovery
By: Trey Brooks, Data Engineer, Healthcare Bluebook
MCS-DS Graduate Student at University of Illinois Urbana-Champaign
This lecture provides a brief tour of how rules of association are formed, how we can mine patterns efficiently, and why these methods are so important for data science today. Pattern Discovery is a fundamental principle of data mining. It allows a data scientist to derive association and correlation. Patterns can illuminate latent clusters in data that would not be immediately obvious. Searching for these patterns can be applied to multimedia data, spatiotemporal data, time-series data, or stream data. Furthermore, the application of sequential pattern discovery can help us understand the likelihood of natural disasters, what medical treatments have been effective, and how the DNA has changed over time.
How to Build Skynet: 7 steps (with pictures), an Intro to Neural Networks
Learn about the history of neural networks, what they are and how they work. Then build your very own AI using a neural network to predict whether or not a mushroom is poisonous or edible. If that wasn't cool enough, you'll build a second AI to classify pictures of cats and dogs. What you will learn:
1. What a neural network actually is
2. Why it is the premier data science tool.
3. How neural networks can be used in endless applications
4. What is truly important when creating an AI with neural network technology.
Most importantly, you'll leave with two basic AIs of your very own and the knowledge you need to build more.
Garbage In -> Garbage Out: Proper Data Handling for Machine Learning
By: Alyssa Batula, Recent PhD Graduate, Drexel University
Machine learning algorithms can do some incredible things, but they still rely on humans to make sure they're getting the right data. Even the most powerful algorithm will benefit from a well-prepared dataset, and a good dataset can be the difference between a working algorithm and a fancy pseudo-random number generator. This talk will cover some of the important steps required to make your data machine-learning-ready, including: * How to find or create a dataset * Initial dataset cleaning * How to make use of training, testing, and validation data sets, and why they're so important * How to select the best information to give to the classifier * How to select a classifier for your dataset