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Adversarial Machine Learning: Fraud, Security, AML

Event Details

Adversarial Machine Learning: Fraud, Security, AML

Time: September 10, 2016 all day
Location: Geekdom SF
Street: 620 Folsom St #100
City/Town: San Francisco, CA 94107
Website or Map: http://conf.startup.ml/advers…
Phone: 4155470131
Event Type: machine, learning, conference
Organized By: Arshak Navruzyan
Latest Activity: May 29, 2016

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Event Description

Machine learning techniques were originally designed for environments in which the training and test data are assumed to be generated from the same (although possibly unknown) distribution and/or process. In the presence of intelligent and adaptive adversaries, however, this working hypothesis is likely to be violated.

Applying machine learning to use cases like fraud, security,  anti-money laundering and know your customer (KYC) presents a unique set of challenges:

- Little or no labeled data
- Non-stationary data distributions
- Model decay
- Counterfactual conditions

This event is entirely devoted to understanding how modern machine learning methods can be applied to these adversarial environments.  We will have hands-on workshops as well as talks by leading practitioners from industry and academia. 

Leading practitioners from Google, Capital One, Coinbase, Stripe, Square, etc. will cover their approaches to solving these problems in hands-on workshops and talks.  

The conference will also include a hands-on, 90 minute tutorial on TensorFlow by Illia Polosukhin one of the most active contributors to Google's new deep learning library.

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