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December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference

Schedule: Chat, machine learning, and AI sessions

1:30pm–5:00pm Tuesday, December 6, 2016
Location: 310/311 Level: Intermediate
Wolff Dobson (Google)
Average rating: ****.
(4.71, 7 ratings)
Wolff Dobson walks you through training and deploying a machine-learning system using TensorFlow, a popular open source library, and demonstrates how to build machine-learning systems from simple classifiers to complex image-based models. Read more.
12:05pm–12:45pm Wednesday, December 7, 2016
Location: Summit 1 Level: Beginner
Verdi March (Deep Labs)
Verdi March demystifies deep learning and shares his experience on how to gradually transition to deep learning. Using a specific example in computer vision, Verdi touches upon key differences in engineering traditional software versus deep learning-based software. Read more.
2:35pm–3:15pm Wednesday, December 7, 2016
Location: Summit 1 Level: Intermediate
Arun Veettil (Skellam AI)
Average rating: **...
(2.20, 5 ratings)
Making recommendations for the food and beverage industry is tricky as they must take into consideration the user's context (location, time, day, etc.) in addition to the constraints of a regular recommendation algorithm. Arun Veettil explains how to incorporate user contextual information into recommendation algorithms and apply reinforcement learning to track continuously changing user behavior. Read more.
5:05pm–5:45pm Wednesday, December 7, 2016
Location: 310/311 Level: Beginner
Tags: ai, text
Matt Winkler (C+E) (Microsoft), Jennifer Marsman (Microsoft)
Average rating: ***..
(3.50, 2 ratings)
Matt Winkler and Jennifer Marsman explain how to easily extend your apps and services with bots to reach users where they are—in messaging apps—covering use cases and case studies, how to quickly get started building a bot, how to process input using linguistic analysis, and how to deploy and integrate bots with messaging apps. Read more.
11:15am–11:55am Thursday, December 8, 2016
Location: Summit 1 Level: Non-technical
Alyona Medelyan (Thematic)
Average rating: ****.
(4.25, 8 ratings)
With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases. Read more.
1:45pm–2:25pm Thursday, December 8, 2016
Location: Summit 1 Level: Intermediate
Qirong Ho (Petuum, Inc.)
When operating on billions of data events per day, modern AI and machine-learning programs require distributed clusters with tens to hundreds machines. Qirong Ho offers an introduction to high-efficiency AI and ML distributed systems developed as part of the Petuum open source project and explains how they can reduce capital and operational costs for businesses. Read more.
2:35pm–3:15pm Thursday, December 8, 2016
Location: Summit 1 Level: Intermediate
Tags: telecom
Flavio Clesio (Movile), J.P. Eiti Kimura (Movile)
Average rating: ***..
(3.50, 4 ratings)
Can you imagine an intelligent software to assist in your decision making and drive actions? Flavio Clesio and Eiti Kimura offer a practical demonstration of using machine learning to create an intelligent monitoring application based on a distributed system data analysis using Apache Spark MLlib. Read more.
4:15pm–4:55pm Thursday, December 8, 2016
Location: 321/322 Level: Intermediate
Adam Gibson (Skymind)
Average rating: ***..
(3.50, 2 ratings)
Adam Gibson offers a brief overview of deep reinforcement learning on Spark, exploring how to run large-scale training on Spark and the implications on deep reinforcement learning targeting the doom environment. Read more.
4:15pm–4:55pm Thursday, December 8, 2016
Location: 328/329 Level: Advanced
Anusua Trivedi (Microsoft)
Average rating: ***..
(3.00, 1 rating)
Anusua Trivedi proposes a method to apply a pretrained deep convolution neural network (DCNN) on images to improve prediction accuracy. This approach improves prediction accuracy on domain-specific image datasets compared to state-of-the-art machine-learning approaches. Read more.