AI Learning Accelerator
Alexander Statnikov
Free

Machine Learning Powers Better Decisions in Financial Services

Ben Vigoda
Free

A Breakthrough for Natural Language

Michael Mahoney, PhD
Free

Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap

We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system.

Alex Pentland
Free

Blockchain and AI, or future data systems must be built differently

Alejandro Jaimes, PhD
Free

Challenges and Opportunities in Applying Machine Learning

Skipper Seabold
Free

Introduction to Python for Data Science

We'll take a closer look at how Python can be leveraged to build effective data science workflows.

Alex Sandy Pentland
Free

Blockchain AI, or Future Data Systems Must Be Built Differently

Joshua Cook
Free

Engineering For Data Science

This talk will discuss Docker as a tool for the data scientist.

Andreas Mueller, PhD
Free

Introduction to Machine Learning

This talk gives a general introduction to machine learning, as well as introduces practical tools for you to apply machine learning in your research.

Aedin Culhane, PhD
Free

Enter the Matrix: Unsupervised feature learning with matrix decomposition to discover hidden knowledge in high dimensional data

Tyler Freckmann
Free

Deep Learning in Real-Time

We will take a tour of different DL algorithms and applications, learn how different DL models are built, and see how to deploy DL models for real-time processing with SAS technology.

Jason Prentice
Free

Mapping the Global Supply Chain Graph

Jon Peck
Free

Deploying your AI/FML investments

Amit Surana, PhD
Free

Applications of Deep Learning in Aerospace and Building Systems

This talk demonstrates using DBN, DAE, DRL and GAN in five different aerospace and building systems applications.

Michael Bell, PhD
Free

Machine Learning for Mobile Sensing Applications

In this talk we’ll detail the kinds of sensor data available from mobile phones and other smart devices.

Stephen Lawrence
Free

Applied Finance - The Third Culture

In this session we explore why it is important that we bridge the gap between the traditional data science cultures and applied finance.

Lukas Biewald
Free

Deep Learning Techniques for Vision

This is an extremely hands-on course to take students from little knowledge of deep learning to comfort building vision models with Keras and TensorFlow.

Trevor Grant
Free

Pavlov’s Sandman: Issues detecting snorers, training oneself not to snore via shock collar, war crime technicalities, and how to avoid all three

This talk is the journal of the explorations of a total novice audio analyst, seeking to correctly identify snores, and shock himself appropriately.

Dr. Catherine Havasi
Free

TRANSFER LEARNING: APPLICATIONS FOR NATURAL LANGUAGE UNDERSTANDING

This talk focuses on language related use cases for customer service, search, question answer, self-help and consumer finance. We'll also have some fun with applications of transfer learning.

Buck Woody
Free

Standardized Data Science: The Team Data Science Data Process - with a practical, example in Python

William Richoux
Free

Crawling the internet Data Science Within a Large Engineering System

We will discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure.

Sean Patrick Gorman, PhD & Steven Pousty
Free

How to use Satellite Imagery to be a Machine Learning Mantis Shrimp

In this session we are going to start by showing you how satellite imagery actually allows you to “see” in more bands of color than the mantis (how about 26 bands) – each band is a massive amount of data about the earth.

Sam Ransbotham, Ph.D
Free

THE ADOPTION OF AI IN BUSINESS: OPPORTUNITIES AND CHALLENGES

MIT Sloan Management Review’s recent research on AI and business strategy offers a "state of the state" of AI adoption inside corporations. This session will provide an overview of organizational readiness for and adoption of AI across sectors.

Alexander Spangher
Free

PROJECT FEELS: DEEP TEXT MODELS FOR PREDICTING THE EMOTIONAL RESONANCE OF NEW YORK TIMES ARTICLES

Topics discussed will be active learning, deep learning, Bayesian inference and causality.