J.P. Morgan DeepFin Workshop

Accrete is excited to be partnering with J.P. Morgan in delivering a machine learning (ML) workshop to portfolio and fund managers, quantitative analysts, data scientists, and valued customers of JPM with an interest in ML. Accrete has put together a stimulating agenda which will examine the future of work, and detail real world examples. Attendees will participate in hands-on interactive sessions that focus on applying practical examples on using Big Data & Machine Learning in a portfolio management context.

DEEPFIN MACHINE LEARNING TUTORIAL

J.P. Morgan is pleased to invite you to a hands-on training session with some practical examples on using Big Data & Machine Learning in a portfolio management context. Experts from Accrete.AI will discuss the state of Natural Language Processing (NLP) in finance and show us how to setup and run ML models on some NLP problems in the financial industry.

The "DeepFin" tutorial will be held in the J.P. Morgan office at 560 Mission Street, San Francisco, CA from 7:30AM to 12:00 noon on June 6th, 2019.

OVERVIEW

The 4 hour DeepFin workshop will begin with an overview of the state of ML/AI deployment in NLP. We will go through real world use cases in areas such as healthcare financial analysis, followed by a discussion of the challenges posed by the growth of unstructured data. In the next 2 hours, we will focus on mathematical introduction to various numerical data representations of text, deep learning models, and general best practices for developing these types of models. We will also go through interactive examples that implement these models with use cases in the financial industry.

Finally, we wrap up with recent research highlights from JPM Big Data and AI Strategies.

AUDIENCE

Portfolio and Fund Managers, Quantitative Analysts, Data Scientists, Stock & Sector Analysts, Traders, Dealers and anyone with an interest in ML in general.

PREREQUISITIVE KNOWLEDGE

To ensure you get the most out of the DeepFin sessions, a basic understanding of scripting language (Python or R) is assumed (writing loops, importing csv files, manipulating data frames, etc.) Some basic knowledge of statistical and ML techniques would also be helpful (linear and non-linear regression, classification, optimization, etc.). Bringing your own laptop is helpful, but not required. Pre-requisite materials and setup instructions will be provided prior to the workshop.

AGENDA

7:30 - Registration and Breakfast

8:00 - Overview and Outlook of ML in NLP

• Introduction: The future of work
• The uprising of complexity: Introduction to the fundamental components and the challenges of NLP solutions using an example in the finance domain.
• Case study: Problems in healthcare financial analysis

8:45 - Problems Due to Exponential Data Growth

• Cognitive overload due to amount of data
• Identifying data sources and types
• Defining data in the context of asset classes and markets
• Generating hypothesis to test against large amounts of data

9:00 - Refreshment Break

9:15 - Deep Learning for NLP

• Data modeling: Feature representation of text
• Sequential neural networks: Contextual engines
• Sparse training dataset challenge: Benchmarking models w.r.t. experts
• Sentiment analysis: Quantification of financial documents (Interactive example)
• Text classification: Extracting signal from noise (Interactive example)

11:15 - Refreshment Break

11:30 - JPM Big Data and AI Research Highlights

TRAINING FACILITATORS

Prashant Bhuyan, Founder & CEO, Accrete

Yurdaer Doganata, PhD, Chief Technology Officer

Sahil Agarwal, PhD, Director of AI Research

James Tannahill, Healthcare Intelligence Associate

Alex Adamis, Financial Intelligence Associate

HOST

Peng Cheng, Executive Director, Global Quantitative and Derivatives Strategy, J.P. Morgan

REGISTRATION

To register for the event contact a JPM Sales Representative

Posted by Nick Potts

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