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.
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.
Portfolio and Fund Managers, Quantitative Analysts, Data Scientists, Stock & Sector Analysts, Traders, Dealers and anyone with an interest in ML in general.
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.
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)
11:30-JPM Big Data and AI Research Highlights
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
Peng Cheng, Executive Director, Global Quantitative and Derivatives Strategy, J.P. Morgan
To register for the event contact a JPM Sales Representative