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Event Registration - Meet Up: A Master Class for Financial Professionals for AI and Machine Learning

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Date:
Friday, June 21, 2019
Time:
12:00 PM - 1:30 PM Eastern Daylight Time
Meet Up: A Master Class for Financial Professionals for AI and Machine Learning
featuring Sri Krishnamurthy, CFA, CAP, QuantUniversity

Summary
The use of Data Science and Machine learning in the investment industry is increasing and investment professionals both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.

In this workshop, we aim to bring clarity on how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques and AI in the investment industry. At the end of this workshop, participants can see a concrete picture on how to machine learning and AI techniques are fueling the Fintech wave!

Agenda for the workshop:
In the Part 1, we will discuss key trends in AI and machine learning in the financial services industry. We will discuss the key use cases, challenges and best practices of using AI and ML techniques in financial services. We will also discuss key players and drivers for the AI and Machine learning revolution.

In Part 2, we will illustrate two case studies where AI and Machine Learning techniques are applied in financial services. Note: This session is a repeat of the master class to be will be delivered at theCFA Annual Conference in London on May 14th 2019.

Case studies:
  1. Sentiment Analysis using Natural Language Processing in finance In this case study, we will demonstrate the use of Natural Language Processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google and Azure APIs. We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
  2. Credit Risk decision making using Lending Club Data In this case study, we will be using the Lending club data set to build a credit risk model using machine learning techniques.
Speaker:
Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program ( www.analyticscertificate.com ) and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri has had significant analytical applications at Citigroup, Endeca, MathWorks and has consulted to more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School. Many of his students work in Data science roles at Fidelity, Santander, Wellington, GMO, State Street etc. Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College. Sri can be reached at sri@quantuniversity.com

Please note, attendees are asked to bring their own lunches. There is no charge for Members for this event, however registration is required in advance.

Online registration is now closed. Walk-ins will be accomodated for this event.
Address:
MCLE
10 Winter Pl
14th Floor
Boston, MA 02108
Description:Registration is complimentary for members. Non-Members: $10.00