Machine Learning and AI for Financial Professionals

Learn how to build pragmatic AI and ML applications with case studies in finance


  PRMIA Training Partner -- QuantUniversity

    Instructor: Sri Krishnamurthy, CFA, CAP   
   Delivery: Self-Directed
 Featuring recorded lectures, Case studies
 and hands-on activity labs using the QuSandbo
   Duration: 8 Lessons | 1.5 hours/lesson 
   Prerequisites: Participants are expected to have
 a working knowledge of Python.  
   Completion time: 6 months from date of purchase  
 Standard Registration Fee: $599.00

   Value Add: 3-part "Just Enough Python for Data Science in Finance"  
  on-demand course for just $150 ($349 value)
   PRMIA Network Fee: $499.00 
 Use discount code: PRMIA100
  Question/Need support?  Our training partner is here to help

Be sure to add "Just Enough Python" to your cart for only $150 more!
 Register Now!

  (You will be redirected to our training partner's registration system.)

The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

You will learn:
• Role of Machine Learning and AI in Financial services
• When do we use Machine learning and AI techniques?
• What are the key machine learning methodologies?
• How do you choose an algorithm for a specific goal?
• Practical Case studies with fully functional code

Your Instructor: Sri Krishnamurthy, CFA, CAP

Sri is the founder of QuantUniversity, a data and quantitative analysis company. He has more than 15 years experience in analytics, quantitative analysis, statistical modeling and software development. He is a quantitative specialist with significant experience in designing data mining and analytic systems for some of the world’s largest asset management and financial companies

Sri has worked at MathWorks as a Computational Finance Consultant where he worked with more than 25 customers providing asset management, energy analytics, risk management and trading solutions. Prior to that, Sri was a consultant at Endeca (now Oracle) in their Analytics Group and at Citigroup in their Fixed-Income Group building large-scale analytical and trading systems.He is a Charted Financial Analyst and a Certified Analytics Professional.

He is an active member of the Boston Security Analysts society and QWAFAFEW.

Sri is the creator of the Fintech Certificate Program & Analytics Certificate Program and teaches graduate courses in Quantitative methods, Data science and Analytics and Big Data at Babson College, Northeastern University and Hult International Business School.

Program Content

Module  Topic

Module 1

Machine Learning and AI: An intuitive Introduction
• Machine Learning vs Statistics: How has the world changed?
• A tour of Machine Learning and AI methods
  o Supervised Learning Vs Unsupervised Learning; Deep Learning; Reinforcement Learning
• Key drivers influencing the adoption of Machine Learning and AI
  o Big Data, Hardware, Fintech, AI, Alternative Data
• Key applications
  o Credit risk, Personalization, Predicting risk, Portfolio optimization and selection; Key players
• Technology companies, Data vendors, Banks, Fintech startups

Module 2 Exploratory Data Analysis & Case Study
• Exploring and Visualizing large datasets
• The Visualization zoo
• A framework to decide how to chart datasets
• Examples on how to build powerful dashboards
• Case study 1: Visualizing Categorial, Numerical, Cross-sectional and Time series Financial datasets
Module 3 Exploratory Learning: Visualization & Case Study
• Dimension reduction and visualizing datasets using PCA, T-SNE
• Manifold Learning
• Case study 1: Visualizing high-dimensional Datasets
Module 4 Exploratory Learning: Clustering and more & Case Study
• Clustering Techniques
• Distance measures
• K-means
• Hierarchical Clustering
• Affinity Propagation
• Case study 2: Using K-means for automatic clustering of stocks
Module 5 Exploratory Learning: Learn from the past & Case Study
• How does Supervised machine learning work?
• Cross sectional data 
• Time series analysis
• Regression, Random Forests and Neural Networks
• Evaluating machine learning algorithms
• Case study 3: Predicting interest rates and credit risk using Alternative data sets
Module 6 Neural Networks + Synthetic Data Generation
• Introduction to Neural Networks and Deep Neural Networks
• Case study 4: Synthetic Data Generation for VIX Scenarios
Module 7 Natural Language Processing & Case Study
• Making sense of Text and Natural Language Processing
• Sentiment Analysis: How to interpret sentiments and use it in stock selection?
• Case study 5: Analyzing Earning calls using text analytics 
Module 8 Frontier Topics
• Key issues in adopting AI and Machine learning into investment workflows
• How will Machine Learning and AI change the investment industry
• Frontier topics
  o Anomaly detection; Reinforcement learning; Quantum Computing; Risk in Machine Learning and AI; Model governance, Interpretability and Model Management
Module 9
Capstone Project (Optional/Included)
 Project presentation

PRMIA Network Fee: $499.00 using
 discount code: PRMIA100 
Add on Just Enough Python course for only $150 more! 

Register Now! 
(You will be redirected to our training partner's registration system.)

Who should attend?

Fundamental and quantitative analysts, risk and investment professionals, portfolio managers new to data science and machine learning
Financial professionals new to data-driven methodologies
Machine learning enthusiasts interested in use cases in fintech and financial organizations

QUANTUNIVERSITY Registration Policies

This course is being delivered by a PRMIA Training Partner, QuantUniversity. All registrations, payments, and course operations will be managed exclusively by QuantUniversity. When registering for the course, you will leave and use QuantUniversity's registration system, proprietary learning management system, and QuSandbox for labs. Questions and requests should be directed to QuantUniversity at

Cancellations received up to one week from the course start date (May 5, 2020) will receive a full refund less a $100 processing fee.  After this date, refunds or credits will not be issued; the registrant forfeits full payment. This cancellation policy supersedes PRMIA's posted cancellation policy.  In the event sufficient registrations are not received, PRMIA and/or QuantUniversity reserve the right to cancel this course one week prior to the start date. Full refunds will be issued if cancelled by the training partner. 

Need Support? 

Contact our training partner for questions about the course, group registrations and technical support

Analytics for a cause initiative - Thank you QuantUniversity for supporting future risk leaders!

QuantUniversity is a proud sponsor and contributor to educational scholarships, valued at $30,000, to students from eight countries and 12 chapters, participating in the PRMIA Risk Management Challenge for 2020 and 2021!  Learn more about this and other outreach supported by QuantUniversity.

PRMIA Network Fee: $499.00 using discount code: PRMIA100
Add on Just Enough Python course for only $150 more! 

Register Now!
(You will be redirected to our training partner's registration system.)


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