Machine Learning and AI for Financial Professionals

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 to use Machine learning and AI techniques
• The key machine learning methodologies
• How to choose and algorithm for a specific goal
• Practical case studies with fully functional code

Sessions are pre-recorded with interactive videos, slides, demos, and fully functional code through Qu.Academy.

Prerequisite:  Participants are expected to have a working knowledge of Python. Please consider taking the Just Enough Python for Data Science in Finance  if you don’t know Python.

This course is a part of the QuantUniversity Machine Learning and AI Risk Certificate Program.

 Take advantage of additional discounts by enrolling in the Certificate program.

 Machine Learning and AI: An intuitive Introduction

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

 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

 Exploratory Learning: Visualization & Case Study

• Dimension reduction and visualizing datasets using PCA, T-SNE
• Manifold Learning
• Case study 1: Visualizing high-dimensional Datasets

 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

 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

 Neural Networks + Synthetic Data Generation

• Introduction to Neural Networks and Deep Neural Networks
• Case study 4: Synthetic Data Generation for VIX Scenarios

 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 

 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
  • Anomaly detection; Reinforcement learning; Quantum Computing; Risk in Machine Learning and AI; Model governance, Interpretability and Model Management

• 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

Sri Krishnamurthy, CFA, CAP is the founder of QuantUniversity, a data and quantitative analysis company. His experience includes 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 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.

Sri is a Charted Financial Analyst and a Certified Analytics Professional.  He is an active member of the Boston Security Analysts society and QWAFAFEW. He 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.