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
- Session: 1.5 hours/session
- Duration: 9 weeks
- Case study + Labs using the QuSandbox
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
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.
Analytics for a cause initiative:
QuantUniversity sponsors scholarships ,valued at $30,000 to their educational offerings to students from eight countries and 12 chapters, participating in the PRMIA - Professional Risk Managers' International Association Risk Management Challenge. Additional details about our announcement here
Starts Feb 1st
Number of sessions
9 Case studies + Labs using the QuSandbox
1.5 hours/session - 9 weeks
Online through QuAcademy
If you would like an invoice for your payment for reimbursement or related questions on alternative payment methods, please contact firstname.lastname@example.org
Machine Learning and AI: An intuitive Introduction
Exploratory data analysis
- Dimension reduction and visualizing datasets using PCA, T-SNE
- Manifold Learning
- Case study: Visualizing high-dimensional Datasets
Learn from the past: How does Supervised machine learning work?
Neural Networks + Synthetic Data Generation
Natural Language Processing
CAPSTONE PROJECT (OPTIONAL)
Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..