2025 Applied Machine Learning for Finance Workshop
AI-driven insights for investment strategies
AUT City Campus - 6 November 2025
This hands-on workshop introduces key Machine Learning (ML) techniques tailored for financial applications. Participants will explore Natural Language Processing (NLP) methods for extracting market insights and ML-driven portfolio optimization strategies.
Workshop registration
The workshop registration deadline is 30 October 2025.
Workshop instructor
Vitali Alexeev, University of Technology Sydney, Australia, and University of Guelph, Canada
Vitali Alexeev is an Associate Professor of Finance at the University of Technology Sydney and Adjunct Professor at the University of Guelph, Canada. He holds a PhD in Economics from the University of Guelph and a Graduate Diploma in Financial Engineering from the Schulich School of Business, York University. His academic trajectory spans appointments at the University of Tasmania, University of Toronto, and City University of London, among others.
Vitali's research sits at the intersection of financial econometrics, portfolio optimisation, and machine learning. His work focuses on high-frequency data modelling, robust asset allocation under uncertainty, dynamic time warping in financial time series, and AI-driven sentiment analysis from news, social media, and images. His recent publications appear in top-tier journals including the Journal of Finance, Journal of Banking & Finance, Journal of Empirical Finance, Studies in Nonlinear Dynamics & Econometrics, and Quantitative Finance.
He is the founder of the Graduate Certificate in Applied AI for Finance at UTS and has developed and taught subjects such as AI-integrated Sustainable Finance, AI-powered Investment and Risk Management, and AI-driven Compliance, Anomaly and Fraud Detection. These offerings equip students with practical, industry-relevant AI skills, especially in the context of sustainable finance and algorithmic decision-making.
Vitali regularly delivers workshops on machine learning, NLP, high-frequency finance and sentiment analytics, and generative AI applications. He has held visiting appointments at leading institutions, including Hitotsubashi University, University of Vienna, Free University of Berlin and City University London.
He is a frequent speaker at international finance and econometrics conferences, an award-winning researcher and educator, and serves on editorial boards and grant assessment panels in finance and economics.
Workshop schedule
- 9am - 10am: Introductory Session – Python for Finance: Getting Started
- 10am - 10.30am: Morning tea
- 10.30am - 12.30pm: Session 1 – Natural Language Processing (NLP) in Finance: Sentiment Analysis and Topic Modelling
- 12.30pm - 1.30pm: Lunch
- 1.30pm - 3.30pm: Session 2 – Portfolio Construction: From Classical Methods to Machine Learning
Topics covered
Python for Finance: Getting Started
An accessible introduction to Python tailored for financial applications.
Natural Language Processing in Finance
Exploring how text data, sentiment, and topics inform financial decision-making.
Portfolio Construction: From Classical Methods to Machine Learning
From Markowitz to modern ML-driven approaches, with practical illustrations.
Who should attend?
Finance professionals, quantitative analysts, students, and researchers who want to integrate ML techniques into their workflow.
Prerequisites
No prior programming experience is required, but familiarity with financial concepts is recommended. The Introduction to Python for Finance session will provide sufficient background for those new to Python.
Participants are encouraged to register an account on Anaconda.Cloud before the workshop to run and modify interactive examples.
Contact
Email: acfr@aut.ac.nz