1. Nottingham Business School, Nottingham Trent University

1.1. "Contemporary Topics in Digital Finance" 2024/2025

This year (2024/2025) I was appointed to develop and deliver the second  half of a new module titled “Contemporary Topics in Digital Finance” on the postgraduate level.  I did this job by myself, given my knowledge and industry experience, with no help provided (though I wasn't  a module leader).

Check the Github Repository for the material (lecture slides) for the  “Contemporary Topics  in Digital Finance" - Part 2  (2024/2025) module:

• Topic 1. Central Bank Digital Currencies (CBDCs) ;

• Topic 2. Carbon Footprint of Artificial Intelligence (AI), FinTech and Crypto Industry ; 

• Topic 3. Cybersecurity in the Modern World .

1.2. “Principles of Machine Learning in Finance” 2024/2025

This year  (2024/2025) I was appointed to develop and deliver a new module titled “Principles of Machine Learning in Finance” on the postgraduate level.  I did this job by myself, given my knowledge and industry experience, with no help provided. As a module leader, I decided to create a teaching module based on experiential education where students could learn both underlying theoretical concepts, including maths and stats, and advanced coding with Python during each session.

Check the Github Repository for the material (lecture slides and coding lab answers) for the  “Principles of Machine Learning in Finance” (2024/2025) module:

• Topic 1. Types of Machine Learning (ML) | Feature Engineering :

  • Lab 1. Feature Engineering with Python | [ European Bank Data Modelling] ;

• Topic 2. Supervised Machine Learning (ML) | Regression | Linear Regression : 

  • Lab 2. Regression Model for a Financial Dataset | [Stock Price Prediction with Python] ;

• Topic 3. Supervised Machine Learning (ML) | Classification | Logistic Regression : 

  • Lab 3. Binomial Logistic Regression with Python ;

• Topic 4. Supervised Machine Learning (ML) | Classification | Naive Bayes : 

  • Lab 4. Naive Bayes Model with Python ;

• Topic 5. Unsupervised Machine Learning (ML) | Clustering | K-Means : 

  • Lab 5.1. K-Means Model for Colour Compression with Python (Non-Synthetic Data)  ;

  • Lab 5.2. K-Means: Inertia and Silhouette Score with Python (Synthetic Data) ;

• Topic 6. Advanced Supervised Machine Learning (ML) | Tree-based Learning : 

  • Lab 6.1. Decision Tree  with Python | [European Bank Data Modelling] ;

  • Lab 6.2. Random Forest  with Python | [European Bank Data Modelling] .

Module Overview

Our Module serves to deliver an up-to-date knowledge of machine learning in finance, both theoretical and practical. It aims to become a starting point for your journey as a data professional.

This module incorporates elements of experiential learning where you’ll be able to apply an acquired knowledge of machine learning models to empirical financial implications with Python. We’ll use real-world data along with synthetic data and world-known public databases like Kaggle sufficiently applied by data professionals to train, test and evaluate machine learning models.

In addition, we’ll rely on an essential Python library for machine learning Scikit-Learn and several packages, modules, and libraries needed for data analysis and visualisation.

It requires basic knowledge of Python coding and statistics but is calibrated to deliver all the essentials you need to succeed in this module.

Welcome to Principles of Machine Learning in Finance - 2024/2025!

In this module, we’ll embark on an interdisciplinary journey to dive into the field of machine learning along with its primary application in finance. We’ll learn about different types of machine learning (ML), their conceptual relation to artificial intelligence (AI), and underlying models of both supervised and unsupervised learning. The last but not the least. We’ll implement and enhance your knowledge of Python programming language while constructing and evaluating machine learning models to solve financial problems with such tasks as prediction, classification and clustering.

Looking forward to working with you all!

1.3. Courses developed and taught in 2023-2025

• Principles of Machine Learning in Finance (PG); 

• Global Financial Markets (PG); 

• Contemporary Topics in Digital Finance (PG); 

• Investment and Portfolio Management (UG, PG); 

• Research Project / Applied Business Research Project (UG); 

• Accounting and Finance for Managers (UG); 

• Consultancy Experience Project (PG); 

• Business Research Project (PG)

2. Saint Petersburg School of Economics and Management, HSE University

2.1. Courses developed and taught in 2016-2018

2.1. I developed and taught my author course on International Banking for undergraduate students that received the 2016/2017 and 2017/2018 “Best Course in the Module” Awards.

2.2. I received 2016/2017 and 2017/2018 “Best Teacher” Awards for the courses I taught both on undergraduate and postgraduate levels (incl. ERASMUS Program in Finance students):

• Financial Markets and Institutions (PG); 

• International Banking (UG); 

• Introduction to Financial Management (PG);

• Corporate Finance (UG); 

• Theory of Finance (UG);

• Mergers and Acquisitions (PG);

• Research Seminar in Financial Economics (UG).