95661 - ADVANCED MACHINE LEARNING

Conoscenze e abilità da conseguire

The Advanced ML course is geared towards state of the art application of neural network to pricing and market risk problem. The studend will acquire a sound knowledge of the principles underlying Neural Networks and will be guided in a tour of the relevant literature concerning the exploitation of machine learning for pricing of highly exotic products and applications to market risk managment. Altough the approach demands very large scale computing facilities, impossible to be provided to the students, nonetheless students will learn how to design solutions to this type of problem and will gain hands on experience of the methodology on simpler and smaller toy models.

Contenuti

Course Short Description

This course explores the application of deep learning techniques to financial modeling. Students will learn how to design, implement, and evaluate neural networks to solve key problems in pricing, representation, and risk modeling. Emphasis is placed on hands-on programming and a deep understanding of model mechanics.

Lesson Plan

> Fundamentals of Deep Learning

Perceptron and logistic regression

Loss functions: cross-entropy and log-likelihood

Gradient descent and backpropagation

Feedforward architectures

Hands-on: Implementing a neural classifier from scratch

Objective: Understand and code the building blocks of neural networks

> Derivatives Pricing with Deep Learning

Brief overview of financial options: European, American, exotic

Black-Scholes model: assumptions and limitations

Volatility surface and implied volatility

Deep learning approaches to pricing

Hands-on: Build a neural network to price options using Keras

Objective: Apply neural networks to solve pricing problems

> Representation Learning in Finance

Representation learning: definitions and importance

PCA and Autoencoders

Self-supervised learning paradigms

Applications to financial time series and cross-section

Hands-on: Use Autoencoders for dimensionality reduction

Objective: Learn to extract structured representations from financial data

> Generative Models and Financial Applications

Introduction to Generative AI (GANs, VAEs – conceptual level)

Synthetic data generation and its uses

Using generative models to detect arbitrage and anomalies

Challenges of generative modeling in finance

Objective: Understand how generative models can support financial applications

Testi/Bibliografia

Here is a short bibliography based on the course program:

1. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- A comprehensive guide to machine learning techniques, with a focus on practical implementations using Python, Scikit-Learn, Keras, and TensorFlow.

2. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- A foundational textbook for deep learning, covering both theoretical and practical aspects, including neural networks and advanced models.

3. "Machine Learning for Asset Managers" by Marcos López de Prado
- A specialized book that applies machine learning techniques specifically to asset management, focusing on real-world financial data and models.

4. "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
- A classic text on reinforcement learning, providing both the theoretical background and practical applications of RL in various fields, including finance.

5. "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper
- This book introduces essential NLP techniques and tools using Python, particularly useful for analyzing financial texts and performing sentiment analysis.

These texts will support the deeper understanding of machine learning and its applications in finance, as outlined in the course.

Metodi didattici

The course employs a blended learning approach that combines theoretical instruction with practical hands-on experience. The didactical methods can be summarized as follows:

1. Theoretical Lectures:
- Each topic begins with a comprehensive lecture that provides the necessary theoretical background. These sessions introduce key concepts, algorithms, and their relevance to finance, ensuring that students understand the principles behind machine learning models and techniques.
- Theoretical lectures cover both foundational topics (e.g., machine learning basics, financial data structures) and advanced subjects (e.g., deep learning, reinforcement learning, and NLP).

2. Practical Examples Using Python:
- Following each theoretical session, students engage in practical exercises using Python. These hands-on sessions allow students to apply the concepts learned in real-world financial contexts, such as stock price prediction, sentiment analysis, and trading strategies.
- The exercises use popular Python libraries like Pandas, Scikit-learn, TensorFlow, and Keras to demonstrate data processing, model building, and evaluation techniques.
- The integration of Python into the learning process is essential, as it provides students with practical coding experience and deepens their understanding of how to implement machine learning models effectively.

The combination of theory and practice ensures that students not only grasp the academic aspects of machine learning but also gain the technical skills necessary to apply these methods in their financial careers.

Modalità di verifica e valutazione dell'apprendimento

The course concludes with a final practical project, which plays a key role in assessing the students' ability to apply the learned concepts. Students are required to develop a machine learning project related to finance, which will be discussed on the day of the examination.

Key details regarding the project:

- Project Approval: The project must be proposed and approved by the instructor at least one week before the examination date. This ensures that the chosen topic aligns with the course objectives and allows for timely feedback.

- Examination Discussion: During the final exam, students will present and discuss their project, demonstrating their understanding of the applied techniques, the financial data used, and the results obtained.

This approach integrates both theoretical knowledge and practical skills, encouraging students to independently explore machine learning applications in finance.

Strumenti a supporto della didattica

The course utilizes a variety of teaching tools designed to enhance both theoretical understanding and practical application of machine learning techniques in finance. The main tools used throughout the course include:

1. Python Programs:
- Python is the primary programming language used for all practical exercises. Students will work with key libraries such as Pandas, Scikit-Learn, TensorFlow, and Keras to implement machine learning models. Python enables students to handle financial data, build predictive models, and apply advanced machine learning techniques in real-world financial scenarios.

2. Jupyter Notebooks:
- All practical sessions are conducted using Jupyter Notebooks, an interactive environment that allows students to write Python code alongside explanatory text and visualizations. This tool is essential for experimenting with different machine learning models, performing data analysis, and documenting results. It fosters an exploratory learning style where students can visualize the output of their code in real-time.

3. Lecture Notes:
- Detailed lecture notes accompany each theoretical session. These notes provide a structured overview of key concepts, algorithms, and case studies discussed during lectures. They are designed to serve as a reference for students as they work through both the theoretical and practical aspects of the course.

4. Data Files:
- All relevant data files required for practical sessions are provided in advance. These datasets cover various financial data types (e.g., time series, stock prices, sentiment analysis data) and are used to practice machine learning tasks such as regression, classification, and clustering.

Accessibility of Didactical Materials:
- All course materials, including lecture notes, data files, and Jupyter Notebooks, will be made available in advance through the university’s online platform for didactical materials.

Orario di ricevimento

Consulta il sito web di Giovanni Della Lunga