20902 - FINTECH AND MACHINE LEARNING FOR FINANCE
Department of Finance
Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP | SECS-P/11) - M (6 credits - II sem. - OP | SECS-P/11) - IM (6 credits - II sem. - OP | SECS-P/11) - MM (6 credits - II sem. - OP | SECS-P/11) - AFC (6 credits - II sem. - OP | SECS-P/11) - CLELI (6 credits - II sem. - OP | SECS-P/11) - ACME (6 credits - II sem. - OP | SECS-P/11) - DES-ESS (6 credits - II sem. - OP | SECS-P/11) - EMIT (6 credits - II sem. - OP | SECS-P/11) - GIO (6 credits - II sem. - OP | SECS-P/11) - PPA (6 credits - II sem. - OP | SECS-P/11) - FIN (6 credits - II sem. - OP | SECS-P/11) - AI (6 credits - II sem. - OP | SECS-P/11)
Course Director:
FRANCESCO CORIELLI
FRANCESCO CORIELLI
Suggested background knowledge
This course is based on a mix of Statistics/Econometrics and coding
The following abilities would be very useful:
Knowledge of Statistics beyond the basic Statistics courses (e.g. a course in Econometrics)
Knowledge of matrix notation and elementary matrix algebra
Some ability with VBA and some knowledge of Python possibly as implemented in Google Collaborate
A general knowledge of financial markets, in particular the stock market, and traded financial securities
Mission & Content Summary
MISSION
This course is designed to introduce students to the applications of machine learning (ML) in finance and how fintech companies are using ML to create new financial products and services.
The course will cover the fundamental concepts of ML, including supervised and unsupervised learning, neural networks, deep learning, and natural language processing.
Students will learn how to apply these concepts to financial data and build predictive models to support financial decision-making.
CONTENT SUMMARY
—Supervised and unsupervised learning
—Regression and classification
—Evaluation metrics
—Introduction to deep learning
—Neural networks
—Convolutional neural networks
—Recurrent neural networks
—Introduction to NLP
—Credit scoring and risk assessment
—Fraud detection
—Algorithmic trading
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
-understand the fundamentals of machine learning and its applications in finance
-understand how to specify and estimate ML models
-understand how to evaluate the performance of ML models
-understand the latest trends and developments in the interaction between fintech and machine learning in finance
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
-analyze financial data using machine learning techniques
-build basic predictive models to support financial decision-making
-evaluate the ethical and regulatory implications of using machine learning in finance
Teaching methods
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Case studies /Incidents (traditional, online)
- Individual assignments
DETAILS
—Each topic in the course shall be first introduced with a traditional lecture and then described in detail with a mix of case studies and computer work
—Computer work shall be based on ML libraries for Python implemented in Google Collaborate
Assessment methods
| Continuous assessment | Partial exams | General exam | |
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ATTENDING AND NOT ATTENDING STUDENTS
—80% final written exam
—20% class partecipation and homeworks
Note: this is still provisional. Being this a new course some (minor) change in the exam setup is possible.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
Textbook (to be defined and communicated before the course begins)
Software, slides and handouts provided by the teacher
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