Course 2024-2025 a.y.

20902 - FINTECH AND MACHINE LEARNING FOR FINANCE

Department of Finance

Course taught in English

Class timetable
Exam timetable
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

Classes: 31 (II sem.)
Instructors:
Class 31: 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 and neural networks

—Autoencoders and dimension reduction

—Convolutional neural networks

—Recurrent neural networks

—A selection of examples from:

  1. Credit scoring and risk assessment
  2. Fraud detection
  3. 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
 


Teaching methods

  • Practical Exercises
  • Individual works / 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
  • Written individual exam (traditional/online)
     x
  • Takehome Assignment
     x

ATTENDING AND NOT ATTENDING STUDENTS

—80% final written exam

—20% takehome assignment

 


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

Textbook (to be defined and communicated at the beginning of the course)

Software, slides and handouts provided by the teacher

 

Last change 02/12/2024 16:29