MODELS AND TECHNOLOGY FOR THE FINANCIAL INDUSTRY
- Academic year
- 2024/2025 Syllabus of previous years
- Official course title
- MODELS AND TECHNOLOGY FOR THE FINANCIAL INDUSTRY
- Course code
- EM1414 (AF:449563 AR:257637)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Master's Degree Programme (DM270)
- Educational sector code
- SECS-P/02
- Period
- 3rd Term
- Course year
- 2
- Where
- VENEZIA
- Moodle
- Go to Moodle page
Contribution of the course to the overall degree programme goals
The teaching is one of the characterizing courses of the Master's Degree course in DATA ANALYTICS FOR BUSINESS AND SOCIETY and reviews the machine learning techniques applied to the financial industry.
The course focuses on understanding the most common algorithms and tools used by data scientists in finance.
During the course, practical cases will be discussed in order to apply the acquired knowledge. Python will be used.
The course focuses on understanding the most common algorithms and tools used by data scientists in finance.
During the course, practical cases will be discussed in order to apply the acquired knowledge. Python will be used.
Expected learning outcomes
1. Knowledge and comprehension
- study and understanding of the data available in the financial industry
- review of the common machine learning techniques applied to the financial industry
2. Application of Knowledge and comprehension
- Identify and apply the machine learning techniques to the analyzed problem
- Analysis of practical cases with the use of Python
3. Judgment skills
- Validation and testing of the model
- study and understanding of the data available in the financial industry
- review of the common machine learning techniques applied to the financial industry
2. Application of Knowledge and comprehension
- Identify and apply the machine learning techniques to the analyzed problem
- Analysis of practical cases with the use of Python
3. Judgment skills
- Validation and testing of the model
Pre-requirements
Follow the requirements indicated by the Master's Degree course in DATA ANALYTICS FOR BUSINESS AND SOCIETY
Contents
Introduction to Machine Learning in the financial industry
Unsupervised learning
Supervised learning and regularization (Linear and Logistic regression, survival analysis, and support vector machines)
Financial Portfolio and regularization
Unsupervised learning
Supervised learning and regularization (Linear and Logistic regression, survival analysis, and support vector machines)
Financial Portfolio and regularization
Referral texts
Written material will be provided on the Moodle course linked with the lessons.
Suggested reading: Hull, J. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Third Edition. Amazon Distribution.
Suggested reading: Hull, J. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Third Edition. Amazon Distribution.
Assessment methods
For attendees: The learning assessment consists of a written part (open questions) and an empirical analysis carried out in a group or individually.
For non-attendees: Written exam.
For non-attendees: Written exam.
Teaching methods
Lecture. Slides, coding activities, exercises and examples available on Moodle platform.
Teaching language
English
Type of exam
written
2030 Agenda for Sustainable Development Goals
This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development
Definitive programme.
Last update of the programme: 21/03/2024