APPLICATIONS OF ARTIFICIAL INTELLIGENCE - FINANCE AND LAW
APPLICAZIONI DELL'INTELLIGENZA ARTIFICIALE - FINANZA E DIRITTO
| A.Y. | Credits |
|---|---|
| 2023/2024 | 3 |
| Lecturer | Office hours for students | |
|---|---|---|
| Stefano Ferretti |
Assigned to the Degree Course
| Date | Time | Classroom / Location |
|---|
| Date | Time | Classroom / Location |
|---|
Learning Objectives
The course aims to introduce the student to recent advances and the application of deep learning. To this end, case studies from the fields of economics, finance and law will be introduced.
Program
01. Libraries for machine learning
01.01 Python and Scikit Learn
01.02 Tensorflow and pyTorch
02. Deep Learning
02.01 Recurrent Neural Networks
02.02 Convolutional Neural Networks
02.03 Applications in the fintech domain
03. Neural Networks on Graphs
03.01 Introductions to complex networks
03.02 Graph Neural Networks
03.03 Application in the Law and Financial domains, e.g., Anti Money Laundering in cryptocurrencies
Bridging Courses
machine learning
Learning Achievements (Dublin Descriptors)
Knowledge and Understanding: Upon completion of this course, the student understands the fundamental ideas, recent advances, and application potential of deep neural systems. The student understands basic neural topologies, methods for visualizing and understanding the behavior of neural networks, recurrent networks, and networks on graphs. The student is able to apply these technologies to solving classification problems in realistic domains.
Ability to apply knowledge and understanding: the student will be able to apply acquired knowledge with the goal of designing machine learning systems for classification and prediction.
Autonomy of judgment: the student will be able to evaluate the efficiency of a learning system.
Communication skills: the student will be able to appropriately explain the descriptive features of neural network system, the functionality of the related system..Learning skills: the student will learn the ability to design and implement machine learning systems.
Teaching Material
The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it
Teaching, Attendance, Course Books and Assessment
- Teaching
Lectures and lab
- Attendance
Although strongly recommended, attendance is not mandatory.
- Assessment
Project
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
Additional Information for Non-Attending Students
- Teaching
Lectures and lab
- Attendance
Not required
- Assessment
Project
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
| « back | Last update: 31/08/2023 |
