It is hard to name a sector that will not be dramatically affected by machine learning or even artificial intelligence. There are many excellent courses that teach you the mechanics behind these innovations, helping you develop an engineering skill set. This course takes a different approach. It is aimed at people who want to deploy these tools, either in business or policy, whether through start-ups or within a large organization. While this requires some knowledge of how these tools work, it is only a small part of the equation – just as knowing how an engine works is a small part of understanding how to drive. What is really needed is an understanding of what these tools do well and what they do badly. This course focuses on giving you a functional, rather than mechanistic, understanding. By the end, you should be an expert at identifying ideal use-cases and thereby well-placed improve analysis and policy using machine learning.
This Florence School of Banking and Finance course thus aims to provide an overview and understanding of popular Machine Learning (ML) techniques, understand the opportunities and limitations of these techniques and be able to interact with the experts. In practical terms you will work hands-on with ML/AI methods in Python, demystify the black boxes and prime you so that you can continue to learn by yourself.
During this course you will learn how to:
Use popular Machine Learning (ML) and Artificial Intelligence (AI) techniques
Understand the opportunities and limitations of these techniques
Be able to interact with the experts
Work hands-on with ML/AI methods in Python
“Demystify” the black box of ML/AI
Continue to learn by yourself
Popular Machine Learning (ML ) and Artificial Intelligence (AI) Techniques
Opportunities and Limitations of ML and AI
AI and ML methods in Python and practical exercises
A master or PhD in any discipline that works with data. The main example used will be using credit risk data.
Basic knowledge of traditional econometric methods (OLS, panel and time-series models) is assumed.
The tutorials use Python running in a Jupyter Notebooks on Google Colab. This is browser based so no software installation need. A good internet connection is required. The material will also be distributed through Gitlab.
Knowledge of Python is helpful but not is required.
Policy makers and analysts engaged in data-driven policy making.
We expect the participants to be familiar with basic data manipulation tasks (e.g. Excel) and have a working knowledge of linear regressions, that is to be able to run OLS in its variations and interpret the results.
The tutorials use Python in Jupyter Notebooks on Google Colab. This is browser based so no installation need. Knowledge of Python is helpful but not required. Basic coding skills and familiarity with Python is recommended. For a tutorial, see this here (link).
Please notice that the course dinner, and most of the social activities, will take place downtown.
Florence airport is located 8 km from the city centre, approximately 30 minutes by taxi or bus. Taxis can be found outside the arrivals terminal; no reservation is needed. A taxi ride from the airport costs about €20 and takes approximately 25/30 minutes.
A tramway (line T2) connects the airport to the city centre. Trains leave from the airport terminal and take 20 minutes to the main railway station. One-way tickets can be bought from vending machines for €1.50.
Bus tickets are sold outside the railway station, at Autolinee Toscane ticket kiosks and vending machines, tobacconists (tabacchi), newspaper kiosks (edicole), and most cafes (bar). They must be bought before boarding and stamped using the machine on the bus. A ticket costs €1.50 and it is valid for 90 minutes. Bus tickets can be purchased also on board (€ 2.50), but the driver is not obliged to give change.
From the A1 Milano-Napoli (Autostrada del Sole), take the Firenze Sud exit and follow directions to the city centre/Stadio. Follow the directions to the stadium (Stadio), then for Fiesole. San Domenico is on the main road to Fiesole.
The EUI has several free parking areas available all over the Campus.
Early bird fees* apply until 28 March 2022 – Registration deadline 16 May 2022
Early bird € 1575 (Standard fee: € 1750) – Public Authorities (e.g. National Competent Authorities, Central Banks) and European Institutions
€ 950 (No early bird) –Full-Time Professors, PhD Students, Research Associates
Please submit a certificate attesting your status of Professor, PhD Student or Research Associate to firstname.lastname@example.org before registering. FBF secretariat will provide you with a code to register. *Seats for academics are limited.
* Please note that to qualify for early bird rates, registration and payment must be processed by the deadline of the early bird.
Please note that the payment must be settled one week before the start of the course.
The fee includes tuition, access to all course materials and pedagogic activities, coffee and lunch breaks and social activities. It does not include travel and accommodation expenses or other local transportation costs (taxis, private cars).
Limited seats per institution
A certificate of attendance will be provided to all participants after the course.
In case you can no longer attend the course, you are required to inform the organisers by sending an email to email@example.com in order to free a seat for participants in the waiting list.
In case of frequent cancellations, FBF reserves the right not to accept further registrations from the same person.