Machine learning: Tools and applications for policy
06 – 08 June 2022
REGISTRATION DEADLINE: 26 MAY 2022Download the programme
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.
What you will learn
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
Meet the Instructors
Iman van Lelyveld heads the Data Science Hub at DNB and is Professor of Banking and Financial Markets at the Finance Group of the VU Amsterdam. He has been involved in many regulatory policy issues and the BCBS Research Task Force – chairing several groups. He has published widely on international banking and financial networks and has worked for Deutsche Bank, the Bank of England, and the International Data Hub at the Bank for International Settlements (BIS). At the BIS he helped to setup analysis of the exposure network of the largest banks in the world. In the last few years he has been busy with setting up a Data Science Hub (DSH) at De Nederlandsche Bank, the Dutch central bank and prudential supervisor.
Dieter Wang is a financial econometrician/data scientist with a PhD from the VU Amsterdam and Tinbergen Institute. He has extensive experience on tackling real world policy problems, by applying econometrics and machine learning to various datasets, ranging from financial Bloomberg data to geospatial satellite data. He’s passionate about getting key insights across with visualizations that tell a story. During his time, he has worked with international organizations, central banks, global tech companies, major reinsurances, investment banks and a space agency. He held visiting positions at Columbia Business School and the Dutch Central Bank.
Thorsten Beck (Scientific Advisor)
Pierre Schlosser (Scientific Advisor)
- 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.
Recommended hotels nearby the EUI:
Recommended hotels in downtown Florence:
Suggested restaurants in Florence city centre
- Restaurant Accademia – Ph. +39 055 217343
- Restaurant Cucina Torcicoda – Ph. +39 055 265 4329
- Finisterrae – Ph. +39 0552638675
- Il Vezzo – Ph. +39 055 281096
- Osteria di Giovanni – Ph. + 39 055 284897
On arrival, participants will be provided with temporary wi-fi access for the whole duration of the course.
For general queries: firstname.lastname@example.org
GENERAL INFORMATION ON LOCAL TRANSPORT
From Florence airport:
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.
From the central railway station:
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: € 1755 (Standard fee: € 1950) – Private Sector
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 email@example.com 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 firstname.lastname@example.org 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.
For more details, please contact email@example.com