Machine Learning in Macroeconomics by Department of Economics, University of Oxford

This is an intensive three-day course, held in the Department of Economics, Oxford. 

TOPICS COVERED:
– Why Machine Learning in Economics?
– Deep Learning
– Reinforcement Learning
– Empirical methods in machine learning

WHO CAN ATTEND?
The course is open to students and practitioners in macroeconomics and aims to introduce participants to Machine Learning in Macroeconomics.

ANY PRIOR REQUIREMENTS?
– No established knowledge in computing is required since the course will cover the basics and revision material will be offered in advance.
– However, familiarity with a scripting language such as Matlab, R, Stata, or Python would be an advantage.
– Participants are required to bring their own laptop equipped with MatLab and Julia.

FEES:
– Professionals: £1500
– Students and Academics: £950
Please note we can only accept payment for the summer school via AMEX, MasterCard, Visa credit card, Maestro or Visa debit card.

APPLICATION PROCESS:
Once your application has been submitted, it will be reviewed by the course tutors. Please ensure that you provide all the necessary information to help us process your application as quickly as possible.

You will then receive an email notification to confirm whether or not you have been accepted onto the course. 

For those that have been offered a place, you will receive the link to pay the tuition fee via a secure portal. Please note we require payment up until the 10th of June to confirm your place. 

Once payment has been received, you will receive confirmation of your place on the course(s), and further information so you have everything you need.

ACCOMMODATION:
Participants will be required to source their own accommodation arrangements. Please contact [email protected] if you require any help with this. 

GENERAL ENQUIRIES: 
We would be delighted to answer your questions about our Summer School. For general queries, including payment and application, please email [email protected]

For more details, click here

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