29 August 2020
Machine Learning for Financial Dataonline course
This course will be given online.
Stay home. Learn together.
Financial data analytics are in the focus of interest due to a number of reasons. For example, predictions of vector time series underwent a huge progress due to the deep learning technologies. This is the direction of the stock market. Another direction is to consider the underlying networks. The diverse trees of production lines form such networks; packaging industry depends on miniaturization, the price of steel depends on the price of a number of components, such as oil, iron, chemicals as well as many others. The web of prices and products influences prices in diverse ways and it would be interesting to look at such dependencies. Pattern mining (item sets, association rules, frequent graphs, etc.) or recommendation techniques represent other interesting directions. In turn, financial data analytics have several challenges, such as traditional and deep learning technologies on financial data, data and pattern mining on the web of dependencies, and data visualization for the sake of human-AI interaction and enhanced user experience.
The school will provide an introduction to the benefits and best practices of machine learning and deep learning in the financial sector. Data and pattern mining on the web of special transactions-like data as well as visualization techniques will also be presented.
Students will work on business assignments for innovative use of machine learning in cooperation with companies and start-ups in the Budapest region.
This programme is organised by EIT Digital Summer School and Eötvös Loránd University (ELTE), Hungary.
We welcome all qualified participants interested in digital innovation and entrepreneurship: Business professionals, bachelor’s degree students, master’s degree students, and doctoral students from any university and industry.
Acceptance of transfer credit is always a decision of receiving institutions. Any student interested in transferring 4 ECTS credits to another college or university should check directly with the receiving institution.
EUR 800: To do our small part to help you make the most of your summer, we have reduced the programme fee from 2,450 EUR to the new attractive and competitive rate of 800 EUR.