The homework is mandatory, which means that you have to perform some data analysis, summarize its result in a report in 5-10 pages (submit it in PDF) and present your work on the last lecture in 5+5 minutes.

A guide for the selection of homework:

PhD related homework

If your MSc/PhD research is related to topics covered at the lecture, then please send me an e-mail (antal at mit.bme.hu) indicating the relevant topic and briefly your intended task (keywords are enough).

Topics with lecturers:

1, Visual data analytics, exploratory data analytics: Prof. András Pataricza/László Gönczy

2, Classification and regression with linear models and classical neural networks (e.g. shallow MLPs): Prof. Tadeusz Dobrowiecki

3, Probabilistic graphical models and deep neural networks: Péter Antal

Default homework

If your MSc/PhD research is not related to these topics or data analysis, then please indicate it and as a default homework I propose a model class in the scikit-learn library to explore using a standard dataset.

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