Course title: 
Data Science Aided Measurements
Primary programme: 
Fizikus mérnök BSc
ECTS credits: 
Course type: 
Number of lectures per week: 
Number of practices per week: 
Number of laboratory exercises per week: 
Further knowledge transfer methods: 
Laboratory exercises
Coursework grade
Special grading methods: 
The competence is tested prior to the measurements, and laboratory reports are submitted after the measurements. The grade is based on both aspects.
Introduction to Machine Learning, Measurement Techniques Laboratory
Responsible lecturer: 
Dr. Gergő Fülöp, research fellow, PhD
Lecturers and instructors: 
Course description: 
Advanced data processing algorithms have become widespread both in industrial applications and research laboratories. In this laboratory course the students gain hands-on experience in such modern data science methods through various practical examples. In each laboratory exercise, the students carry out a complex experimental project end-to-end. Their tasks include - setting up and performing the experiment, - evaluating the experimental data, - analyzing the data collection and evaluation pipeline, - optimizing the data acquisition parameters, -evaluation of the experiemtal data with the toolbox of data science - exploring the performance and limitations of the methods, - compiling a written report.
Reading materials: 
Descriptions of laboratory exercises on the website of the BME Physics Institute.
List of competences: 
Please find the detailed list, as quoted from the Hungarian training and outcome requirements of the Physicist Engineer program, in the Hungarian version of the course description.