Course title:
Data Science Aided Measurements
Primary programme:
Fizikus mérnök BSc
ECTS credits:
3
Course type:
elective
Number of lectures per week:
0
Number of practices per week:
0
Number of laboratory exercises per week:
2
Further knowledge transfer methods:
Laboratory exercises
Grading:
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.
Semester:
6
Prerequisites:
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.