SubjectsSubjects(version: 945)
Course, academic year 2023/2024
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Automation in physics - NFPL242
Title: Automatizace ve fyzice
Guaranteed by: Department of Condensed Matter Physics (32-KFKL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2023
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:1/2, MC [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information:
Guarantor: RNDr. Petr Čermák, Ph.D.
Annotation -
Last update: Mgr. Kateřina Mikšová (10.05.2023)
The lecture will cover the role of automation and robotics in data treatment, including automatic data acquisition and analysis, machine learning, and artificial intelligence. The gained knowledge has direct application in materials science, quantum physics or research on large infrastructures. We will also touch on large language models and how to use them efficiently. In fact, this annotation and syllabus are written by ChatGPT :). Very basic knowledge of Python is expected. The course is suitable for all grades, English is possible.
Course completion requirements -
Last update: Mgr. Kateřina Mikšová (10.05.2023)

Creating a program to automate a given experimental problem.

Literature -
Last update: RNDr. Petr Čermák, Ph.D. (10.05.2023)

G.R. Bradski, A. Kaehler: Learning OpenCV, O'Reilly 2008

All resources at the web

C.E. Rasmussen, K.I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.

K.G. Reyes and B. Maruyama, MRS Bull. 44, 530 (2019)

Syllabus -
Last update: RNDr. Petr Čermák, Ph.D. (10.05.2023)

1. Instrument architecture and PLC programming

  • Communication protocols used at large scale infrastructures

2. Communication with 6-axis robotic arm

3. Computer Vision

  • Object recognition, evaluation 2D images from microscope

4. Machine learning basics

5. Artificial intelligence and expert systems

6. Automatic planning of experiments

7. Bayesian optimization of measurements

8. AI driven data analysis

9. Publishing the data

  • F.A.I.R. principles
  • Describing data by the scripts
  • Storing data for generations

10. Future trends in automation

  • Using of the Large language models for writing papers

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