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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
Additional information:	https://cermak.science/teaching/automation/

Guarantor:	RNDr. Petr Čermák, Ph.D.
Teacher(s):	RNDr. Petr Čermák, Ph.D.

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

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.

Last update: Mikšová Kateřina, Mgr. (10.05.2023)

Course completion requirements -

Creating a program to automate a given experimental problem.

Last update: Mikšová Kateřina, Mgr. (10.05.2023)

Literature -

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

All resources at the web autonomous-discovery.lbl.gov

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)

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

Syllabus -

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

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