Programming in Python 2

Course guarantor:

Jakub Klinkovský

Lecturer:

Jakub Klinkovský, Rudolf Pecinovský

Tutor:

Jakub Klinkovský, Rudolf Pecinovský

Supervisor:

Department of Software Engineering

Synopsis:

This course introduces students to practical applications of the Python language in scientific as well as commercial fields. The course is a seminar where each presented topic is accompanied by a short demo of a real-world application in the specific field.

Requirements:

Syllabus of lectures:

1.Interaction between Python and C++

2.Parallel and distributed computing with Python

3.Using the principles of object-oriented programming in heuristic optimization

4.Using the Jupyter environment for scientific research while respecting the principles of software development, including code review and versioning

5.Using Python for data processing in the cloud (Amazon Web Services)

6.Processing and statistical analysis of big data in distributed systems

7.Introduction to machine learning (basic concepts, introduction to the Scikit-Learn library)

8.Introduction to the design of neural networks using the Keras library

9.Using machine learning in practice: from statistical models to artificial intelligence

10.Integration of trained models in deployed systems and applications

11.Practical examples of using linear and quadratic programming for economic optimization of energy storage

12.Practical examples from the control theory (design of simple control for water turbine, frequency characteristics and stability of the system)

13.Design of web applications using the Django framework

Syllabus of tutorials:

Study Objective:

Study materials:

Recommended literature:

[1] P. Wentworth, J. Elkner, A.B. Downey, C. Meyers, How to Think Like a Computer Scientist: Learning with Python, 2nd Edition, Green Tea Press, 2012. ISBN 9781491939369. https://www.ict.ru.ac.za/Resources/cspw/thinkcspy3/

[2] E. Smith, Introduction to the Tools of Scientific Computing, 2nd Edition, Springer, 2022. ISBN 978-3-031-16972-4.

[3] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3nd Edition, O'Reilly Media, 2022. ISBN 9781098125974.

Note:

Time-table for winter semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon	roomTR:101 Klinkovský J. 16:00–17:50 (lecture parallel1) Trojanova 13
Tue
Wed
Thu
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Aplikace informatiky v přírodních vědách (elective course)
• Aplikované matematicko-stochastické metody (compulsory elective course, elective course)
• Aplikovaná informatika (elective course)
• Fyzikální inženýrství - Inženýrství pevných látek (elective course)