Description:

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.

Contents:

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

Recommended literature:

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.

Keywords:

Practical applications of the Python language, scientific computing, data processing, machine learning, artificial intelligence.

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar