Course: Introduction to Python for AI

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Course title Introduction to Python for AI
Course code UAI/324
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Frequency of the course In each academic year, in the winter semester.
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Vohnoutová Marta, Ing.
  • Symonová Radka, doc. Mgr. Ph.D.
Course content
The aim of this course is to give an introduction to the Python programming language. The main features of this language and the principles of programming in it are discussed. The student will be able to algorithmize the problem and implement it in Python. Python basics 1. Python properties, installation and Python environment 2. Variables and identifiers, assignments, expressions 3. Operators, conditional statement 4. Cycles, functions 5. Lists, work with lists 6. Strings, working with strings 7. Multidimensional lists, dictionaries 8. Files 9. N-mer 10. Formatted output 11. Error handling 12. Python in practice, adding libraries Object Python: 1. Basics of object programming in Python Basic libraries for applied Python: 1. Itertools 2. Numpy 3. Scipy 4. Pandas 5. Matplotlib Jupyter - environment for scientists

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Practical training
  • Class attendance - 56 hours per semester
  • Preparation for exam - 25 hours per semester
  • Semestral paper - 45 hours per semester
Learning outcomes
The aim of this course is to give an introduction to the Python programming language.
The student is able to program and to apply the programs written in Python and similar. He knows how to use appropriate libraries supplied together with the implementations of development environment for Python. The student can design own algorithms and create his/her own programs, debug them and test them.
Prerequisites
Basic knowledge of the Linux operating system and knowledge to create algorithms is expected. Knowledge of Python or other programming languages is a plus but is not necessary.

Assessment methods and criteria
Seminar work, Interim evaluation

Attendance at seminars at least 70%. Independent work and its defense. Oral exam.
Recommended literature
  • Chollet, F. Deeplearning v jazyku Python. Grada Publishing 2019. 328 s.. 2019. ISBN 978-80-247-3100-1.
  • Pecinovský, Rudolf. Python : kompletní příručka jazyka pro verzi 3.9. První vydání. Praha : Grada Publishing, 2020. ISBN 978-80-271-1269-2.
  • REMEŠ, R. Programujeme v jazyku Python. České Budějovice: Jihočeská univerzita, 2008. ISBN 9788073941284.
  • SUMMERFIELD, M. Python 3: Výukový kurz. Brno: Computer Press, 2012. ISBN 978-80-251-2737-7.
  • VAN ROSSSUM, Guido. An Introduction to Python. Network Theory, 2018. ISBN 978-1906966133.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester