Lectures

Date	Lecture	Topic	Slides	Code
18.02.2026	1	Basic data structures - arrays, sets, dictionaries, algorithmic complexity motivation	Lecture 1	Notebook 1
25.02.2026	2	2D arrays (matrices), array processing (search, cumulative sum)	Lecture 2	Notebook 2
04.03.2026	3	Strings, text processing, string edit distance, files	Lecture 3	Notebook 3
11.03.2026	4	Numpy arrays	Lecture 4	Notebook 4
18.03.2026	5	Array processing II (sliding window, sorting, convolution, sparse matrices)	Lecture 5	Notebook 5
25.03.2026	6	ADT - trees, stacks, queues	Lecture 6 Example	Notebook 6 Example
01.04.2026	7	Refresher lecture (preparation for midterm)		Midterm examples
08.04.2026	8	Trees and recursion I	Lecture 7	Notebook 7
15.04.2026	9	Trees and recursion II	Lecture 8 Tree examples	Notebook 8
22.04.2026	10	OOP, PyGame	Lecture 9 pygame demo	Notebook 9
29.04.2026	11	Scientific Python libraries - data loading, processing and visualization	Lecture 10	Notebook 10
06.05.2026	12	Run time estimation, profiling, asymptotic complexity	Lecture 11	Notebook 11
13.05.2026	13	Rector's day
20.05.2026	14	Reserve, preparation for exam

Supplementary material for lectures

| NumPy (& string + list) cheatsheet

The supplementary materials (interactive Jupyter notebooks) for the lectures are located here:

pge_lectures@FEL Gitlab

Download the repository and read the instruction for installation in the `README.md` file. It is assumed that you already have Python installed, ideally (but not necessarily) with some Python environment management. For example, Conda or Mamba.

Video guide on how to use the lectures:

Slides and supplementary materials to lectures will be added progressively during the semester, as this course is going through changes.