The aim of studying and bibliography

The main aim of study: Acquiring knowledge and skills in the field of static and dynamic data structures, selected algorithm construction techniques and computational complexity.

The general information about the module: Acquiring knowledge and skills in the field of static and dynamic data structures, selected algorithm construction techniques and computational complexity. The module presents basic data structures (lists, stacks, queues, trees) and algorithms for processing these structures with regard to complexity.

Bibliography required to complete the module

Bibliography used during lectures

1	Cormen T. H., Leiserson C. E., Rivest R. L., Stein C.	Wprowadzenie do algorytmów	Wydawnictwo Naukowe PWN.	2018
2	Wirth Niklauth	Algorytmy + struktury danych = programy	Warszawa, WNT.	2001.
3	Wróblewski Piotr	Algorytmy, struktury danych i techniki programowania	Helion.	2009

Bibliography used during classes/laboratories/others

1	Świder Krzysztof	Wykłady z algorytmów i struktur danych z zadaniami	Oficyna Wydawnicza PRz.	2004
2	Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman	Projektowanie i analiza algorytmów	Helion.	2003

Bibliography to self-study

1	A.V. Aho,J.E. Hopcroft, J.D. Ullman	Algorytmy i struktury danych	Helion.	2003
2	Sysło Maciej	Algorytmy	Helion.	2016

Basic requirements in category knowledge/skills/social competences

Formal requirements: The student satisfies the formal requirements set out in the study regulations.

Basic requirements in category knowledge: The basis of knowledge in computer science, algebra and statistics.

Basic requirements in category skills: Desired knowledge of any programming language.

Basic requirements in category social competences: Knowledge and observance of the student's duties and basic ethical principles.

Module outcomes

MEK	The student who completed the module	Types of classes / teaching methods leading to achieving a given outcome of teaching	Methods of verifying every mentioned outcome of teaching	Relationships with KEK	Relationships with PRK
01	Has basic knowledge of selected algorithm design techniques and understands how to improve their efficiency and transparency.	Lecture / Laboratory / Project	Completion part written	K_W05+ K_W06++ K_U05+ K_K01+	P6S_KK P6S_UW P6S_WG
02	Has basic knowledge of elementary data structures (e.g. queue and stack lists, trees) and is able to perform basic operations on these structures.	Lecture / Laboratory / Project	Completion part written. Credit, practical part	K_W07+ K_U15+ K_U18+ K_K05+	P6S_KO P6S_UW P6S_WG
03	Has basic knowledge of sorting algorithms, can explain their operation and assess the complexity of selected algorithms.	Lecture / Laboratory / Project.	Completion part written Completion part practical	K_W06++ K_W07+ K_U10+ K_U23+ K_K02+	P6S_KK P6S_KO P6S_UK P6S_UW P6S_WG

Attention: Depending on the epidemic situation, verification of the achieved learning outcomes specified in the study program, in particular credits and examinations at the end of specific classes, can be implemented remotely (real-time meetings).

The syllabus of the module

Sem.	TK	The content	realized in	MEK
1	TK01	Computational complexity of programs. Notation of algorithms: network of activities, linear notation. Writing algorithms in pseudo-code.	W01, W02	MEK01
1	TK02	Memory representation and basic algorithms on selected dynamic structures (stacks, queues, trees lists).	W03, W04	MEK02
1	TK03	Tree structures and their properties. Binary trees. Recursion. Binary search trees (BST).	W05	MEK01
1	TK04	Definition, basic features and algorithms on mounds (heap). Priority queues.	W06	MEK02
1	TK05	Sorting - basic definitions, formulation of the problem. Presentation and evaluation of the complexity of selected sorting algorithms.	W07	MEK03
1	TK06	Solving problems using recursion. Constructing and practical verification of selected sorting algorithms.	W08	MEK01 MEK03

The student's effort

The type of classes	The work before classes	The participation in classes	The work after classes
Lecture (sem. 1)	The preparation for a test: 10.00 hours/sem.	contact hours: 15.00 hours/sem.	complementing/reading through notes: 10.00 hours/sem. Studying the recommended bibliography: 10.00 hours/sem.
Laboratory (sem. 1)	The preparation for a Laboratory: 7.00 hours/sem. The preparation for a test: 3.00 hours/sem.	contact hours: 15.00 hours/sem.	Finishing/Making the report: 7.00 hours/sem.
Project/Seminar (sem. 1)	The preparation for projects/seminars: 3.00 hours/sem.	contact hours: 15.00 hours/sem..	Doing the project/report/ Keeping records: 5.00 hours/sem. The preparation for the presentation: 2.00 hours/sem.
Advice (sem. 1)	The preparation for Advice: 1.00 hours/sem.	The participation in Advice: 1.00 hours/sem.
Credit (sem. 1)	The preparation for a Credit: 7.00 hours/sem.	The written credit: 2.00 hours/sem.

The way of giving the component module grades and the final grade

The type of classes	The way of giving the final grade
Lecture	grade from written test.
Laboratory	assessment of reports and tasks performed in class.
Project/Seminar	assessment of the implementation of the project task.
The final grade	the final grade is the average of the above ratings.

Sample problems

Required during the exam/when receiving the credit
(-)

Realized during classes/laboratories/projects
(-)

Others
(-)

Can a student use any teaching aids during the exam/when receiving the credit : no

The contents of the module are associated with the research profile: yes

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2	G. Drałus; M. Gołębiowski; P. Hawro; P. Krutys; T. Kwater	Comprehensive online estimation of object signals for a control system with an adaptive approach and incomplete measurements	2024
3	G. Drałus	Metody śledzenia punktu MPP modułu fotowoltaicznego	2023
4	G. Drałus; J. Drałus; J. Kusznier; D. Mazur	Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation	2023
5	A. Czmil; G. Drałus; D. Mazur	Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet	2021
6	G. Dec; G. Drałus; B. Kwiatkowski; D. Mazur	Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic	2021
7	G. Drałus; T. Rak	Prognozowanie w horyzoncie jednej godziny produkcji energii przez panel fotowoltaiczny	2020
8	G. Drałus; T. Rak	Programowanie równoległe w hybrydowym środowisku MPI i OpenMP na klastrze serwerów	2020