Algorithms and programming
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| B3B33ALP | Z,ZK | 6 | 2P+2C | Czech |
- Corequisite:
- The course is a substitute for:
- Programming Essentials (BAB37ZPR)
- Lecturer:
- Jan Kybic (guarantor)
- Tutor:
- Jan Kybic (guarantor), Denis Baručić, Martin Řimnáč, Petr Štěpán, Vojtěch Vonásek
- Supervisor:
- Department of Cybernetics
- Synopsis:
-
This subject will give students a basic understanding of algorithms
and programming and teach them to design, implement and test
algorithms for simple tasks. The students will understand the notion
of computational complexity. They will learn about basic program
building blocks such as loops, conditional statements, variables,
functions and recursion. We will introduce the most often used data
structures (queue, stack, list, array etc) and operations on them. We
will show the basic algorithms, for example for searching and
sorting. Students will learn to write simple programs in Python.
- Requirements:
-
None.
- Syllabus of lectures:
-
1. Introduction. Basis programming structures and techniques (loops, conditional
statements). Python programming langyage.
2. Functions, strings, modules. Principles of functional programming.
3.Data types. Arrays.
4. Assymptotic complexity. Searching and sorting.
5. Recursion. Quick sorting.
6. Records. Principles of object oriented programming. Queue and stack.
7. Finite automaton. Regular expressions.
8. Linked lists. Trees.
9. Priority queue, heap, heapsort.
10. Sets and association maps. Hash tables.
11. State space search.
12. Constraint satisfaction. Dynamic programming.
13. Graphs and graph algorithms.
14. Numerical calculations and visualization.
- Syllabus of tutorials:
-
Python programming language.
Students will independently solve a number of short practical programming exercises.
- Study Objective:
-
The goal of the course is to give students elementary knowledge of programming and algorithmization and teach them to design, implement and test programms for solving simple tasks.
- Study materials:
-
• Wentworth, Peter, Elkner, Jeffrey and Downey, Allen B. and Meyers, Chris. 2012. How To Think Like a Computer Scientist - Learning with Python 3 (RLE), 3. vyd. Available online: http://openbookproject.net/thinkcs/python/english3e/.
• Downey, Allen B. 2016. Think Python: How to Think Like a Computer Scientist. 2. vyd. Sebastopol, CA: O’Reilly Media. Available online: https://greenteapress.com/thinkpython2/html/index.html
• Zelle, John. 2016. Python Programming: An Introduction to Computer Science. 3. vyd. Portland, Oregon: Franklin, Beedle & Associates. Materials at https://mcsp.wartburg.edu/zelle/python/.
• Sedgewick, Robert, Kevin Wayne, a Robert Dondero. 2015. Introduction to Programming in Python: An Interdisciplinary Approach. New York: Addison-Wesley Professional.
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/B3B33ALP
- Time-table for winter semester 2021/2022:
-
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 Tue Wed Thu Fri - Time-table for summer semester 2021/2022:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Cybernetics and Robotics 2016 (compulsory course in the program)
- Medical electronics and bioinformatics (compulsory course in the program)
- Cybernetics and Robotics 2016 (compulsory course in the program)