Algorithms and Programming
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| B3B33ALP | Z,ZK | 6 | 2P+2C | Czech |
- Relations:
- In order to register for the course B3B33ALP, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
- During a review of study plans, the course BAB37ZPR can be substituted for the course B3B33ALP.
- It is not possible to register for the course B3B33ALP if the student is concurrently registered for or has previously completed the course BAB37ZPR (mutually exclusive courses).
- Course guarantor:
- Vojtěch Vonásek
- Lecturer:
- Vojtěch Vonásek
- Tutor:
- Matej Novosad, Michal Reiser, 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 language.
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:
-
Exercises in computer labs will help the students to internalize the content from lectures; they will get used to programming habits and will experimentaly verify the features of algorithms. Programming at home is an important part of the study.
- 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: OReilly 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 2024/2025:
-
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 2024/2025:
- 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 elective course)
- Cybernetics and Robotics 2016 (compulsory course in the program)