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Efficient Algorithms - N500004

Title:	Efektivní algoritmy
Guaranteed by:	CTU in Prague, Faculty of Information Technology (500)
Faculty:	University of Chemistry and Technology, Prague
Actual:	from 2013 to 2015
Semester:	winter
Points:	winter s.:5
E-Credits:	winter s.:5
Examination process:	winter s.:
Hours per week, examination:	winter s.:2/2, C+Ex [HT]
Capacity:	unknown / unknown (unknown)
Min. number of students:	unlimited
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Level:

Guarantor:	Tvrdík Pavel prof. Ing. CSc.

Examination dates Schedule

Annotation -

Students get an overview of efficient algorithms and data structures for solving classical algorithmic problems, such as searching and sorting, on dynamically changing data sets. Students are able to design and implement such algorithms, to use methods for analysing their computational and memory complexity. They understand the sorting algorithms with O(n.log n) time complexity, special sorting algorithms with linear complexity, algorithms for associative and address searching. They are able to use the efficient dynamic data structures, such as hash tables, search trees, balanced search trees, heaps, B-trees, and others. They are able to work with recursive algorithms and dynamic programming.

Last update: Jirát Jiří (09.01.2014)

Aim of the course -

Students will be able to:

Understand a set of well-known standard algorithms

Recognize a proper algorithm variant for any specific usage

Use learn basic methods of algorithm analysis to be used as a main efficiency criterion

Last update: Jirát Jiří (31.01.2014)

Literature -

R:Cormen, T. H., Leiserson, C. E., Rivest, R. L. Introduction to Algorithms. The MIT Press, 2001. ISBN 0262032937.

Last update: Jirát Jiří (09.01.2014)

Syllabus -

1. Data structures I: Fundamental ADTs.

2. Data structures II: Hash tables.

3. Sorting algorithms I.

4. Data structures III: Trees, heaps.

5. Sorting algorithms II.

6. Data structures IV: Advanced heaps.

7. Searching and search trees.

8. Recursive algorithms.

9. B-trees and their variants.

10. Balanced search trees.

11. Dynamic programming.

12. Computational geometry algorithms.

Last update: Jirát Jiří (09.01.2014)

Learning resources -

https://edux.fit.cvut.cz/courses/BI-EFA/

(login necessary)

Last update: Jirát Jiří (09.01.2014)

Registration requirements -

An ability to solve basic algorithmic problems actively, to express the algorithmic solution in a high-level programming language (Java, C++), and knowledge of basic notions from calculus and combinatorics is assumed.

Last update: Jirát Jiří (09.01.2014)

Teaching methods
	Activity	Credits	Hours
	Účast na přednáškách	1	28
	Práce na individuálním projektu	1.5	42
	Příprava na zkoušku a její absolvování	1.1	30
	Účast na seminářích	1	28
		5 / 5	128 / 140