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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BIE-EFA Efficient Algorithms Extent of teaching: 2P+2C
Instructor: Completion: Z,ZK
Department: 18101 Credits: 5 Semester: Z

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.

Lecture 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.2. Data structures II: Hash tables.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.

Seminar syllabus:
1. Algorithms on arrays, multidimensional arrays and mapping functions. mathematics. ^2. ADT stack, queue, list. ^3. ADT table, set. ^4. Hash tables. ^5. Basic sorting algorithms. ^6. Trees and binary heaps. ^7. Sorting algorithms II. ^8. Binomial and Fibonnaci heaps.^9. Searching and binary search trees.^10. Recursive algorithms.^11. B-trees.^12. Balanced search trees.^13. Dynamic programming.^4. Data structures III: Trees, heaps.1. Algorithms on arrays, multidimensional arrays and mapping functions. mathematics.
2. ADT stack, queue, list.
3. ADT table, set.
4. Hash tables.
5. Basic sorting algorithms.
6. Trees and binary heaps.
7. Sorting algorithms II.
8. Binomial and Fibonnaci heaps.
9. Searching and binary search trees.
10. Recursive algorithms.
11. B-trees.
12. Balanced search trees.
13. Dynamic programming.

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

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.

Information about the course and courseware are available at https://courses.fit.cvut.cz/BI-EFA/
A pair of courses BIE-EFA + BIE-GRA can be recognized by proxy, if a student successfully completes a pair of courses BIE-AG1 + BIE-AG2. Students can ask for it at the study department.

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BIE-TI.2015_ORIGINAL Computer Science (Bachelor, in English) V Není
BIE-TI.2015 Computer Science (Bachelor, in English) V Není
BIE-WSI-SI.2015 Software Engineering (Bachelor, in English) V Není
BIE-BIT.2015 Computer Security and Information technology (Bachelor, in English) V Není


Page updated 28. 3. 2024, semester: Z/2023-4, L/2019-20, L/2022-3, Z/2019-20, Z/2022-3, L/2020-1, L/2023-4, Z/2020-1, Z,L/2021-2, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška