Course: Algorithms

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Course title Algorithms
Course code KMI/ALS1
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Konečný Jan, doc. RNDr. Ph.D.
  • Masopust Tomáš, doc. RNDr. Ph.D., DSc.
Course content
The course is devoted to advanced analysis of search algorithms and data structures. Hashing - collision resolution, consistent hashing Bloom and quotient filters Count-min sketch HyperLogLog Sampling streaming data B-trees and their variants, LSM-trees Algorithms in external memory

Learning activities and teaching methods
Lecture, Demonstration
Learning outcomes
The students become familiar with selected advanced concepts of algorithms and complexity.
2. Comprehension. Understand basic concepts of algorithms and complexity.
Prerequisites
unspecified

Assessment methods and criteria
Oral exam, Written exam

Active participation in class. Completion of assigned homeworks. Passing the oral (or written) exam.
Recommended literature
  • Cormen T. H. (2013). Algorithms Unlocked. The MIT Press.
  • Cormen T. H., Leiserson C. E., Rivest R. L., Stein C. (2001). Introduction to Algorithms. Second Edition.. MIT Press.
  • Elden, L. (2007). Matrix Methods in Data Mining and Pattern Recognition. SIAM.
  • Knuth D. E. (1973). The Art of Computer Programming, Volumes I & III. Addison-Wesley.
  • Manolopoulos Y., et al. (2005). R-Trees: Theories and Applications.. Springer.
  • Skiena S. S. (1998). The Algorithms Design Manual. Springer, New York.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Applied Computer Science - Specialization in Software Development (2024) Category: Informatics courses 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Computer Science - Specialization in Artificial Intelligence (2020) Category: Informatics courses 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Computer Science - Specialization in Computer Systems and Technologies (2024) Category: Informatics courses 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Bioinformatics (2021) Category: Informatics courses 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Computer Science - Specialization in General Computer Science (2020) Category: Informatics courses 1 Recommended year of study:1, Recommended semester: Winter