Advanced Algorithms and Data Structures
BASIC DATA
course listing
A - main register
course code
ITI8590
course title in Estonian
Algoritmide ja andmestruktuuride erikursus
course title in English
Advanced Algorithms and Data Structures
course volume CP
4.00
ECTS credits
6.00
to be declared
yes
assessment form
Examination
teaching semester
spring
language of instruction
Estonian
English
Study programmes that contain the course
Structural units teaching the course
IT - Department of Software Science
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Tudengid on võimelised kasutama erinevaid algoritme ja andmestruktuure erinevate rakenduste kontekstis.
course aims in English
The students are able to apply a variety of advanced algorithms and data
structures in different application contexts.
structures in different application contexts.
learning outcomes in the course in Est.
Aine läbinud üliõpilane:
- teab mitmete erialgoritmide tööpõhimõtteid;
- on suuteline rakendama neid algoritme erinevate ülesannete lahendamiseks,
- teab mitmete andmestruktuuride tööpõhimõtteid ja on suuteline neid rakendama;
- suudab analüüsida järjestikuste ja rekursiivsete algoritmide keerukust.
- teab mitmete erialgoritmide tööpõhimõtteid;
- on suuteline rakendama neid algoritme erinevate ülesannete lahendamiseks,
- teab mitmete andmestruktuuride tööpõhimõtteid ja on suuteline neid rakendama;
- suudab analüüsida järjestikuste ja rekursiivsete algoritmide keerukust.
learning outcomes in the course in Eng.
After completing this course the student:
- knows the internals of many advanced algorithms for a variety of applications:
- is able to adapt these algorithms to different problems;
- understands the internals of advanced data structures and know how to use them;
- can analyze the complexity of sequential and recursive algorithms.
- knows the internals of many advanced algorithms for a variety of applications:
- is able to adapt these algorithms to different problems;
- understands the internals of advanced data structures and know how to use them;
- can analyze the complexity of sequential and recursive algorithms.
brief description of the course in Estonian
Kursus katab järgnevaid teemasid:
- algoritmide loomise paradigmade süvakäsitlus;
- algoritmide analüüsi tehnikad, sh amortiseeritud analüüs ja rekurrentsete võrrandite lahendamine;
- efektiivsed algoritmid mitmete ülesannete lahendamiseks, nagu sorteerimine, otsimine, maatriksite korrutamine;
- efektiivseid algoritme toetavad anmestruktuurid, nagu sorteeritud järjestused, massiivid ja puud;
- juhuslikustatud algoritmid.
Võimalikud lisateemad:
- täisarvude korrutamine, signaalitöötlus, mõningad andmestruktuurid (prioriteetjärjekord ja mittelõikuvad alamhulgad) ja paisksalvestus
- algoritmide loomise paradigmade süvakäsitlus;
- algoritmide analüüsi tehnikad, sh amortiseeritud analüüs ja rekurrentsete võrrandite lahendamine;
- efektiivsed algoritmid mitmete ülesannete lahendamiseks, nagu sorteerimine, otsimine, maatriksite korrutamine;
- efektiivseid algoritme toetavad anmestruktuurid, nagu sorteeritud järjestused, massiivid ja puud;
- juhuslikustatud algoritmid.
Võimalikud lisateemad:
- täisarvude korrutamine, signaalitöötlus, mõningad andmestruktuurid (prioriteetjärjekord ja mittelõikuvad alamhulgad) ja paisksalvestus
brief description of the course in English
The course covers the following topics:
- in-depth treatment of algorithm paradigms,
- techniques of algorithm analysis, including amortized analysis and solving of recurrence equations;
- efficient algorithms for a variety of problems, including sorting, graph search, and matrix multiplication;
- data structures that support efficient algorithms, including data structures for ordered sequences, lists, and trees;
- randomized algorithms.
Further potential topics:
- integer multiplication, signal processing, other data structures (priority queues, disjoint sets), and hashing
- in-depth treatment of algorithm paradigms,
- techniques of algorithm analysis, including amortized analysis and solving of recurrence equations;
- efficient algorithms for a variety of problems, including sorting, graph search, and matrix multiplication;
- data structures that support efficient algorithms, including data structures for ordered sequences, lists, and trees;
- randomized algorithms.
Further potential topics:
- integer multiplication, signal processing, other data structures (priority queues, disjoint sets), and hashing
type of assessment in Estonian
eristav
type of assessment in English
examination
independent study in Estonian
koduülesanded
independent study in English
home exercises
study literature
Introduction to Algorithms, T.H. Cormen, C.E.Leiserson, R.L. Rivest, and C. Stein
Additiona literature on the course web page
Additiona literature on the course web page
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
1.0
practices
-
exercises
1.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2024/2025 spring