MAT-72006 Advanced Algorithms and Data Structures, 7 cr
Additional information
Suitable for postgraduate studies
Person responsible
Tapio Elomaa
Lessons
Implementation 1: MAT-72006 2015-01
| Study type | P1 | P2 | P3 | P4 | Summer |
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Requirements
Lectures, weekly exercises, examination
Completion parts must belong to the same implementation
Learning Outcomes
After completion of the course the student is familiar with advanced algorithms and data structures. S/he understands how they work, what are their efficiency differences, and the purpose that these techniques serve on applications. General design principles of algorithms and their general analysis techniques have become familiar.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Algorithm Analysis | ||
| 2. | Sorting and Order Statistics | ||
| 3. | Data Structures | Augmenting data structures | |
| 4. | Advanced Design and Analysis Techniques | Dynamic programming, greedy algorithms, amortized analysis | |
| 5. | Advanced Data Structures | Fibonacci heaps | |
| 6. | Selected Topics | Matrix operations, linear programming, number theoretic algorithms, approximation algorithms | Deterministic primality testing |
Instructions for students on how to achieve the learning outcomes
The assessment is based on an exam and different exercises done throughout the course. Diligent exercise solving is the best way to achieve the learning outcomes.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | Introduction to Algorithms | Cormen, Leiserson, Rivest, Stein | No |
Prerequisites
| Course | Mandatory/Advisable | Description |
| MAT-02650 Algoritmimatematiikka | Mandatory | |
| TIE-02200 Ohjelmoinnin peruskurssi | Mandatory |
Correspondence of content
There is no equivalence with any other courses