SylabUZ
| Course name | Data structures and algorithms |
| Course ID | 13.2-WF-FizP-DSA-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | First-cycle studies leading to Bachelor's degree |
| Beginning semester | winter term 2020/2021 |
| Semester | 3 |
| ECTS credits to win | 5 |
| Available in specialities | Computer Physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Credit with grade |
| Laboratory | 45 | 3 | - | - | Credit with grade |
Teaching the student the ability to adjust the mathematical model and algorithm adequately to considered problem. Students use the knowledge and skills acquired earlier in the courses of general physics, the course of numerical methods and mathematical methods of physics.
Students know numerical methods, passed courses of mathematical analysis course and general physics.
The course deals with the general principles of algorithm writing, the ability to calculate the complexity of the algorithm.
Examples of algorithms and their implementation are considered. The special attention is devoted to optimization problems.
Lecture:
Conventional lecture, workshop, working with documentation
Laboratory:
Laboratory exercises, project method, independent work
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture:
Test - minumum 50%
Laboratory:
Students have to implement algorithms presented during the lecture. In addition, they have to apply one of the proposed algorithms (e.g. traveling salesman problem, image recognition using the Hausdorff dimension, evolutionary algorithm) in a real life problem and write a report describing the algorithm, programming techniques, and results of the work.
Before taking the exam a student must gain positive grade during the laboratory
Final grade: mean average of the exam (50%) and grade from the laboratory (50%).
[1] L. Banachowski, K. Diks, W. Rytter, Algorytmy i struktury danych, Wydawnictwa Naukowo-Techniczne, 2006.
[2] N. Wirth, Algorithms and Data Structures, Prentice Hall, 1985.
[1] W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes. The Art of Scientific Computing. Third Edition, Cambridge University Press, 2007.
Modified by dr hab. Piotr Lubiński, prof. UZ (last modification: 03-06-2020 17:00)
