Opetussuunnitelmat

Objective

Purpose:
The operation of society is based on utilization of information technology where various processes of society are regulated at many levels with data. Data always describes some phenomenon or event for the interpretation of which algorithms are needed. In this course, the focus is on planning, implementation and testing of computational algorithms.

EUR-ACE Competences:
Knowledge and understanding 
Engineering practice

Learning outcomes:
You have defined a problem that requires computational algorithms. You have processed data that is essential to solving the problem. You know how to design an algorithm to solve a computational problem and you have implemented and tested the algorithm in practice. You know how to optimize an algorithm and you know the limitations related to the use of the algorithm in terms of data and the problem to be solved.

Content

- Algorithms
- NumPy
- Lists and sorting
- Combinations
- Greedy algorithm
- K- means

Location and time

Opintojakso toteutetaan 6.3.2023-30.4.2023.

Oppimateriaali ja suositeltava kirjallisuus

Harjoitustöiden tekemisessä hyödynnetään Anaconda ohjelmistoa (Python 3.7-versio tai uudempi): https://www.anaconda.com/download/

Teaching methods

Kurssilla tutustutaan laskennallisten algoritmien soveltamiseen liittyvään teoriaan, minkä pohjalta tehdään arvioitavat harjoitustyöt. Harjoitustyöt tehdään yksilötöinä, mutta vertaistukea on tarjolla kurssin Teams – ryhmästä. Kurssin voi suorittaa täysin virtuaalisesti.

Employer connections

Kurssin sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Vaihtoehtoiset suoritustavat

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.

Further information

Kurssi arvioidaan palautettujen harjoitustöiden perusteella. Harjoitustöitä voi palauttaa vain kurssitoteutuksen aikana.

Evaluation scale

0-5

Arviointikriteerit, tyydyttävä (1-2)

Sufficient 1:
You understand and know how to define a problem that requires computation. The data required for solving the computational problem has been taken into use. You are able to plan and implement a simple algorithm that produces the solution to the defined computational problem. You are familiar with optimization as a part of algorithm development. The documentation of the implementation covers the necessary parts.

Satisfactory 2:
You understand and are able to define the problem requiring computation. The essential data for solving the computational problem has been taken into use. You are able to plan and implement a simple algorithm that produces the solution to the defined computational problem. The effectiveness of the algorithm has been assessed. Documentation of implementation and optimization covers the necessary parts.

Arviointikriteerit, hyvä (3-4)

Good 3: You understand and are able to define the problem that requires computation. The data for solving the computational problem has been processed. You are able to plan and implement an algorithm that produces several solutions to the defined computational problem. You are able to assess the need for optimization based on assessment of effectiveness. The documentation of the implementation is of good quality and gives a clear idea of the implementation.

Very good 4: You understand and are able to comprehensively define the problem that requires computation. The data required to solve the computational problem has been for its essential parts processed. You are able to plan and implement alternative algorithms that produce several solutions to the defined computational problem. You are able to assess and if needed implement optimization of the algorithm based on effectiveness assessment. The documentation of the implementation is of high quality and it illustrates the implementation clearly.

Assessment criteria, excellent (5)

Excellent 5: You understand and are able to define a computational problem thoroughly. Data for solving the computational data is widely processed. You are able to plan and implement alternative algorithms that produce several solutions to the defined computational problem. You are able to assess and if necessary implement the algorithm optimization based on assessment of effectiveness. In optimization the suitability of the programming language has been considered for the computational problem to be solved. The implementation has been documented thoroughly and it illustrates the implementation clearly.

Qualifications

Basics in data structures and algorithms, data visualization, good command of some programming language. The programming language used in the course is Python.