AI Applications (5 cr)
Code: 5Y00FE88-3002
General information
- Enrolment period
- 01.06.2021 - 17.10.2021
- Registration for the implementation has ended.
- Timing
- 01.08.2021 - 12.12.2021
- Implementation has ended.
- Credits
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- MD in Data Expertise and Artificial Intelligence
- Campus
- TAMK Main Campus
- Teaching languages
- Finnish
- Degree programmes
- Master's Degree Programme in Data Expertise and Artificial Intelligence
Objectives (course unit)
The student is able to implement various applications utilizing artificial intelligence and knows how to use data as part of an artificial intelligence application. The student knows different artificial intelligence application techniques and methods and is able to choose the most suitable one for different applications.
Content (course unit)
Designing and implementing an artificial intelligence application using a programming language, processing data as part of an artificial intelligence application, teaching and testing artificial intelligence models, and evaluating results.
Assessment criteria, satisfactory (1-2) (course unit)
The student knows how to implement an artificial intelligence application and knows how to use the data as part of an artificial intelligence application. The student knows some application technology and / or method of artificial intelligence.
Assessment criteria, good (3-4) (course unit)
The student is able to implement various applications utilizing artificial intelligence and knows how to use data as part of an artificial intelligence application. The student is familiar with some artificial intelligence application techniques and methods and can use some of them.
Assessment criteria, excellent (5) (course unit)
The student knows how to implement various applications using artificial intelligence and knows how to use data as part of an artificial intelligence application. The student is versatile in different artificial intelligence application techniques and methods and is able to choose the most suitable one for different applications.
Exam schedules
ei tenttiä
Assessment methods and criteria
1 - esimerkkimallin kokeilua ja arviointia, työraportti
2 - esimerkin kouluttaminen eri opetusdatalla, mallin kokeilua ja arviointia, työraportti
3 - esimerkin kouluttaminen eri opetusdatalla, tulosten analysointi suhteessa esimerkin dataan / soveltuvuus tapaukseen, työraportti
4 - oman casen deep learning toteutus, oman mallin kokeilua ja arviointia, työraportti
+1 - esitys 10.12.
Assessment scale
0-5
Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)
ei palautusta eikä esitystä
Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)
Opiskelija osaa toteuttaa ohjatusti tekoälyä hyödyntävän sovelluksen ja tietää, miten dataa käytetään osana tekoälysovellusta. Opiskelija tuntee jonkin tekoälyn sovellustekniikan ja/tai menetelmän.
Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)
Opiskelija osaa toteuttaa erilaisia tekoälyä hyödyntäviä sovelluksia ja tietää, miten dataa käytetään osana tekoälysovellusta. Opiskelija tuntee joitakin tekoälyn sovellustekniikoita ja menetelmiä sekä osaa käyttää niistä jotakin.
Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)
Opiskelija osaa toteuttaa monipuolisesti erilaisia tekoälyä hyödyntäviä sovelluksia ja tietää hyvin, miten dataa käytetään osana tekoälysovellusta. Opiskelija tuntee monipuolisesti erilaisia tekoälyn sovellustekniikoita ja menetelmiä sekä osaa valita niistä sopivimman eri käyttötarkoitukseen.