Skip to main content

AI Solutions (10 cr)

Code: 5Y00FD89-3001

General information


Enrolment period
25.06.2020 - 30.09.2020
Registration for the implementation has ended.
Timing
27.08.2020 - 04.12.2020
Implementation has ended.
Credits
5 - 10
Mode of delivery
Contact learning
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
Finnish
Degree programmes
Master's Degree Programme in Data Expertise and Artificial Intelligence
Teachers
Esa Kujansuu
Ossi Nykänen
Pekka Pöyry
Person in charge
Pekka Pöyry
Course
5Y00FD89

Objectives (course unit)

The student knows different solutions utilizing artificial intelligence and knows the role of data as part of artificial intelligence applications. The student is able to identify different situations where artificial intelligence can be utilized and what kind of data the solutions require.

Content (course unit)

Studying and analysis of AI cases. The role of data as part of an artificial intelligence application.

Assessment criteria, satisfactory (1-2) (course unit)

The student researches and analyzes artificial intelligence solutions and recognizes the role of data as part of AI solutions.

Assessment criteria, good (3-4) (course unit)

The student is able to study and analyze artificial intelligence solutions from different perspectives and understands the role of data as part of artificial intelligence applications. The student is able to recognize situations where artificial intelligence can be utilized and to understand what kind of data is needed.

Assessment criteria, excellent (5) (course unit)

The student is able to research and analyze artificial intelligence in many ways and well understands the role of data as part of artificial intelligence applications. The student is able to recognize situations in which artificial intelligence can be utilized and understood well, what kind of data is needed and whether there is enough data available and where more data can be obtained.

Exam schedules

Ei tenttiä, uusinta ja korotus sovitaan erikseen opettajien kanssa.

Assessment methods and criteria

1 - seminaari esitys pidetty (pakollinen)
+1 - seminaari esityksen laajuus ja sisältö asianmukainen
+1 - väliraportti palautettu ajallaan
+1 - loppuraportti ja esitysmateriaali palautettu ajallaan
+1 - aiheesta tehty käytönnön esimerkki (esim. PoC, demo, sovellus, valmiin sovelluksen analysointi)

Assessment scale

0-5

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

ei seminaariesitystä

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

Opiskelija tutkii ja analysoi tekoälyratkaisuita ja tunnistaa datan roolin osana tekoälysovelluksia.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

Opiskelija osaa tutkia ja analysoida tekoälyratkaisuita eri näkökulmista ja ymmärtää datan roolin osana tekoälysovelluksia. Opiskelija osaa tunnistaa hyvin tilanteita, joissa tekoälyä voidaan hyödyntää ja ymmärtää, millaista dataa tällöin tarvitaan.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

Opiskelija osaa tutkia ja analysoida tekoälyratkaisuita monipuolisesti ja ymmärtää hyvin datan roolin osana tekoälysovelluksia. Opiskelija osaa tunnistaa erinomaisesti tilanteita, joissa tekoälyä voidaan hyödyntää ja ymmärtää hyvin, millaista dataa tällöin tarvitaan ja onko dataa riittävästi saatavilla ja mistä dataa saadaan lisää.

Go back to top of page