Applied Artificial Intelligence (AI) - ICB013AS3AE - Opinto-opas

Applied Artificial Intelligence (AI), 5 cr - ICB013AS3AE

Upcoming implementations

Applied Artificial Intelligence (AI) ICB013AS3AE-3002 25.03.2024-17.05.2024 5 op (IT4PAICB1, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

25.03.2024 - 17.05.2024

Enrollment

27.11.2023 - 04.12.2023
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Juhani Heikkinen, X-Ohjelmisto Ohjelmistokehitys, Anne Wuokko

Seats

15 - 60

Degree Programme

TRATI Tradenomi tietojenkäsittely, ITBBA Business Information Technology

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5

Current implementations

Applied Artificial Intelligence (AI) ICB013AS3AE-3001 23.10.2023-15.12.2023 5 op (IT4PAICB1, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

23.10.2023 - 15.12.2023

Enrollment

12.06.2023 - 20.10.2023
Groups
- IT4PAICB1
- VIRTUAL
- ITB4PAICB1
- INSTRUCTED
Teachers

Lili Aunimo, Juhani Heikkinen

Seats

15 - 40

Degree Programme

TRATI Tradenomi tietojenkäsittely

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5

Past implementations

No past implementations yet.

The overall learning objective of the course is to give the students insight into machine learning and natural language processing (NLP) technologies and their application in practice.
Upon successful completion of the course, the student:
• knows the main concepts of machine learning and natural language processing,
• can apply a machine learning method in a business case,
• can build a simple chatbot,
• has gained basic skills in using selected machine learning and NLP tools and
• is capable of planning and implementing a project involving AI technologies

• Main concepts of machine learning and natural language processing.
• Business cases where machine learning and natural language processing methods are used.
• Tools (e.g. Anaconda, Python library scikit-learn ) for machine learning.
• Tools (e.g. Google Dialogflow, IBM Watson Virtual Assistant)
• Course project involving AI technologies.

Implementation Methods:
- Virtual, blended and contact implementations
- Recognition of Prior Learning

This course (Applied AI) provides basic knowledge on machine learning algorithms in Python and Knime.