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

Applied Artificial Intelligence (AI), 5 cr - ICB013AS3AE

Upcoming implementations

Applied Artificial Intelligence (AI) ICB013AS3AE-3005 20.10.2025-12.12.2025 5 op (IT4PAICB1, ...) +-
Teaching methods and instruction

The course is virtual, requiring independent study and group work skills virtually. The learning environment is Moodle. Moodle contains learning materials and instructions.

The implementation involves a group assignment. This requires online meetings of groups (3-5 people).

Course materials are available at the start of the course. Current company lectures and other materials may be added during the course, but these will be announced separately.

Learning material and recommended literature

Coursebook is Machine Learning Engineering with Python (Second edition) from Andrew P. McMahon, Adi Polak.

Course book is available in Haaga-Helia online library.

Also there is material available in Moodle.

Campus

Pasila Campus

Teaching language

English

Timing

20.10.2025 - 12.12.2025

Learning assignments

The course contains some individual assignments and group assignment.

Enrollment

14.05.2025 - 22.05.2025

Content scheduling

Course is virtual and deadlines for the assignments can be found from the Moodle.
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Jukka Remes, Ville Pennanen

Seats

30 - 60

Degree Programme

TRATI Tradenomi tietojenkäsittely

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5
Applied Artificial Intelligence (AI) ICB013AS3AE-3006 23.03.2026-15.05.2026 5 op (IT4PAICB1, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

23.03.2026 - 15.05.2026
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Juhani Heikkinen, Jukka Remes

Seats

30 - 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-3004 24.03.2025-16.05.2025 5 op (IT4PAICB1, ...) +-
Teaching methods and instruction

The implementation is virtual, no compulsory online lectures. The might be voluntary online lectures from co-operation companies and universities. There are exercises which apply example Python notebooks. It is easier to make exercises if you have Python skills, but previous programming experience is not required.

Learning material and recommended literature

AI learning paths and tutorials from different AI topic areas e.g. machine learning, generative AI and chatbots.

Working life connections

Visiting lectures and RDI projects' topis are connected to the program and exercises.

Campus

Pasila Campus

Teaching language

English

Timing

24.03.2025 - 16.05.2025

Enrollment

02.01.2025 - 21.03.2025
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Jukka Remes, Ville Pennanen

Seats

30 - 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

Past implementations

Applied Artificial Intelligence (AI) ICB013AS3AE-3003 21.10.2024-13.12.2024 5 op (IT4PAICB1, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

21.10.2024 - 13.12.2024

Enrollment

03.06.2024 - 18.10.2024
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Juhani Heikkinen, Jukka Remes

Seats

30 - 60

Degree Programme

TRATI Tradenomi tietojenkäsittely

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5
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

02.01.2024 - 22.03.2024
Groups
- IT4PAICB1
- ITE4PADIG1
- ITE4PAICB1
- VIRTUAL
- INSTRUCTED
- ITB4PAICB1
Teachers

Juhani Heikkinen, Anne Wuokko, Jukka Remes

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

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.

When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25 %, your grade will be lowered by one. If you are absent more than 50 %, the course is failed.