Machine learning (5 cr)
Code: DA00EK58-3003
General information
- Enrollment
-
04.11.2024 - 17.11.2024
Registration for the implementation has ended.
- Timing
-
13.01.2025 - 31.05.2025
Implementation is running.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 0 cr
- Virtual portion
- 5 cr
- Mode of delivery
- Distance learning
- Unit
- Department of Business
- Campus
- Ecampus
- Teaching languages
- Finnish
- Seats
- 20 - 60
- Degree programmes
- Degree Programme in Data Analytics
- Teachers
- Atte Reijonen
- Jarkko Ansamäki
- Teacher in charge
- Jarkko Ansamäki
- Groups
-
DAKV23SVData analytics, online studies
- Course
- DA00EK58
Realization has 4 reservations. Total duration of reservations is 6 h 0 min.
| Time | Topic | Location |
|---|---|---|
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Fri 14.02.2025 time 09:00 - 10:30 (1 h 30 min) |
Koneoppiminen DA00EK58-3003 |
Etäopetus lukujärjestyksen mukaan
|
|
Fri 07.03.2025 time 12:50 - 14:20 (1 h 30 min) |
Koneoppiminen DA00EK58-3003 |
Etäopetus lukujärjestyksen mukaan
|
|
Fri 04.04.2025 time 12:50 - 14:20 (1 h 30 min) |
Koneoppiminen DA00EK58-3003 |
Etäopetus lukujärjestyksen mukaan
|
|
Fri 04.04.2025 time 14:30 - 16:00 (1 h 30 min) |
Koneoppiminen DA00EK58-3003 |
Etäopetus lukujärjestyksen mukaan
|
Objective
You understand the principles of artificial intelligence and machine learning.
You can determine what type of machine learning is appropriate for a given task.
You can do small artificial intelligence projects.
Content
How does artificial intelligence work?
How can machine learning be classified?
What are the different machine learning solutions suitable for?
How can simple artificial intelligence solutions be made?
Course material
Web materials
Study forms and methods
Individual learning tracks should always be discussed with the lecturers
RDI and work-related cooperation
The course may have assignments made for companies or assignments agreed individually with students.
Student workload
1 cr = 27 hours work
Further information
Opiskelijan tulee hallita R-ohjelmoinnin perusteet. Ne voi hankkia suorittamalla samaan aikaan pidettävän Tilastolliset menetelmät -kurssin tai kertausmateriaalien avulla. Suosittelemme kurssien samanaikaista suorittamista.
Evaluation scale
1-5
Assessment methods and criteria
There are multiple lecturers in the course and final evaluation is based on the individual evaluations of the lecturers and the evaluation of the final project(s)