Neural Network Project (5 cr)
Code: TX00EY34-3006
General information
- Enrollment
-
02.12.2024 - 16.03.2025
Registration for the implementation has ended.
- Timing
-
17.03.2025 - 11.05.2025
Implementation is running.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- On-campus
- Unit
- (2019-2024) School of ICT
- Campus
- Myllypurontie 1
- Teaching languages
- Finnish
- Seats
- 0 - 35
- Degree programmes
- Information and Communication Technology
Implementation has 7 reservations. Total duration of reservations is 45 h 30 min.
| Time | Topic | Location |
|---|---|---|
|
Fri 21.03.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 28.03.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 04.04.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 11.04.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 25.04.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 02.05.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
|
Fri 09.05.2025 time 09:30 - 16:00 (6 h 30 min) |
Neuroverkkoprojekti TX00EY34-3006 |
MPA5020
Oppimistila
|
Objective
The students applies neural networks to solve real-world problems. This includes analysing the problem domain, acquiring and exploring data, searching, experimenting and evaluating alternative solutions, implementing and validating the chosen solution, building data processing pipelines and deploying the solution.
Content
• Group work project in accordance with the objectives of the course
• Applying the machine learning process model from idea to product
• Problem-based use of neural network and machine learning libraries
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
The student's contribution to the project meets the objectives set.
Assessment criteria, good (3)
The student is an active member of the team, has a clear role in the project and performs it to achieve the project's objectives.
Assessment criteria, excellent (5)
The student plays a central and innovative role in the project and performs their task in an exemplary manner.
Assessment criteria, approved/failed
The student's contribution to the project meets the objectives set.
Qualifications
Data Handling and Machine Learning, Neutral Networks