AI project (5 op)
Toteutuksen tunnus: MS00CN48-3001
Toteutuksen perustiedot
- Ilmoittautumisaika
-
02.12.2023 - 16.01.2024
Ilmoittautuminen toteutukselle on päättynyt.
- Ajoitus
-
26.02.2024 - 30.04.2024
Toteutus on päättynyt.
- Opintopistemäärä
- 5 op
- Lähiosuus
- 5 op
- Toteutustapa
- Lähiopetus
- Yksikkö
- Tekniikka ja liiketoiminta
- Opetuskielet
- suomi
- englanti
- Koulutus
- Insinööri (ylempi AMK), Data Engineering and AI
- Tradenomi (ylempi AMK), Data Engineering and AI
- Opettajat
- Golnaz Sahebi
- Pertti Ranttila
- Ryhmät
-
YDATIS23Insinööri (ylempi AMK), Data Engineering and AI
-
YDATTS23Tradenomi (ylempi AMK), Data Engineering and AI
- Opintojakso
- MS00CN48
Toteutuksella on 2 opetustapahtumaa joiden yhteenlaskettu kesto on 7 t 0 min.
Aika | Aihe | Tila |
---|---|---|
Ma 15.04.2024 klo 08:30 - 12:00 (3 t 30 min) |
AI project MS00CN48-3001 |
EDU_1091
Hammarbacka esitystila byod
|
Ti 16.04.2024 klo 08:30 - 12:00 (3 t 30 min) |
AI project MS00CN48-3001 |
EDU_1090
Ringsberg esitystila byod
|
Arviointiasteikko
Hyväksytty/Hylätty
Sisällön jaksotus
A deep learning-focused, project-based course that empowers master's students to explore and develop AI projects, fostering their ability to conceive, implement, and optimize intelligent solutions.
Session 1: AI Project Lifecycle and Data Selection
Session 2: Data Preprocessing and Model Selection
Session 3: Presenting First Results and Discussion
Session 4: Algorithm Optimization and Tuning
Tavoitteet
After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented
Sisältö
Practical project related to AI
Oppimateriaalit
The course materials will be announced later during the course.
Tenttien ajankohdat ja uusintamahdollisuudet
No Exam
Opiskelijan ajankäyttö ja kuormitus
Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours
Final project: approximately 114 hours
Total: approximately: 130 hours
Lisätiedot
This course is project-based, requiring students to possess knowledge in machine learning and deep learning, specifically in image recognition and Sequential models.
Consequently, it is advisable to enroll in the 'Components and Applications of Artificial Intelligence' course first. In that course, students learn how to employ deep neural networks for image recognition (using CNN) and using RNN for sequential models. This foundational knowledge will better prepare students for the project-based nature of this course.