AI project (5 cr)
Code: MS00CN48-3001
General information
- Enrollment
-
02.12.2023 - 16.01.2024
Registration for the implementation has ended.
- Timing
-
26.02.2024 - 30.04.2024
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Engineering and Business
- Teaching languages
- Finnish
- English
- Degree programmes
- Master of Engineering, Data Engineering and AI
- Master of Business Administration, Data Engineering and AI
- Teachers
- Golnaz Sahebi
- Pertti Ranttila
- Course
- MS00CN48
Realization has 2 reservations. Total duration of reservations is 7 h 0 min.
| Time | Topic | Location |
|---|---|---|
|
Mon 15.04.2024 time 08:30 - 12:00 (3 h 30 min) |
AI project MS00CN48-3001 |
EDU_1091
Hammarbacka esitystila byod
|
|
Tue 16.04.2024 time 08:30 - 12:00 (3 h 30 min) |
AI project MS00CN48-3001 |
EDU_1090
Ringsberg esitystila byod
|
Evaluation scale
Hyväksytty/Hylätty
Content scheduling
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
Objective
After completing the course, the students can
- work in the AI project
- describe and understand how AI projects are implemented
Content
Practical project related to AI
Materials
The course materials will be announced later during the course.
Exam schedules
No Exam
Student workload
Contact hours:
- 4 times 4h theory and practice: 4 x 4h = 16 hours
Final project: approximately 114 hours
Total: approximately: 130 hours
Further information
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.