Artificial Intelligence Applications (5 cr)
Code: 5051253-3005
General information
- Enrollment
-
04.12.2024 - 13.01.2025
Registration for the implementation has ended.
- Timing
-
13.01.2025 - 30.04.2025
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Engineering and Business
- Campus
- Kupittaa Campus
- Teaching languages
- Finnish
- Seats
- 0 - 40
- Degree programmes
- Degree Programme in Information and Communication Technology
Realization has 12 reservations. Total duration of reservations is 36 h 0 min.
| Time | Topic | Location |
|---|---|---|
|
Wed 15.01.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1090
Ringsberg esitystila byod
|
|
Wed 22.01.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
ICT_C2025
Kieliluokka
|
|
Wed 29.01.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 05.02.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 12.02.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 26.02.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 05.03.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 12.03.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_3029
Lovisa muunto byod
|
|
Wed 19.03.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 26.03.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
|
Wed 02.04.2025 time 09:00 - 12:00 (3 h 0 min) |
Quest Speaker Paulo Maio: Tekoälysovellukset 5051253-3005 |
EDU_3001
Kaarle muunto byod
|
|
Wed 09.04.2025 time 09:00 - 12:00 (3 h 0 min) |
Tekoälysovellukset 5051253-3005 |
EDU_1089
Maskulin esitystila byod
|
Evaluation scale
H-5
Content scheduling
Starting from week 3 and continuing weekly
The course covers the following topics:
Basics and fundamental concepts of artificial intelligence
Traditional programming vs. machine learning
Regression and classification
Principles of neural networks
Data and its characteristics in machine learning
Supervised and unsupervised machine learning methods and applications
Backpropagation algorithm
Functioning of the perceptron algorithm
Reinforcement learning methods and applications
Deep learning methods and applications
Decision trees
Applications of generative artificial intelligence
Production and characteristics of synthetic data
Objective
After completing the course the student:
- understands what the artificial intelligence is in social and health care
- can explain the basic concepts of artificial intelligence
- knows the available artificial intelligence applications in social and health care
- understands the possibilities and limitations of artificial intelligence in social and health care
Content
- Basics of Artificial Intelligence
- Artificial Intelligence Applications
- Neural networks
- Machine learning
Materials
The lecture material is mainly in English.
Lecture materials, exercises, and other resources will be distributed via ITS-learning.
Teaching methods
Lectures, code examples, and independent exercises
Exam schedules
The course exam can be retaken three times.
Pedagogic approaches and sustainable development
The course includes approximately 13 lectures or practice sessions.
Student workload
Contact hours in total: approximately 40 hours
Independent study and homework: 90 hours
Total: approximately 130 hours
Evaluation methods and criteria
Assessment is on a scale of 0 to 5.
To pass the course, you must successfully complete the exam. The passing threshold for the exam is at least 40% of the maximum points. The passing grades are evenly distributed between 40% and 100%.
It is possible to earn additional points from exercises, which are added to the points from the passed exam. The points earned from exercises can raise the final grade by a maximum of 0.75.
Further information
Course announcements will be made via ITS-learning