Machine Learning services in the cloud (5 cr)
Code: HT00BN67-3004
General information
- Enrollment
-
18.11.2024 - 09.01.2025
Registration for the implementation has ended.
- Timing
-
03.03.2025 - 09.05.2025
Implementation is running.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Face-to-face
- Unit
- School of Business
- Campus
- Main Campus
- Teaching languages
- Finnish
- Seats
- 20 - 30
- Degree programmes
- Bachelor's Degree Programme in Business Information Technology
Realization has 9 reservations. Total duration of reservations is 33 h 45 min.
Time | Topic | Location |
---|---|---|
Wed 05.03.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35F408
Oppimistila Diploma/LIKE
|
Wed 12.03.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Wed 19.03.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Wed 02.04.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Thu 03.04.2025 time 12:15 - 16:00 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Tue 08.04.2025 time 13:00 - 16:45 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35C129c
Muuntuva oppimistila
|
Wed 16.04.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Wed 23.04.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Wed 30.04.2025 time 08:30 - 12:15 (3 h 45 min) |
Tekoäly- ja koneoppimispalvelut pilvialustalla HT00BN67-3004 |
R35G205
Oppimistila KIKE/KOPA
|
Evaluation scale
0-5
Objective
Do You Want to learn how to understand cloud services that enable the construction of AI (Artificial Intelligence) and ML (machine learning) services with ready-made models and cloud services? In the course, the students will familiarise and utilise artificial intelligence and machine learning services with image- and optical character recognition and forecasting services in the cloud. The course enables students to understand the potential of cloud-based AI and machine learning services in a digital operating environments.
Course competencies
- Web development competence
- Systems and methods in ICT
- Learning and information management competence
Content
- Artificial intelligence and machine learning services in the cloud.
- document analysis
- forecasting
- image recognition
- other current/relevant AI and machine learning services
Location and time
Main campus, Rajakatu 35, class implementation. 03.03.2025 - 09.05.2025 (8 x 3,5h lectures)
Materials
Material written by the teacher can be found at Moodle. Any AWS Academy Learning material or additional machine learning and artificial intelligence books can be used as additional material.
Teaching methods
- Lectures and assignments (in-class)
- Exercises
- Knowledge checks
- Final assignment
- Final exam
The primary mode of delivery is a combination of theory and in-class activities on campus. The in-class activities deepen the learning in the forms of tutorials, individual and group work, reflection and guidance.
Exam schedules
The final exam is held at the end of the course. Renewal opportunities will be announced at the beginning of the course.
Completion alternatives
You can apply for a recognition of prior knowledge through the eRPL process. Contact the teacher by e-mail.
Student workload
Approximately 135 hours.
Assessment criteria, satisfactory (1)
Sufficient 1
You have basic knowledge in the area of AI & Machine learning services in the cloud. You know the components and terms of data preprocessing cloud services and what they mean.
Satisfactory 2
You have knowledge in the area of AI & Machine learning services in the cloud. You know the components terms of data preprocessing cloud services and what they mean.
Assessment criteria, good (3)
Good 3
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life data preprocessing problems.
Very good 4
You understand the concepts of AI & Machine learning services in the cloud and can apply the gained knowledge in a real-life AI & Machine learning problems and use cases. You have expanded your knowledge independently during the course.
Assessment criteria, excellent (5)
Excellent 5
In addition to previous requirements you can analytically discuss about the concepts in AI & Machine learning services in the cloud and present and defend (based on knowledge and evidence) your opinions. You can question the presented information and give valid options.
Qualifications
-Web Development Tools
-SQL Basics
-Basics of programming
-Use of command prompt and/or terminal
Further information
Avoin amk 3
EduFutura 5