Seminar: Machine Learning and Data Engineering (5 cr)
Code: R504D97-3003
General information
- Enrollment
- 18.03.2024 - 15.09.2024
- Registration for the implementation has ended.
- Timing
- 09.09.2024 - 08.12.2024
- Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Bachelor of Engineering, Information Technology
- Teaching languages
- English
- Seats
- 0 - 30
- Teachers
- Tuomas Valtanen
- Kenneth Karlsson
- Teacher in charge
- Kenneth Karlsson
- Groups
-
R54D24SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2024
- Course
- R504D97
Evaluation scale
Approved/Rejected
Objective
Student gains high-level understanding of Machine Learning and Data Engineering (MLDE), learning about fundamental concepts, principles, terminology, applications, relations to other areas of study and is able to form a bigger picture of own professional field.
Execution methods
Group work
Accomplishment methods
Active participation in group work
Active and critical information retrieval
Content
A series of seminars that cover various themes of machine learning through presentations by students
Location and time
Meetings at Jokiväylä 11, Rovaniemi.
Tentative meeting topics and schedule:
week 37: Course contents, getting started, grading, and other practicalities
week 38: What is AI
week 39: Seminar topics
week 40: AI problem solving
week 41. Real world AI
week 43. Machine learning
week 45. Machine learning
week 47. Neural networks
week 48. Guest lecture
week 49. Student seminar
Materials
Elements of AI and materials in Moodle workspace.
Teaching methods
Student gains high-level understanding of Machine Learning and Data Engineering (MLDE), learning about fundamental concepts, principles, terminology, applications, relations to other areas of study and is able to form a bigger picture of own professional field.
A series of seminars that cover various themes of machine learning through presentations by students.
Assessment criteria, approved/failed
Active participation in group work
Active and critical information retrieval
Assessment criteria, approved/failed
Approved if the student is actively participating in group work