Seminar: Machine Learning and Data Engineering (5 cr)
Code: R504D97-3002
General information
Enrollment
02.07.2022 - 30.09.2022
Timing
12.09.2022 - 16.12.2022
Credits
5 op
Virtual proportion (cr)
2 op
Mode of delivery
60 % Contact teaching, 40 % Distance learning
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 35
Teachers
- Jyri Kivinen
- Kenneth Karlsson
Responsible person
Kenneth Karlsson
Student groups
-
R54D22S
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.
Content
A series of seminars that cover various themes of machine learning through presentations by students
Location and time
Tentative schedule Theme
13 SEP: Course contents, getting started, grading, and other practicalities
23 SEP: What is AI
30 SEP: Seminar topics
6 OCT: AI problem solving
10 OCT: Real world AI
28 OCT: Machine learning
15 NOV: Machine learning
22 NOV: Neural networks
1 DEC: Data science and engineering
2 DEC: Guest lecture: Sustainability for AI and Sustainable AI
9 DEC: Student seminar, Feedback
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
Approved: Active participation in group work and Active and critical information retrieval
Rejected: Fail to participate and no input to the Seminar group work.
Evaluation scale
Approved/Rejected
Assessment criteria, approved/failed
Approved if the student is actively participating in group work
Assessment methods and criteria
Active participation in group work
Active and critical information retrieval