Seminar: Machine Learning and Data Engineering (5cr)

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

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.

Assessment criteria, approved/failed

Approved if the student is actively participating in group work