Seminar: Machine Learning and Data EngineeringLaajuus (5 cr)
Code: R504D97
Credits
5 op
Teaching language
- English
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
Assessment criteria, approved/failed
Approved if the student is actively participating in group work
Enrollment
18.03.2024 - 31.07.2024
Timing
09.09.2024 - 08.12.2024
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Tuomas Valtanen
- Kenneth Karlsson
Responsible person
Kenneth Karlsson
Student groups
-
R54D24SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2024
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
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
Evaluation scale
Approved/Rejected
Assessment criteria, approved/failed
Approved if the student is actively participating in group work
Enrollment
13.03.2023 - 25.09.2023
Timing
11.09.2023 - 17.12.2023
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 30
Teachers
- Jyri Kivinen
- Kenneth Karlsson
Responsible person
Kenneth Karlsson
Student groups
-
R54D23SBachelor of Engineering, Machine Learning and Data Engineering (full time studies), 2023
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
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
week 50. Wrap-up
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
Evaluation scale
Approved/Rejected
Assessment criteria, approved/failed
Approved if the student is actively participating in group work
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