Curricula

Curriculum Table

Competences

Assessment Framework

General Information

Evaluation Criteria
0-5

Assessment Methods and Criteria
Not applicable

Planned Learning Activities and Teaching Methods
Lectures, interactive exercises (programming) and independent learning, more in the video: https://youtu.be/eobETrnuzwc

Location and time
Time: about 4 hours lessons/week. Independent work at home between lessons needed.

Place: in Zoom: https://oamk.zoom.us/my/mannehannula

Learning environments
Not applicable

Recommended or Required Reading
Chollet, F. (2021). Deep learning with python, Second Edition. Manning Publications.

Also other material will be recommended during the course according to needs.

Further Information
-

Completion Alternatives
-

Teacher(s)
Manne Hannula

Work Placement and Working Life Connections
-

Exam Schedule
Evaluation of the course will happen on the basis of one exercise and a final exam.

International Connections
-

Student's Time Use and Workload
How much time you need to reserve for this course in your own calendar? The extent of the course is 5 credit points, meaning 5x27=135 hours of student's work in total. We have about 8 weeks for the course, meaning about 135/8 =~ 17 hours per week you need to reserve your own time for the course.

Learning Progress
This course covers the principle of machine learning in theory (shorter) and practice (longer) step by step and observationally.

At the beginning of the course, the general terminology of artificial intelligence is reviewed as much as is necessary, after which the issues related to machine learning are examined step by step. The idea of the course is to create a solid foundation for the student on selected artificial intelligence and machine learning topics, so that independent application of artificial intelligence in one's own work is possible after successful completion of the course. After the course, the student has the confidence to continue familiarizing himself with the subject area in more specific and deeper topics related to machine learning.

Evaluation criteria accepted
Not applicable

Evaluation criteria failed
Not applicable