Study Guide

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