Machine learning and Artificial Intelligence 1 (5cr)

Evaluation scale

H-5

Content scheduling

Introduction for independent work at Adobe connect 9.8.2021, probably afternoon, actual time announced later.
Contact sessions in AC and Tornio campus, 16.8.2021 - 27.8.2021

Objective

This module provides you the essential practical knowledge on the selected topics on Machine Learning and Artificial Intelligence applied for Data Analytics. You will acquire basic working knowledge of the main concepts, approaches and algorithms in this field. Furthermore, you get ideas of applications where Machine Learning and Artificial Intelligence are used. The prerequisite for this course is a successful completion of Mathematics and Statistics 1 and 2 and a good understanding of Data Analytics processes and methods.

Content

Tieto puuttuu

Location and time

09.8.2021 - 27.8.2021, Adobe Connect and at Tornio campus

Materials

Will be told in the beginning of course

Teaching methods

Problem-based and team based learning may be applied where applicable. Students will seek information and solve problems related to subject presented. Different activating vocational teaching methods will be used depending on the group taught and the facilities available. If applicable, conventions from selected areas in software industry may be used as a part of teaching. Teacher guides the learning process by short introductory lectures and/or initial subject related material to be studied before practical work. Teacher prepares the setting for learning and provides coaching for the students. Teaching sessions may take place on campus and online. The main focus will be on guided knowledge searching and practical work on it.

Employer connections

Software industry conventions are used. This course may include a case company selected by the university. In addition, students are able to propose your own case companies, whose business information and data analytics they would like to develop. Students must provide a free form commission agreement from their own case companies.

Completion alternatives

Before the course starts, students may propose to the course teachers their personal implementation plan. The plan must be realistic and result in verificable development in the targeted competence(s). In addition, guidance from MIGRI and student visa must be taken into account. Course teachers accept or reject student's plan based on their own consideration.

Student workload

The student's estimated workload of this implementation is 135 h as follows:
Roughly half is Independent individual and teamwork guided when needed.
Rest of hours will be mostly learning the subject with guided practical and knowledge seeking exercises.

Assessment criteria, satisfactory (1)

Evaluation target: You know the essentials of Machine Learning algorithms and Artificial Intelligence.

Satisfactory
You understand the main subjectconcepts on a general level andapply them to do the basic tasks.

Assessment criteria, good (3)

Good
You understand the main subjectconcepts. Youanalyse tasks and choose appropriate ways to solve them.

Assessment criteria, excellent (5)

Excellent
You understand all the subjectconcepts comprehensivelyand apply them correctly. You choose an appropriate way to solve a task and explain the solution.

Qualifications

NULL