Machine Learning and Data Analytics in Technology (5 cr)

Objectives (course unit)

Student
- knows the key terms, concepts and principles related to machine learning
- identifies applications of machine learning, especially in industry
- knows the key terms and concepts of data analytics
- knows the main principles of data collection, storage and analysis methods
- knows the most common data management and visualization methods
- understands the meaning and uses of data, especially in automation technology processes

Content (course unit)

Introduction to algorithms, machine learning and the basics of artificial intelligence
Machine learning applications
Introduction to Big Data and its applications in industry
Data utilization methods
The most common Big Data systems
Key concepts: data definition, Big Data, data visualization, algorithms, machine learning, artificial intelligence

Assessment criteria, satisfactory (1-2) (course unit)

The student knows the basics and most important concepts of machine learning and data analytics.

Assessment criteria, good (3-4) (course unit)

The student knows the basics and most important concepts of machine learning and data analytics. The student has applied skills in the key topics of the course.

Assessment criteria, excellent (5) (course unit)

In addition to the above, the student is able to apply the ideology of machine learning and data analytics in a versatile way, especially in industry. The student has a broad understanding of the meaning and uses of data, especially in automation technology processes.

Evaluation methods and criteria

Kurssi jakautuu kahteen osioon: koneoppiminen ja data-analytiikka. Molempiin osioihin liittyy kehittämistehtävä, josta voi saada 0-50 pistettä. Kurssin maksimipistemäärä on 100 pistettä ja arvosanarajat menevät seuraavasti:

< 44 pistettä -> 0

>45 pistettä -> 1

>60 pistettä -> 2

>70 pistettä -> 3

>80 pistettä -> 4

>90 pistettä ->5

Assessment scale

0-5

Teaching methods

Lähiopetusta, oppimistehtäviä, harjoituksia.