Study Guide

Objectives

Student:
• understand data, signals and their analysis as a concept
• understand machine learning as a concept
• understand different data and signal analysis methods and their benefits
• understand the uses, benefits and legalities of machine learning
• knows how to process process data produced by automation
• can choose and apply analysis methods in automation
• learns to use at least one software for analyzing data and signals
• can choose and apply machine learning in automation.

Content

Process data processing, digital signal processing, data analysis, machine learning methods, application of machine learning, automation.

Assessment criteria, satisfactory (1-2)

The student understands data and signals and machine learning as concepts through individual examples or cases. The student recognizes some data analysis methods and machine learning methods. The student understands the operating principles, usage methods and applicability of the methods he has identified and knows how to apply the methods narrowly.

Assessment criteria, good (3-4)

The student understands data and signals and machine learning as concepts in general and broadly. The student recognizes several machine learning methods and data analysis methods. The student understands the operating principles of the methods, their use and limitations of applicability and knows how to apply the methods widely.

Assessment criteria, excellent (5)

The student understands data and signals and machine learning as concepts in general and broadly. The student recognizes a wide range of analysis methods. The student understands the operating principles, usage methods and limitations of applicability of machine learning and data preprocessing and analysis methods and knows how to apply the methods very widely. The student knows how to process data efficiently and knows the theory of methods.