Study Guide

Objective

The course focuses on different machine learning and artificial intelligence-based techniques that can be used for system identification, process optimization, classification, and predictive purposes.

After completing the course, the student:
- has knowledge of what AI and ML is, where it is used and how it has development
- can identify and recognize industrial problems that are solvable through machine learning
- can plan and apply the machine learning workflow for classification and regression problems
- understands and can explain the difference between different machine learning tasks and techniques

- can recognize different inputs used in AI and machine learning
- is able to develop and build machine learning models for different tasks
- can validate and test the performance of a model

- knows how to interpret the result of a machine learning experiment
- report on the result of a machine learning experiment
- can assess the outcome of a machine learning experiment

Content

The availability of big amounts of data from various systems makes it possible to apply both linear and nonlinear models. The course focuses on different techniques that can be used for identification, classification, and regression in industrial automation. The course also gives knowledge of feature selection and pre-processing techniques.

Materials

The study material will be provided by the lecturer.

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

Grade 1: Satisfactory skills in understanding and applying ML techniques for classification, prediction, and identification.

Assessment criteria, good (3)

Grade 3: Good skills in understanding and using ML techniques for classification, prediction, and identification.

Assessment criteria, excellent (5)

Grade 5: Excellent skills in understanding and using ML techniques for classification, prediction, and identification. 

Qualifications

No prerequisites.