•   Machine learning methods INS24IS03-3001 11.11.2024-30.01.2025  5 credits  (IS24H-V) +-
    Competence objectives of the study unit
    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
    Prerequisites
    No prerequisites.
    Content of the study unit
    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.
    Assessment criteria
    Assessment criteria – satisfactory (1-2)
    Grade 1: Satisfactory skills in understanding and applying ML techniques for classification, prediction, and identification.
    Assessment criteria – good (3-4)
    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. 
    Name of lecturer(s)

    Ray Pörn

    Learning material

    The study material will be provided by the lecturer.

    Teaching language

    English

    Timing

    11.11.2024 - 30.01.2025

    Enrollment date range

    15.06.2024 - 10.11.2024

    Group(s)
    • IS24H-V
    Responsible unit

    Faculty of Technology and Seafaring

    Teachers and responsibilities

    Ray Pörn

    Degree Programme(s)

    Degree Programme in Intelligent Systems

    Campus

    Vasa, Wolffskavägen 33

    RDI share

    0.00 credits

    Share of online studies

    0.00 credits

    Assessment scale

    H-5