• Machine Learning (TTC8050-3004),
         03.10.2022 – 13.11.2022,  4 cr  (ZJA22STIDA2) — Face-to-face +-
    Learning outcomes of the course
    You understand the basic principles of machine learning. You know the most common machine learning methods, you know how to apply them to existing data in practice, and how to interpret the results of the methods.

    EUR-ACE Competences:
    Knowledge and Understanding
    Engineering Practice
    Research and information retrieval
    Prerequisites and co-requisites
    Basic ICT skills, basic skills in programming, knowledge and command of Python programming language.
    Additionally, courses in Computational algorithms and Data Preprocessing .
    Course contents
    - Supervised and unsupervised machine learning and the most common regression and classification models
    - Application using Python libraries (NumPy, Pandas and scikit-learn)
    - Data format and quality
    - Splitting of the data set into training and test data
    - Evaluation of model accuracy

    Different models of machine learning:
    - k-nearest neighbors
    - k-means clustering
    - Naive Bayes method
    - Support Vector Machine
    - Principal Component Analysis (PCA)
    - Decision trees and random forest
    - Perceptron (simple neural network)
    Assessment criteria
    Assessment criteria - grade 1 and 2
    Satisfactory 2: You know the most commonly used techniques of machine learning for various problems. You are able to choose the techniques of machine learning and apply your technical know-how in practice. In addition, you are able to assess your implementation superficially.

    Sufficient 1: You know the most commonly used techniques in machine learning and are able to apply them. In addition, you are able to give a limited assessment of your implementation.
    Assessment criteria - grade 3 and 4
    Very good 4: You recognize the advantages of machine learning in the digital era. You know the most common techniques used in machine learning and are able to justify the use of the implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

    Good 3: You are aware of the advantages of machine learning in the digital era. You knows the most commonly used techniques for various problems. You are able to apply your technical know-how in practice and validate its development.
    Assessment criteria - grade 5
    Excellent 5: You recognize the advantages of machine learning in the digital era. You master the techniques of machine learning in a versatile manner and are able to justify the use of implemented techniques in various tasks. You are able to apply your technical know-how in practice and assess your implementation critically as well as validate its development.

    Language of instruction

    Finnish

    Location and time

    Opintojakso toteutetaan syyslukukaudella 2022. (not translated)

    Planned learning activities, teaching methods and guidance

    Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen. (not translated)

    Learning materials and recommended literature

    Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-ympäristöä, Git-versiohallintaa, scikit-learn, Pandas, visualisointikirjastoja sekä muita soveltuvia kirjastoja. (not translated)

    Lecturer(s)

    Juha Peltomäki

    Working life cooperation

    Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin. (not translated)

    Exam dates and re-exam possibilities

    Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. (not translated)

    Timing

    03.10.2022 - 13.11.2022

    Learning assignments and student workload

    Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia. (not translated)

    Groups
    • ZJA22STIDA2
    Alternative learning methods

    Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä. (not translated)

    Degree Programme

    Bachelor's Degree Programme in Information and Communications Technology

    Mode of delivery

    Face-to-face

    Credits
    • 4 cr
    Unit

    School of Technology