Københavns Erhvervsakademi

en

Machine Learning

2021/2022
Engelsk titel
Machine Learning
Uddannelse
Datamatiker
Uddannelsestype
Fuldtidsuddannelse
Niveau
Erhvervsakademi
Semester
4. semester
Fagets/modulets varighed
1 semester
Ects
10
Udd. element
Valgfag
Sprog
Engelsk
Opstart
Efterår
Studiested
Håndværkergården, København N
Fagkode
3050414
Fag- /modulansvarlig
Jon Eikholm
  • Formål og læringsmål

    Machine Learning (ML) is a deeply fascinating topic. Here developers can write programs that "magically" solve difficult problems.
    Feed some data to the algorithm, and a result will appear. If you are not satisfied, then add more data or train the model. Repeat.

    In this course you will learn how to use Machine Learning (ML) to solve problems of different kind. We will start with a simple problem like how to train a model to behave like a logical AND operator. From there we move on to predict height based on age and to predict people's political views based on socio-economic data.

    This course will focus on understanding the fundamentals of ML. We will solve the same problem in 3 different ways:
    First using Google Sheets, then a Deep Neural Network Simulation Tool and finally using Python.


    For the mandatory task, students will define their own project. It can be based on any of the following:
    Deep Neural Network (DNN)
    Reinforcement learning
    Azure Machine Learning Studio
    TensorFlow.js
    Convolutional Neural Networks (CNN)
    Recurrent Neural Networks (RNN)

    The goal is for the student to get inspired and find their own problem to solve.

    Viden

    The student knows how a computer can learn.
    The student knows several ML methods, such as Regression, Classification, Clustering and Deep Learning. And hence the concepts of Supervised Learning and Unsupervised Learning.
    The student knows tools such as Numpy and Pandas.

    Færdigheder

    The student can use ML to solve practical tasks.
    The student can prepare a dataset from raw data. This includes Data Cleaning, Feature Selection, Data Transforms and Feature Engineering.
    The student can make visual representation of data to select features. The student can train and test a model.
    The student can use an ML-framework to solve a practical task.

    Kompetencer

    The student knows how to select a relevant ML method to solve a given problem.
    The student can assess the quality and efficiency of a solution.
    The student can find and use relevant online resources and documentation to build a ML solution.

  • Undervisningsform
    Class presentations of new material. In-class exercises. Group work and individual assignments.
  • Forudsætninger for at deltage i faget

    Faglige forudsætninger for at deltage i faget
    The student must know programming at a level of 3rd. semester KEA Computer Science AP

    Materielle forudsætninger for at deltage i faget
    Windows is required for the Simulation Tool. For everything else Win/macOS/Linux is OK.

  • Prøve

    Læringsmålene for prøven er identiske med fagets/fagenes læringsmål

    Forudsætninger for indstilling til eksamen
    There will be 2 mandatory exercises. They must be handed in and approved, for the student to gain access to any exam. The 2nd mandatory exercise may be used as an exam project.
    Faget prøves
    Faget/modulet prøves selvstændigt
    Prøveform
    Mundtlig prøve
    Oral 25 minutes exam with internal censor.
    ○ 10 minutes presentation
    ○ 15 minutes Q&A

    Student presents exam project:
    ○ Project demo
    ○ Explain interesting parts and choice of technologies
    ○ Q & A about project and curriculum
    Individuel eller gruppeprøve
    Individuel
    Anvendt sprog til prøven
    Engelsk
    Varighed
    25 min.
    Bedømmelsesform
    7-trins skala
    Bedømmer(e)
    Intern censur
48
timers undervisning
226
timers forberedelse
Tallene viser omfanget af arbejdsbelastningen relateret til faget fordelt på forskellige studieaktiviteter.

På faget Machine Learning modtager du 48 timers undervisning, hvilket svarer til 64 lektioner (1 lektion = 45 min.) og 18% af din samlede arbejdsbelastning på faget.

Undervisningen vil primært bestå af følgende aktiviteter: klasseundervisning, øvelser.
Forberedelsen vil primært bestå af følgende aktiviteter: øvelser.

Læs om KEAs studieaktivitetsmodel

*KEA kan fravige det angivne timetal, hvis det er begrundet i særlige forhold.