Copenhagen School of Design and Technology

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Machine Learning

2021/2022
Danish title
Machine Learning
Study programme
Computer Science
Type of education
Full time education
Level of education
Academy Profession
Semester
4. semester
Duration of the subject/module
1 semester
Ects
10
Programme elements
Elective
Language
English
Start time
Autumn
Location
Håndværkergården, København N
Subject number
3050414
Responsible for the subject(s)/modul(es)
Jon Eikholm
  • Purpose and learning objectives

    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.

    Knowledge

    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.

    Skills

    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.

    Competences

    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.

  • Type of instruction
    Class presentations of new material. In-class exercises. Group work and individual assignments.
  • Subject/module requirement for participation

    Academic requirement for participation
    The student must know programming at a level of 3rd. semester KEA Computer Science AP

    Equipment needed to participate
    Windows is required for the Simulation Tool. For everything else Win/macOS/Linux is OK.

  • Exam

    The learning outcomes of the exam are identical with the learning outcomes of the subject(s)/modul(es)

    Prerequisites for access to the examination
    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.
    Exam in one or more subjects
    Subject/module is tested standalone
    Type of exam
    Oral examination
    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
    Individual exam or group exam
    Individual
    Exam languages
    English
    Duration
    25 min.
    Type of evaluation
    7-point grading scale
    Examiners
    Internal censure
48
hours of teaching
226
hours of preparation
The figure shows the extent of workload related to the subject divided into different study activities.

In the subject Machine Learning you will receive 48 hours of instruction, which corresponds to 64 lessons (1 lesson = 45 min.) and 18% of your total workload for the subject.

The teaching primarily consists of the following activities: classroom teaching, exercises.
The preparation primarily consists of the following activities: exercises.

Read about KEAs Study Activity Model

*KEA can deviate from the number of hours if this is justified by special circumstances