Topic outline

  • General

    This course is fully online.

    Course objectives
    This course teaches you to formulate real-life aspects, such as finding blueberries during a hike, as machine learning problems. You will learn how to solve these machine learning problems using popular machine learning library Scikit-Learn in the programming language Python.

    Our main ambition is to provide students with a minimum skill set that allows them to make good use of ready-made machine learning libraries such as these here. We do not focus on detailed derivations of calculations that are carried out within the machine learning methods.

    Prerequisites
    This course is for everybody with some experience in using a higher-level language such as Java, C++, or Scala. You should also be familiar with using vectors and matrices to represent numeric data.

    Course content
    The course is organized into six thematic rounds with corresponding coding assignments.

    Round 1 - Data, Model and Loss. 
    Round 2 - Regression. 
    Round 3 - Model Validation, Selection and Regularization 
    Round 4 - Classification. 
    Round 5 - Clustering. 
    Round 6 - Feature Learning. 

    SLACK Discussion Forum: Course Slack can be joined via this link.