Topic outline

  • General

    This course is fully online.

    Course email: cs-ej3211@aalto.fi
    SLACK Discussion Forum: Course Slack can be joined via this link.
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    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 problems using the 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. 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 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 covers six topics and for each topic there is a corresponding jupyter notebook with coding exercises (See Notebooks). 
    In addition, students are required to participate in the peer-graded assignments, where the goal is to formulate machine learning problem (See ML Project).

    Note, that there are no lectures! Instead, each week on Wednesday 14-15.00 we will have exercise sessions online (zoom). During exercise session we will briefly recap main material and discuss notebook coding exercises. Attending is not mandatory. Sessions will be recorded and shared.

    Study credits

    This is a 2 ECTS course, workload is approx. 54h.

    Course material
    1. Jupyter notebooks (https://jupyter.cs.aalto.fi/) which includes the textual explanation of the topics and the coding exercises. Check the Notebooks section for details regarding the submission deadlines and exercises sessions. 
    2. Course book: "Machine Learning: The Basics"