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Applied Machine Learning (AML)

Applied Machine Learning (MSc)

Informatics (INFR11211), Semester 1, 2026

Week 4 Announcement

Oct 12 · 2 min read

Greetings AML class,

Below you will find an update for week 4 which discusses the interruptions due to the ongoing strike action. Please read carefully.

Coursework

  • Information about the coursework can be found here
  • The form to indicate your project choice and team members is here and the link can also be found on the course webpage. If you want to work with specific other students you should fill out this form by 12pm Fri 16th Oct.
  • Those who did not submit the project group form will be assigned to a topic and groups by by early next week and the groups will be available in Learn shortly after

Course schedule

  • Please take a look at the revised schedule on the course webpage to see how we have shifted the topics
  • Reminder, that you will not be assessed in the exam on any material that we did not cover during the semester

Q&A sessions (Tuesdays)

  • There will be no Q&A Sessions in weeks 4, 5, and 6 due to the strike
  • Q&A sessions will resume in week 7 (3rd Nov)

Tutorials and labs

  • The tutorials and labs will proceed as normal. However, there may be some disruption to individual sessions if individual tutors/demonstrators are striking

Strike action

  • Please read the announcement in week 2 and here to learn more about the reasons for the strike
  • Going on strike is the last resort for staff: we love teaching and care very much about our students and their education. Staff also are not paid for the days they are on strike, so it really is a last resort!

Have a nice week,
Oisin and Sid

Announcements

In this course we will be introducing a number of machine learning methods and concepts, helping to understand how they work, and how to apply them.

Those wanting to conduct research in, and develop, machine learning methods should consider taking PML (INFR11298) instead. For general information on different machine learning courses at Informatics, see here.

On successful completion of this course, you should be able to:

  1. Explain the scope, goals and limits of ML, and the main sub-areas of the field.
  2. Describe the various techniques covered and where they fit within the structure of the discipline.
  3. Apply the taught techniques to data, to solve ML problems, using appropriate software.
  4. Analyse ML techniques in terms of their limitations and applicability to different problems, as well as potential ethical concerns.
  5. Compare and evaluate the performance of ML techniques using systematic approaches to conducting experiments and assessing scientific hypotheses.