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Applied Machine Learning (AML)

Applied Machine Learning (MSc)

Informatics (INFR11211), Semester 1, 2026

Week 7 Announcement

Nov 2 · 1 min read

Below is an update and summary of your tasks for AML for this week.

Q&A Session

  • Attend this week’s class Q&A session (Tue 3rd at 4:10pm).
  • In it, we will discuss the revised scheduled for the course that takes into account the disruption due to the strikes.

Coursework

  • Continue working on your coursework.
  • The project progress/interim report is due this week – 5pm Wed 04 Nov. The short submission form and instructions are linked on the course website here
  • The feedback session itself will be 2-5pm Fri 06 Nov (AT 5.04). If there are no issues with your report, we will let you know so you do not have to come to this session.

Lectures

  • Watch the videos for the new topics for week 7 - Naive Bayes Classification and Logistic Regression.
  • The links to the videos and slides are on the Schedule page (refresh the page if you don’t see the links). We will discuss these topics in the Q&A session in week 8 (i.e., next week).
  • Ask questions on EdStem for any parts you are not clear about.

Labs

  • Attend Lab 3, covers Evaluation, this week.
  • You should have started working on Lab 3 in advance of your scheduled lab session.
  • You can find the solution for Lab 2 linked on the Schedule page in week 5.

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.