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Applied Machine Learning (AML)

Applied Machine Learning (MSc)

Informatics (INFR11211), Semester 1, 2025

Week 5 Announcement

Oct 13 · 1 min read

Below are your tasks for this week.

Q&A Sessions going forward

  • Q&A sessions from this week onwards will be at 16:10 at 40 George Sq., Lecture Theatre A

Lectures

  • Watch the videos for the new lecture topics for week 5 - Optimisation and Generalisation. The links to the videos and slides are on the Schedule page. We will discuss these in the Q&A session in week 6 (i.e., next week)
  • Ask questions on Piazza for any parts you are not clear about

Labs

  • Lab 2, which will cover Exploratory Data Analysis, Visualisation, and PCA, will take place this week. Note the time and location of your lab session so that you turn up on time
  • You should have started working on Lab 2 in advance of your scheduled lab session
  • You can find the solution for Lab 1 linked on the Schedule page in week 3

Tutorials

  • Your second tutorial will take place in week 6, you can find the tutorial sheet in the schedule for week 6.
  • We strongly recommend that you attempt all the questions in advance of your tutorial session
  • You can find the solution for Tutorial 1 linked on the Schedule page in week 4

Coursework

  • Groups have now been assigned, and You should have all gotten started with your chosen topic.

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 PMR (INFR11134) 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.