Machine Learning


Machine Learning


Ioannis Patras,

Content and organization

This module covers the following key concepts and themes:

The fundamentals:

  • Introduction to Machine Learning
  • Probability and Random Variables

Supervised Machine Learning Methods:

  • Regression (Linear, no Linear, Multivariate)
  • Classification I(Linear, no Linear, regulariston)
  • Classification I (Decision Trees, Naïve bayes, metrics)
  • Neural Networks

Unsupervised Machine Learning Methods:

  • Clustering (k-means, hierarchical)
  • Density Estimation (parametric distributions)
  • Dimensionality reduction

Advanced Topics:

  • Deep Learning, convolutional NN
  • Ensembles


Postgraduate course

Course Type

Semester Course

Marking Scheme

Exam: 60% Assignment 1 (Supervised learning): 20% Assignment 2: (Unsupervised learning): 20%

Participation terms

Duration - 12 weeks, 2 (+1 occasionally) hours of lectures per week Study Hours - • 2 coursework assignments organised in 7 lab sessions of 2 hrs each as follows: • 4 x 2hrs lab sessions for assignment 1 • 3 x 2hrs lab sessions for assignment 2 • Assessment: 60% final exam, 40% coursework

Modality (online/in person):

Mixed Mode


The module will be delivered in four different modes: 1. Static online material: Introduction to each week’s video Slides and links to reading material Summary of each week’s learning 2. Online live lectures: Online live lectures will be delivered online by the lecturer. The lectures will take place at zoom using the link: (TBD) Post your question using the chat box on zoom. Questions will be monitored and will be answered either as they come, or at the end of each thematic unit. 3. Mixed mode laboratory sessions: Weekly two hour long live laboratory sessions will be run by the senior demonstrator on TBD The labs will be taking place in two modes: Face to face lab sessions that will take place in (TBD) for those who can and want to physically attend. The sessions will be supported by demonstrators. Concurrently, for those who cannot physically attend, online sessions will be taking place on slack. Questions will be posed online, and will be answered either online, or if needed in a breakaway private session with one of the junior demonstrators. A channel will be constructed for each assignment in the slack workspace:

Host Institution
Queen Mary University of London

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