Fundamentals of Machine Learning

Module code: G6061
Level 5
15 credits in spring semester
Teaching method: Lecture, Laboratory
Assessment modes: Coursework

In this module, you are introduced to the important field of machine learning.

You use a systematic approach, based on the following three key ingredients:

  • tasks
  • models
  • features.

You are introduced to both regression and classification, and your studies emphasise concepts such as model performance and learnability.

As part of this module, you learn techniques such as:

  • linear regression
  • single and multiple layer perceptron classification
  • kernel-based models (including RBF and SVM)
  • decision tree models and random forest
  • Naïve Bayes classification and k-means clustering.

You are also introduced to techniques for pre-processing the data (including PCA).

In this module, you adopt an example-based approach throughout.


some programming experience

Module learning outcomes

  • Demonstrate basic knowledge of several supervised and unsupervised machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, and PCA.
  • Map machine learning models to tasks based on reasoned arguments.
  • Explain and exploit practical concepts such as cross-validation and learning curve.
  • Use machine learning toolboxes to solve classification/regression problems with real-world data, including pre-processing of the data and incorporating prior knowledge.