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Principles of Machine Learning

15 Credits

This course is an opportunity for students to learn and experiment with the fundamental concepts and techniques used in modern machine learning applications through code and visualisations. This course offers students actionable knowledge of machine learning and deep learning, useful in their future careers (e.g. as data scientists or machine learning engineers).

In this course, students will learn how to set-up a machine learning project: (i) pre-processing a data set; (ii) choosing and implementing appropriate models for that data; (iii) improving its performance; and (iv) interpret and present the results.

Indicative Topics

  • Machine learning frameworks
  • Probabilistic models
  • Cleaning and preparing data
  • Dimensionality reduction
  • Supervised learning: regression and classification
  • Neural networks
  • Stochastic Gradient Descent and back propagation
  • Unsupervised learning
  • Reinforcement learning
  • Interpretability of machine learning models
  • Bias in machine learning models