COMPGI21 - Introduction to Machine Learning

This database contains 2017-18 versions of the syllabuses. For current versions please see here.

PrerequisitesIntroductory courses covering linear algebra, calculus, probability theory and programming.
Taught ByIasonas Kokkinos (100%)
AimsTo have a full understanding of the learning outcomes.
Learning Outcomes

Students will become familiar with the conceptual landscape of machine learning and have developed practical skills to solve real world problems using available software.


Introduction to Supervised Learning

  • Linear Models for regression and classification
  • Concepts of overfitting and regularization, L1 and L2 regularisation
  • Na├»ve Bayes and Logistic Regression
  • Nearest Neighbour classification
  • Boosting, Decision Trees, Random Forests
  • Support Vector Machines

Introduction to Unsupervised Learning

  • K-means
  • Principal Components Analysis
  • Expectation-Maximization, Mixture of Gaussians, Factor Analysis

Deep Learning

  • Neural Networks for regression and classification
  • Unsupervised models: Autoencoders, Generative Adversarial Networks

Method of Instruction

3 hours of Lectures and 2 hour tutorial per week.


The course has the following assessment components:

  • Written Examination (2.5 hours, 70%)
  • Coursework Section (30%)

To pass this course, students must:

  • Obtain an overall pass mark of 50% for all sections combined.


Reading list available via the UCL Library catalogue.