COMPGI21 - Introduction to Machine Learning
This database contains the 2017-18 versions of syllabuses.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
|Prerequisites||Introductory courses covering linear algebra, calculus, probability theory and programming.|
|Taught By||Iasonas Kokkinos (100%)|
|Aims||To have a full understanding of the 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
- Naïve Bayes and Logistic Regression
- Nearest Neighbour classification
- Boosting, Decision Trees, Random Forests
- Support Vector Machines
Introduction to Unsupervised Learning
- Principal Components Analysis
- Expectation-Maximization, Mixture of Gaussians, Factor Analysis
- 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.