# COMPGI21 - Introduction to Machine Learning

**This database contains the 2017-18 versions of syllabuses.** Syllabuses from the 2016-17 session are available here.

**Note:** Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Code | COMPGI21 |
---|---|

Year | MSc |

Prerequisites | Introductory courses covering linear algebra, calculus, probability theory and programming. |

Term | 1 |

Taught By | Iasonas Kokkinos (100%) |

Aims | To 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. |

# Content

### 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

- 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.

# Assessment

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.

# Resources

Reading list available via the UCL Library catalogue.