The goal of the course is to equip students with machine learning tools to analyse high-dimensional data, in particular those that are frequently used in quantitative finance.

(LO1) Load, process and tokenise data for ML

(LO2) Apply appropriate unsupervised learning techniques for gaining insight on high-dimensional data

(LO3) Apply kernel methods to solve supervised non-linear classification problems

(LO4) Describe and show the convergence of gradient descent methods

(LO5) Deploy and train a feed-forward neural networks on a given data set

(LO6) Draw inferences from the implemented and trained models

(S1) Analytical and problem-solving skills

(S2) Digital fluency

(S3) Effective communication with a range of stakeholders

Introduction
- What is machine learning?
- Applications and problems
- Classes of learning problems
- Machine learning setup and scenarios

Visualisation of high-dimensional data
- Principal Component Analysis (PCA)
- Introduction to t-distributed Stochastic Neighbour Embedding (t-SNE)

Kernel methods
- Non-linear classification
- Definition and properties of positive definite symmetric (PDS) kernels
- The Reproducing Kernel Hilbert Space (RKHS) theorem and Representer theorem

Optimisation techniques
- Logistic regression
- Maximum likelihood principle
- Gradient descent
- Stochastic gradient descent

Neural Networks
- Definition of a neural network
- Backpropagation
- Universal approximation theorems

Boosting
- The boosting learning paradigm
- The AdaBoost algorithm

Combination of neural networks, VAEs and the latent space

Generative methods, GANs, RNNs and practical examp les