## Machine Learning for the Markets

This two-day course is a unique opportunity for delegates to learn the main machine learning algorithms as well as how they can be used to tackle problems in the financial markets. The course covers a wide range of techniques, from classification, clustering, dimensionality reduction to regime switching models and structural analysis of time series. Each delegate will be equipped with a PC to perform the R exercises on each topic using financial data.

March 13 to March 14, 2018 | |

Duration: Two days (9.00am to 5.00pm) | |

Location: The Tower Hotel – London E1, UK | |

Trainer: Pedro Rodrigues | |

Course fee: £1890 + VAT – Register online |

### Course Outline

###### Introduction to R and Machine Learning

+ Overview of Machine Learning and associated fields

+ Brief description of main R commands

+ Overview of main R packages for Machine Learning Analysis

###### Classification / Supervised Learning

+ Problem formulation

+ Overview of main classification algorithms:

+ SVM, Decision Tree, Neural Networks, Genetic Algorithms, Random Forests

+ Common classification problems

+ Application of classification algorithms to financial problems

+ R Exercise

###### Clustering / Unsupervised Learning

+ Problem formulation

+ Overview of main clustering algorithms (e.g.: Linear and Non-linear K-means clustering)

+ Common clustering problems

+ Application of clustering algorithms to financial problems

+ R Exercise

###### Dimensionality Reduction / Variable Selection

+ Problem formulation

+ Overview of main dimensionality reduction algorithms (e.g.: PCA, Sparse PCA)

+ Methods to select relevant variables for modelling

+ Application of dimensionality reduction algorithms to financial problems

+ R Exercise

###### Regime Switching Models

+ Problem formulation

+ Overview of main regime switching models (e.g.: Markov Switching Models)

+ Common regime switching problems

+ Application of regime switching models to financial problems

+ R Exercise

###### Structural Analysis of Time Series

+ Problem formulation

+ Overview of main metrics to characterise time series (e.g.: auto-correlation, frequency spectrum)

+ Common problems

+ Application of Structural Analysis to financial time series

+ R Exercise