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.

October 3 to October 4, 2017
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