Machine Learning for the Markets

Network

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.

13 - 14 March 2018

Duration:

2 Days (9am to 5pm)

Location:

London, UK – Tower Hotel, London E1

Trainer:

Pedro Rodrigues

Course Fee:

£1890 + VAT

Register

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 Algorithms (SVM, Decision Trees, neural networks, genetic algo, 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

 

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