Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Level 1 Starts: Tuesday 23rd April 2019

LAB ASSIGNMENTS:

Throughout the programme, candidates work on hands-on assignments designed to illustrate the algorithms studied and to experience first hand the practical challenges involved in the design and successful implementation of machine learning models. The data sets and problems are selected to be representative of the applications encountered in finance. 


Introduction week: Tuesday 23rd April

Welcome to the MLI by the Head of Faculty: 

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Guest Lecturer introducing current market trends in Machine Learning.

  • To be confirmed

Module 1 – Supervised Learning:

In this module, the concepts related to algorithmically learning from data are introduced. The candidates are given an early taste of a supervised machine learning application before going through the fundamental building blocks starting from linear regression and classification models to kernels and the theory underpinning support vector machines and then to the powerful techniques of ensemble learning.

Module 1 Faculty:

  • Adriano Soares Koshiyama: The Alan Turing Institute

The module includes a combination of theoretical and hands-on lab assignments:

Module 1 Supervised Learning Learning from Data 30-Apr-19
Module 1 Supervised Learning Linear Models 7-May-19
Module 1 Supervised Learning Kernel Models 14-May-19
Module 1 Supervised Learning Ensemble Learning 21-May-19

Module 1 includes weekly assignments.


Module 2 – Unsupervised Learning:

An important and challenging type of machine learning problem in finance is learning in the absence of ‘supervision’, or without labelled examples.

In this module, we first introduce the theoretical framework of hidden variable models. This family of models is then used to explore the two important areas of dimensionality reduction and clustering algorithms. There are theoretical and applied lab assignments with financial data sets.

Module 2 Faculty:

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Ivan Zhdankin: Associate, Quantitative Analyst, JPMorgan Chase & Co
Module 2 Unsupervised Learning Introduction 28-May-19
Module 2 Unsupervised Learning Dimensionality Reduction 4-Jun-19
Module 2 Unsupervised Learning Clustering Algorithms 11-Jun-19
Module 2 Unsupervised Learning Applications 18-Jun-19

End of Module 2 Assignment.


Module 3 – Practitioners Approach to ML:

This module focuses on the practical challenges faced when deploying machine learning models within a real life context.

Each session in this module covers a specific practical problem and provides the candidates with guidance and insight about the way to approach the various steps within the model development cycle, from data collection and examination to model testing and validation and results interpretation and communication.

Module 3 Faculty:

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Ivan Zhdankin: Associate, Quantitative Analyst, JPMorgan Chase & Co
Module 3 Practitioner’s Approach Problem setup and data pipeline 25-Jun-19
Module 3 Practitioner’s Approach Feature Engineering 2-Jul-19
Module 3 Practitioner’s Approach Model tuning 9-Jul-19
Module 3 Practitioner’s Approach Dealing with time series data 16-Jul-19

End of Module 3 Assignment.