Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Level 1 Starts: Tuesday 20th April 2021

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.


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: Research Fellow in Computer Science, University College London

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

Module 1 Welcome to The MLI. Supervised Learning Theory: Learning from Data and Linear Models 20-Apr-21
Module 1 Supervised Learning Practical: Learning from Data and Linear Models 27-Apr-21
Module 1 Supervised Learning Theory: Ensemble Models 4-May-21
Module 1 Supervised Learning Practical: Ensemble Models 11-May-21
Module 1 Supervised Learning Theory: Kernel Methods 18-May-21
Module 1 Supervised Learning Practical: Kernel Methods 25-May-21

End of Module 1 Assignment.


Module 2 – Unsupervised Learning:

An important and challenging type of machine learning problems 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.

Module 2 Faculty:

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co

There are theoretical and applied assignments with financial data sets.

Module 2 Unsupervised Learning Introduction & Dimensionality Reduction 1-Jun-21
Module 2 Unsupervised Learning Practical Lab Session 8-Jun-21
Module 2 Unsupervised Learning Clustering Algorithms 15-Jun-21
Module 2 Unsupervised Learning Applications & Practical Lab Session 22-Jun-21

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, JP Morgan Chase & Co
  • Mike Croucher: Technical Evangelist, NAG (Numerical Algorithms Group)
Module 3 Practitioner’s Approach Reproducibility and Deployment of Data Science Workflows 29-Jun-21
Module 3 Practitioner’s Approach Feature engineering / Model tuning 6-Jun-21
Module 3 Practitioner’s Approach Introduction to Natural Language Processing and Practical Lab Session 13-Jun-21
Module 3 Practitioner’s Approach Using Natural Language Processing to Predict Bond Prices 20-Jun-21

End of Module 3 Assignment.