Prior to the start of the MLI students are given access to our online resource in preparation for the certificate.

MLI Online Primer Resource:

• Maths Primer Refresher Material: For each topic (Linear Algebra, Optimization, Probability & Statistics), there will be a specific quiz to test initial background knowledge. We recommend that you first attempt answering the quiz exercises without looking at any material.
• Python Primer for Data Science. Presented by Nikolaos Aletras, Lecturer at The University of Sheffield
• Recorded Primer: Python for Data Science and Artificial Intelligence. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
• Recorded Primer: Advanced Python Techniques. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Additional MLI Learning Resource:

• Big Data, High-Frequency Data, and Machine Learning with kdb+/q Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas.
• Big Data and High-Frequency Data with kdb+/q Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

 

Level 1: Machine Learning Institute Certificate in Finance

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Dates:

• Level 1 Starts: Tuesday 19th October 2021

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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.

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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	19-Oct-21
Module 1	Supervised Learning	Practical: Learning from Data and Linear Models	26-Oct-21
Module 1	Supervised Learning	Theory: Ensemble Models	2-Nov-21
Module 1	Supervised Learning	Practical: Ensemble Models	9-Nov-21
Module 1	Supervised Learning	Theory: Kernel Methods	16-Nov-21
Module 1	Supervised Learning	Practical: Kernel Methods	23-Nov-21

End of Module 1 Assignment.

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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	30-Nov-21
Module 2	Unsupervised Learning	Practical Lab Session	7-Dec-21
Module 2	Unsupervised Learning	Clustering Algorithms	14-Dec-21
Module 2	Unsupervised Learning	Applications & Practical Lab Session	21-Dec-21

End of Module 2 Assignment.

Christmas Break

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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
• Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
• Phil Tooley: HPC Software Engineer, NAG (Numerical Algorithms Group)

Module 3	Practitioner’s Approach	Reproducibility and Deployment of Data Science Workflows	4-Jan-22
Module 3	Practitioner’s Approach	Feature engineering / Model tuning	11-Jan-22
Module 3	Practitioner’s Approach	Introduction to Natural Language Processing and Practical Lab Session	18-Jan-22
Module 3	Practitioner’s Approach	Using Natural Language Processing to Predict Bond Prices	25-Jan-22

End of Module 3 Assignment.

Level 2: Machine Learning Institute Certificate in Finance

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Dates:

• Level 2 Starts: Tuesday 1st February 2022

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Module 4 – Neural Networks:

Neural Network models are an important building block to many of the latest impressive machine learning applications on an industrial scale.

This module aims to develop a solid understanding of the algorithms and importantly, an appreciation for the main challenges faced in training them. The module starts with the perceptron model, introduces the key technique of backpropagation before exploring the various regularisation and optimisation routines. More advanced concepts are then covered in relation to the next module on Deep Learning.

Although we cover the theoretical foundations of Neural Networks, the emphasis of the assignments will be on hands-on lab work where the candidates are given the opportunity to experiment with the techniques studied on financial and non-financial data sets.

Module 4 Faculty:

• Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
• Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank

Module 4	Neural Networks	Perceptron Model	1-Feb-22
Module 4	Neural Networks	Backpropagation	8-Feb-22
Module 4	Neural Networks	Regularisation and Optimisation	15-Feb-22
Module 4	Neural Networks	Network Architectures	22-Feb-22

End of Module 4 Assignment.

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Module 5 – Deep Learning:

Deep Learning has been the driving engine behind many of the recent impressive improvements in the state of the art performance in large scale industrial machine learning applications.

This module can be viewed as a natural follow-up from the previous module on Neural Networks. First, the background and motivations for transitioning from traditional networks to deeper architectures are explored. Then the module covers the deep feedforward architecture, regularisation for deep nets, advanced optimisation strategies and the CNN Architecture.

The assignments of this module will be highly practical with ample opportunity to experiment on financial and non-financial data sets and become familiar with the latest open-source deep learning frameworks and tools.

Module 5 Faculty:

• Blanka Horvath: Assistant Professor, Financial Mathematics King’s College London
• Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
• Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC

Module 5	Deep Learning	Motivation and Examples	1-Mar-22
Module 5	Deep Learning	Reinforcement Learning: introduction	8-Mar-22
Module 5	Deep Learning	Reinforcement Learning: implementation	15-Mar-22
Module 5	Deep Learning	Deep Learning Volatility / Practical Lab Session	22-Mar-22

End of Module 5 Assignment.

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Module 6 – Time Series:

Time series data is an invaluable source of information used for future strategy and planning operations everywhere from finance to education and healthcare. You will be walked through the core steps of building, training, and deploying your time series forecasting models. You’ll build a theoretical foundation as you cover the essential aspects of time series representations, modeling, and forecasting before diving into the classical methods for forecasting time series data.

Module 6 Faculty:

• Francesca Lazzeri: Machine Learning Scientist, Microsoft

Module 6	Time Series	Financial Time Series Data	29-Mar-22
Module 6	Time Series	Time Series Analysis	5-Apr-22
Module 6	Time Series	Practical Lab Session	12-Apr-22

End of Module 6 Quizzes.

Final Examination: 

Examination preparation session: Tuesday 26th April 2022

Examination date: Tuesday 10th May 2022

• Candidates will sit a formal examination on a computer. The exam is taken online by students globally.

Final Project:

DATE: Friday 10th June 2022

At the end of the programme, candidates apply the theoretical and practical skills acquired to a real world application within the financial services industry.

The assessment will take into account the quality and the originality of the work as well as the clarity of its presentation.