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Welcome to The Machine Learning Institute Certificate in Finance (MLI)

Welcome to The Machine Learning Institute Certificate in Finance (MLI)

Start Date: Tuesday 20th April 2021

London & Globally Online

Quantitative finance is moving into a new era. Traditional quant skills are no longer adequate to deal with the latest challenges in finance. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight.

The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 25 lecture weeks, assignments, a practical final project and a final exam which can be taken from any global location online using our live invigilation platform.

This course has been designed to empower individuals who work in or are seeking a career in machine learning in finance. Throughout our unique MLI programme, candidates work with 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 MLI is a career-enhancing professional qualification, that can be taken worldwide.

Faculty & Advisory Board

MLI Certificate Brochure

The Machine Learning Institute Certificate in Finance (MLI)

NEXT COHORT STARTS: Tuesday 20th April 2021

  • SUPER EARLY BIRD DISCOUNT: 30% until 15th January 2021
  • EARLY BIRD DISCOUNT: 25% until 26th  February 2021
  • EARLY BIRD DISCOUNT: 15% until 26th March 2021
  • VOLUME DISCOUNT: If 2 or more people from your institution wish to take The MLI Certificate please contact us
  • REGIONAL OFFERS: Get in touch for offers in your geographic region

*Not to be used in conjunction with other offers


MLI Online Resource Library

Prior to the start of the MLI students are given access to our online resource in preparation for the certificate.
  • Python Primer for Data Science. Presented by Nikolaos Aletras, Lecturer at The University of Sheffield
  • Python for Data Science and Artificial Intelligence Workshop. Presented by Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • 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.

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.

Python Primers: 

Python for Data Science and Artificial Intelligence

Date: Tuesday 13th April 2021

Live and Online: 09.00 – 17.00

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

Advanced Python Features and Putting them to use in Practice

High Performance Python

Date: Thursday 15th April 2021

Live and Online: 09.00 – 17.00

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Mike Croucher: Technical Evangelist, NAG (Numerical Algorithms Group)

Final Examination: 

Examination preparation lecture: Tuesday 2nd November 2021

Examination date: Tuesday 16th November 2021

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

Final Project:

DATE: Friday 17th December 2021

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.

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.

Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 27th June 2021

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 27-Jul-21
Module 4 Neural Networks Backpropagation 03-Aug-21
Module 4 Neural Networks Regularisation and Optimisation 31-Aug-21
Module 4 Neural Networks Network Architectures 7-Sept-21

End of Module 4 Assignment.


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 14-Sept-21
Module 5 Deep Learning Reinforcement Learning: introduction 21-Sept-21
Module 5 Deep Learning Reinforcement Learning: implementation 28-Sept-21
Module 5 Deep Learning Deep Learning Volatility / Practical Lab Session 5-Oct-21

End of Module 5 Assignment.


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 12-Oct-21
Module 6 Time Series Time Series Analysis 19-Oct-21
Module 6 Time Series Practical Lab Session 26-Oct-21

End of Module 6 Quizzes.

Sample Lectures, Information Sessions, Discount & Structure

Information Sessions:

Thought Leadership:

Sample Lectures:

Flexible Payment Options:

The MLI offers several flexible payment options where candidates can pay for the course by instalments.

Option 1:

  • Pay in full

Option 2:

  • Full course: Pay 50% on registration and 50% in lecture week 12
  • Level 1: Pay 50% on registration and 50% in lecture week 11
  • Level 2: Pay 50% on registration and 50% in lecture week 24

Option 3:

  • Full course: Pay £1000 on registration, 50% of remaining balance in lecture week 10 and the final 50% in lecture week 22
  • Level 1: Pay £1000 on registration, 50% of remaining balance in lecture week 6 and the final 50% in lecture week 11
  • Level 2: Pay £1000 on registration, 50% of remaining balance in lecture week 18 and the final 50% in lecture week 24

MLI Early Birds, Group Discounts & Regional Offers

NEXT COHORT STARTS: Tuesday 13th October 2020

Discount Structure

  • SUPER EARLY BIRD DISCOUNT: 30% UNTIL 15th JANUARY 2021
  • EARLY BIRD DISCOUNT: 25% UNTIL 26th  FEBRUARY 2021
  • EARLY BIRD DISCOUNT: 15% UNTIL 26th MARCH 2021
  • VOLUME DISCOUNT: If 2 or more people from your institution wish to take The MLI Certificate please contact us
  • REGIONAL OFFERS: Get in touch for offers in your geographic region

The MLI Certificate offers a global regional fee structure so please apply.

APPLY ONLINE

MLI Certificate Online Application. Fill in the below form and we will then contact you for the next stage. Fee details available on the Brochure.


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    Organisation

    Delivered by The MLI

    Delivered by The MLI

    The MLI faculty is composed of highly experienced machine learning and quantitative finance experts from leading financial and academic institutions. The faculty is responsible for the ownership and the delivery of the course content including lectures material, assignments, projects and the final examination.

    Partnered by

    Partnered by

    The Numerical Algorithms Group (NAG). The NAG Library for Python is a comprehensive collection of functions for the solution of numerical, statistical inc. machine learning problems. The Library is divided into chapters, each devoted to a branch of numerical analysis or statistics. Students and alumni of the MLI may use the NAG Library (Python or other flavours e.g. NAG C Library) for non-commercial usage i.e. for learning and projects relating to the Machine Learning Institute Certificate as well as personal educational usage. Students should request licence keys quoting reference MLI/NAG and are entitled to support and help from NAG via support@nag.co.uk

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