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

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

15% Early Bird Discount until Friday 13th September 2019

Start Date: Tuesday 1st October 2019

The updated certificate now includes 25 lecture weeks, our new Partnership with NAG Numerical NAG (Numerical Algorithms Group), additional practical lab sessions, an extended module 1 on Supervised Learning, new topic updates on Cloud Computing, Natural Language Processing, Practicalities of Neural Networks: CNN, Advanced Practicalities of Neural Networks: Generative NN, and a new full module on Times Series.

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, lab assignments, a practical final project and a final sit down examination using our global network of examination centres.

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 1st October 2019

  • EARLY BIRD DISCOUNT: 15% Discount until 13th September 2019*
  • 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 17th September 2019

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: Tuesday 24th September 2019

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 7th April 2020

Examination date: Tuesday 21st April 2020

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students.

Final Project:

DATE: Friday 22nd May 2020

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 1st October 2019

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 & Module 1: Tuesday 1st October

Welcome to the MLI Faculty:

  • Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
  • Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
  • Adriano Soares Koshiyama: The Alan Turing Institute

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 assignments:

Module 1 Supervised Learning Learning from Data 1-Oct-19
Module 1 Supervised Learning Linear Models 8-Oct-19
Module 1 Supervised Learning Practical Lab Session 15-Oct-19
Module 1 Supervised Learning Kernel Models  22-Oct-19
Module 1 Supervised Learning Ensemble Learning 29-Oct-19
Module 1 Supervised Learning Practical Lab Session 5-Nov-19

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 12-Nov-19
Module 2 Unsupervised Learning Practical Lab Session / Cloud Computing 19-Nov-19
Module 2 Unsupervised Learning Clustering Algorithms 26-Nov-19
Module 2 Unsupervised Learning Applications & Practical Lab Session 3-Dec-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, JP Morgan Chase & Co
  • Mike Croucher: Technical Evangelist, NAG (Numerical Algorithms Group)
Module 3 Practitioner’s Approach Reproducibility and Deployment of Data Science Workflows 10-Dec-19
Module 3 Practitioner’s Approach Feature Engineering / Model tuning 17-Dec-19
Module 3 Practitioner’s Approach Introduction to Natural Language Processing and Practical Lab Session 7-Jan-20
Module 3 Practitioner’s Approach Using Natural Language Processing to Predict Bond Prices 14-Jan-20

End of Module 3 Assignment.

Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 21st January 2020

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 21-Jan-20
Module 4 Neural Networks Backpropagation 28-Jan-20
Module 4 Neural Networks Regularisation and Optimisation 04-Feb-20
Module 4 Neural Networks Network Architectures 11-Feb-20

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:

  • Harsh Prasad: Vice President, Morgan Stanley
  • Blanka Horvath: Assistant Professor, Financial Mathematics King’s College London
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 5 Deep Learning Motivation and Examples 18-Feb-20
Module 5 Deep Learning Practicalities of Neural Networks: CNN 25-Feb-20
Module 5 Deep Learning Practicalities of Neural Networks: Generative NN 03-Mar-20
Module 5 Deep Learning Deep Learning Volatility / Practical Lab Session 10-Mar-20

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 17-Mar-20
Module 6 Time Series Time Series Analysis 24-Mar-20
Module 6 Time Series Practical Lab Session 31-Mar-20

End of Module 6 Quizzes.

Sample Lectures, Information Sessions, Discount & Structure

Sample Lectures

MLI Discount & Flexible Payment Options:

NEXT COHORT STARTS: Tuesday 1st October 2019

  • EARLY BIRD DISCOUNT: 15% until 13th September 
  • 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 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

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

MLI Information Session

Recorded MLI Information Session: The Machine Learning Institute Certificate in Finance (MLI)

This information session gives you the chance to ask the MLI faculty, the questions that matter to your MLI journey.

Webinar details:

Recorded MLI Information Session: Machine Learning Institute Certificate in Finance

Recording Link

Password: Learning2019

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

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

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.

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