The Machine Learning Institute Certificate in Finance (MLI)

Prior to registration you must "Apply Online" via the MLI Certificate website. Fill in the application form and we will then contact you for the next step.

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, 24 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.

 

24 lecture weeks live in London or globally online.

 

10 - 12 learning hours per week. 2 - 3 hour lectures.

 

Final Project, Module Assignments and 3 hour Examination


MLI Structure & Flexible Payment Options

NEXT COHORT STARTS: Tuesday 1st October 2019

  • SUPER EARLY BIRD DISCOUNT: 25% Discount until 21st June 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


Python Primers: 

Python for Data Science and Artificial Intelligence

Date: Tuesday 17th September 2019

Live and Online: 09.00 – 17.00


Advanced Python Techniques 

Date: Tuesday 24th September 2019

Live and Online: 09.00 – 17.00


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: 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 Kernel Models 15-Oct-19
Module 1 Supervised Learning Ensemble Learning 22-Oct-19

Module 1 includes weekly assignments.


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 29-Oct-19
Module 2 Unsupervised Learning Dimensionality Reduction 5-Nov-19
Module 2 Unsupervised Learning Clustering Algorithms 12-Nov-19
Module 2 Unsupervised Learning Applications 19-Nov-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
Module 3 Practitioner’s Approach Problem setup and data pipeline 26-Nov-19
Module 3 Practitioner’s Approach Feature Engineering 3-Dec-19
Module 3 Practitioner’s Approach Model tuning 10-Dec-19
Module 3 Practitioner’s Approach Dealing with time series data 17-Dec-19

End of Module 3 Assignment.


Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 7th 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 7-Jan-20
Module 4 Neural Networks Backpropagation 14-Jan-20
Module 4 Neural Networks Regularisation and Optimisation 21-Jan-20
Module 4 Neural Networks Network Architectures 28-Jan-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 4-Feb-20
Module 5 Deep Learning Deep Feedforward 11-Feb-20
Module 5 Deep Learning Regularisation for Deep Nets 18-Feb-20
Module 5 Deep Learning Deep Learning Volatility & Advanced Optimisation Strategies 25-Feb-20

End of Module 5 Assignment.


Module 6 – Practical Applications:

In this module, candidates will be exposed to a selection of some of the latest practical machine learning and AI applications relevant to the financial services industry. Financial time series data presents particular challenges when it comes to applying machine learning techniques. The challenges and approaches to deal with them will be covered in this module.

Since the lectures are delivered by industry practitioners from leading institutions, the candidates will be encouraged to use the solid technical foundations built throughout the programme to interact and confidently apply and debate the problems and approaches presented.

Module 6 Faculty:

  • Francesca Lazzeri: Machine Learning Scientist, Microsoft
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 6 Practical Applications Financial Time Series Data 3-Mar-20
Module 6 Practical Applications Time Series Analysis 10-Mar-20
Module 6 Practical Applications Natural Language Processing to Predict Bond Prices 17-Mar-20
Module 6 Practical Applications Risk Models for Quant Trading 24-Mar-20

Module 6 Assignments:

Please note that the Module 6 practical hands-on assignment will not be marked or count to the final MLI assessment.

Risk Models for Quant Trading Assignment: “The assignment will amount to running a horserace backtest comparing various risk model constructions discussed in the lecture by using them to optimize quant trading alphas of the student’s choice.  To facilitate the completion of the assignment, it will provide links to the source code for the risk model constructions as well as backtesting, which the student can adapt and tweak (in the computer language of his or her choosing) for the purpose of completing the assignment.  The student will report and debate the results on the forum of the horserace backtest (return-on-capital, Sharpe ratio, cents-per-share, etc.) along with the pertinent information (backtesting period used, data source, description of the alphas, etc.).”


FINAL 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.


MLI Structure & Flexible Payment Options

NEXT COHORT STARTS: Tuesday 1st October 2019

  • SUPER EARLY BIRD DISCOUNT: 25% Discount until 21st June 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 LEVELS 1 & 2:

  • Python Primers: 
    • Python for Data Science and Artificial Intelligence
    • Advanced Python Techniques
  • 24 Lecture Weeks
  • Six Modules
  • FINAL PROJECT
  • FINAL EXAMINATION

MLI LEVEL 1: 

  • Python Primers: 
    • Python for Data Science and Artificial Intelligence
    • Advanced Python Techniques
  • Module 1 – Supervised Learning
  • Module 2 – Unsupervised Learning:
  • Module 3 – Practitioners Approach to ML:
  • Level 1: ASSIGNMENTS

MLI LEVEL 2: 

  • Module 4 – Neural Networks:
  • Module 5 – Deep Learning:
  • Module 6 – Practical Applications:
  • Level 2: ASSIGNMENTS
  • FINAL PROJECT
  • FINAL EXAMINATION

Please note that candidates must pass MLI Levels 1 and 2 to be become fully MLI certified.


MLI 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

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