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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 23rd April 2019

Prior to registration you must "Apply Online". 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.

Faculty

MLI Certificate brochure

The Machine Learning Institute Certificate in Finance (MLI)

Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Level 1 Starts: Tuesday 23rd April 2019

PRIMERS

At the start of the certificate programme, candidates are offered intensive preparation which covers the technical foundations required in order to follow and fully benefit from the course lectures.

Although these sessions are optional, they are highly recommended. For candidates with the required background, they can serve as a timely refresher ahead of the main module lectures.

Primer in Mathematical Methods:

This course provides a rigorous introduction to the key mathematical concepts and methods required during the machine learning lessons. The following areas are covered, with a clear focus on the concepts and techniques most used in machine learning:

  • Probability
  • Statistics
  • Linear Algebra
  • Optimisation Methods

Primer in Python Programming for Machine Learning:

This intensive hands-on session introduces the Python programming language and the most useful scientific computing tools it offers.

Th scope includes:

  • Python fundamentals
  • Data structures
  • Interactive Notebooks
  • Numpy
  • Pandas
  • Plotting tools
  • Scikit-learn
  • Overview of machine learning packages

Level 1 Starts: Tuesday 23rd April 2019


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.

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

Module 1 Supervised Learning Learning from Data 23-Apr-19
Module 1 Supervised Learning Linear Models 30-Apr-19
Module 1 Supervised Learning Kernel Models 7-May-19
Module 1 Supervised Learning Ensemble Learning 14-May-19

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.

There are theoretical and applied lab assignments with financial data sets.

Module 2 Unsupervised Learning Introduction 21-May-19
Module 2 Unsupervised Learning Dimensionality Reduction 28-May-19
Module 2 Unsupervised Learning Clustering Algorithms 4-Jun-19
Module 2 Unsupervised Learning Applications 11-Jun-19

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 Practitioner’s Approach Problem Setup and Data Pipeline 18-Jun-19
Module 3 Practitioner’s Approach Feature Engineering 25-Jun-19
Module 3 Practitioner’s Approach Exploration, Maximum Value Hypothesis 2-Jul-19
Module 3 Practitioner’s Approach Model Tuning 9-Jul-19

LAB 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. The following are examples of the type of topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 16th July 2019
  • Examination: Tuesday 26th November 2019
  • Final Project Hand in Friday 3rd 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 Neural Networks Perceptron Model 16-Jul-19
Module 4 Neural Networks Backpropagation 23-Jul-19
Module 4 Neural Networks Regularisation and Optimisation 30-Jul-19
Module 4 Neural Networks Network Architectures 6-Aug-19

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 Deep Learning Motivation and Examples 3-Sep-19
Module 5 Deep Learning Deep Feedforward 10-Sep-19
Module 5 Deep Learning Regularisation for Deep Nets 17-Sep-19
Module 5 Deep Learning Advanced Optimisation Strategies 24-Sep-19

Module 6 – Advanced Topics:

In this module, candidates will be exposed to a selection of some of the latest machine learning and AI topics relevant to the financial services industry.

Financial timeseries data presents particular challenges when it comes to applying machine learning techniques. These challenges and approaches to deal with them will be covered.

Also, building on the previous module, deep models for timeseries based on the RNN architecture and Long Short-Term Memory will be presented.

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 debate about the problems and approaches presented.

Module 6 Advanced Topics Advanced Topic 1 1-Oct-19
Module 6 Advanced Topics Advanced Topic 2 8-Oct-19
Module 6 Advanced Topics Advanced Topic 3 15-Oct-19
Module 6 Advanced Topics Advanced Topic 4 22-Oct-19

LAB 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. The following are examples of the topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

FINAL EXAMINATION: 

DATE: Tuesday 26th November 2019

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 3rd January 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 23rd April 2019

  • SUPER EARLY BIRD DISCOUNT: 25% Discount until 22nd February 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:

  • Primer in Mathematical Methods
  • Primer in Python Programming for Machine Learning:
  • 24 Lecture Weeks
  • Six Modules
  • FINAL PROJECT
  • FINAL EXAMINATION

MLI LEVEL 1: 

  • Primer in Mathematical Methods
  • Primer in Python Programming for Machine Learning:
  • Module 1 – Supervised Learning
  • Module 2 – Unsupervised Learning:
  • Module 3 – Practitioners Approach to ML:
  • Level 1: LAB ASSIGNMENTS

MLI LEVEL 2: 

  • Module 4 – Neural Networks:
  • Module 5 – Deep Learning:
  • Module 6 – Advanced Topics:
  • Level 2: LAB 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.

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 WBS Training

Partnered by WBS Training

WBS Training is one of the oldest quantitative finance training companies in world, founded in 2000. The wealth of experience gathered over the years will now be brought to fruition in our exciting new joint venture with The Machine Learning Institute.

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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