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Welcome to The Machine Learning Institute Certificate in Finance (MLI) Start Date: Tuesday 2nd October 2018

Please note cohort 1 has limited delegate places and an introductory fee and discount structure.

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.

Speakers

WELCOME TO THE MACHINE LEARNING INSTITUTE CERTIFICATE IN FINANCE (MLI)

WELCOME TO THE MACHINE LEARNING INSTITUTE CERTIFICATE IN FINANCE (MLI)

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

Prior to registration you mustApply 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. 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 comprises of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit down 3 hour 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 quantitative 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.

Next Cohort Starts: Tuesday 2nd October 2018

  • EARLY BIRD DISCOUNT: 15% Discount until 7th September 2018*    
  • 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

Please note cohort 1 has limited delegate places and an introductory fee structure.

Visit the MLI Certificate Website

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.

Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Primers start week commencing: 17th September 2018
  • Level 1 Starts: Tuesday 2nd October 2018

PRIMERS

At the start of the certificate programme, candidates are offered intensive preparation sessions which cover 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 2nd October 2018


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 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 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.


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 15th January 2019
  • Examination: Tuesday 30th April 2019
  • Final Project Hand in Friday 31st May 2019

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 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 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.


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 30th April 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 31st May 2019

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.

Online: Quantitative Trading Strategies Live Course

LIVE ONLINE: THURSDAY 5th JULY – THURSDAY 6th SEPTEMBER

This workshop is available Globally Online.

Start Time: 17.30 – 21.00 BST

Week 1: Thursday 5th July

Topic: Overview, Math Background, Trend Following, Mean-Reversion

Week 2: Thursday 12th July

Topic: More Mean-Reversion: Pairs/RV trading, Carry and Value

Week 3: Thursday 19th July

Topic: Portfolio Allocation, Equities Quant, Styles Investing and ML

Week 4: Thursday 25th July

Topic: Overfitting, Multiple testing, Covariance Penalties, Robustness and Rehash

Final Project

Summer Break

Final Project Hand-In: Thursday 30th August

Week 5: Thursday 6th September (Start Time: 17.30 BST) 
Final Project Review, Catch up & Feed Back Webinar Week

Speaker:

Dr. Nick Firoozye is a mathematician & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman Brothers doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and Deutsche Bank, and at the asset managers, Sanford Bernstein, and Citadel, in areas ranging from quantitative strategy, relative value strategy and trading, to fixed income asset allocation. He is currently Managing Director and Head of Global Derivative Strategy, part of the Quantitative Strategy Group, at Nomura. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent Ph.D. course on the same topic offered at UCL.


FAQ:

Should I attend the programme?

The course is a practitioner-orientated professional course that will enhance the short-term and long-term career prospects of anyone working in (or looking to enter) Algorithmic Trading Strategies.

When will the Quantitative Trading Strategies Live Course commence?

The course starts on Thursday 5th July.

How long is the course?

The course has four 3.5 hour lecture weeks, followed by a summer week break to work on the final project. Then a feedback / review webinar week.

How do I contact the presenter during the course?

Each lecture week will have a corresponding forum to discuss topics with the trainer and fellow students.

What is the fee structure?

There is a 20% Early Bird Discount Until Friday 22nd June.

Where do I attend the course?

The course is available globally online.

How do I access the live global streaming lectures?

The live streaming will be available on Cisco WebEx, you will be given weekly login access details.

What happens if I miss a lecture week?

All the lectures are filmed and are available for you in your Quants Hub course member’s area for the duration of the course.

How do I register to the course?

Register online or scan and send the booking form to:
Email: neil@wbstraining.com

Course Goals:

Professionals – Understand the mechanics of standard implementations of the single asset and portfolio based risk-premia trading strategies, the basis for CTAs and Quant funds, Equities Quant funds, position taking by e-traders/market-makers and a standard set of strategies in HFT. Recognize pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders. Be able to devise new and improved algorithmic strategies.

Algorithmic Traders – Recognize the reasons commonly-used strategies work, the basis for why they should, and when they don’t. Understand the statistical properties of strategies and discern the mathematically-proven from the empirical.  Acquire and improve methods to prevent overfitting.

Academics/students – Gain familiarity with the broad area of algorithmic trading strategies. Master the underlying theory and mechanics behind the most common strategies. Acquire the understanding of principals and the context necessary for new academic research into the large number of open questions in the area.


Prerequisites:

Students will be expected to have a strong grounding in statisticsTime-series statistics (e.g., as taught in signal processing, econometrics) will be very useful but not mandatory. The course will be directed towards those with some finance experience (i.e., those working in finance or actively studying financial markets). Financial markets knowledge of the basics of equities, fixed income, fx and futures, and mean-variance optimisation is assumed, although we will cover some of the background material and provide more as and if requested.

Educational Background

  • Bachelors or Masters degree (or equivalent) in
    • Hard Sciences and Engineering
    • Computer Science (with a firm understanding of mathematics)
    • Economics or Finance (with a firm knowledge of econometrics)

Readings:

These are a few of the standard readings for each topic area. More in-depth readings will be provided during the course, and are available on the Zotero Group Library (shared library) Algo Trading Library.

Forum:

The class will have a forum Slack channel which will serve as a means of ongoing communication during and in-between sessions.

Assignments:

There will be several short assignments given at the end of every class to be turned in on or before the next session, all in Python, Matlab, or R, with the goal of attaining proficiency in coding the standard strategies

Sponsors

There are limited sponsorship opportunities for this event so please contact neil@wbstraining.com
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