Machine Learning & AI in Quantitative Finance Conference, London: 16th - 17th November 2017 & Blockchain Developments in Financial Markets Conference, London: 23rd & 24th November 2017
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. 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.
*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:
MLI LEVEL 1:
MLI LEVEL 2:
Please note that candidates must pass MLI Levels 1 and 2 to be become fully MLI certified.
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:
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:
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
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.
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:
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.
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:
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.
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.
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 Hand-In: Thursday 30th August
Week 5: Thursday 6th September (Start Time: 17.30 BST)
Final Project Review, Catch up & Feed Back Webinar Week
Online: Quantitative Trading Strategies Live Course
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.
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:
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.
Students will be expected to have a strong grounding in statistics. Time-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.
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.
The class will have a forum Slack channel which will serve as a means of ongoing communication during and in-between sessions.
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
MoCaX Intelligence is a new-to-the-market algorithm that accelerates existing Risk Engines without the need for complex systems development or expensive hardware upgrades. MoCaX removes the pricing step bottle-neck that often uses over 90% of computational effort in existing engines and increases capabilities by several orders of magnitude with no loss of accuracy.MoCaX builds on the new Algorithmic Pricer Acceleration (APA) and Algorithmic Greeks Acceleration (AGA) methods. APA synthesises your existing pricers and creates an accelerated version of them. Even your very slowest and complex pricer, passed through MoCaX, will return the same results (down to 10-15 precision) ultra-fast (up to a few nanoseconds). For example, this enables highly accurate Monte Carlo within Monte Carlo in an instant.Please ask for a free version of MoCaX so you can test it for yourself: firstname.lastname@example.org
TriOptima provides risk management services for OTC derivatives, reducing costs and eliminating operational and credit risk through a range of services.triResolve for proactive reconciliation of OTC derivative portfolios, repository validation and dispute resolutiontriReduce for multilateral portfolio compression services across OTC product typestriBalance for rebalancing counterparty risk exposure between multiple CCPs and bilateral relationshipstriCalculate for the complete spectrum of counterparty credit risk analytics leveraging state-of-the-art massively parallel computing devicesTriOptima maintains offices in London, New York, Singapore, Stockholm, and Tokyo.
RavenPack is the leading big data analytics provider for financial services. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world. www.ravenpack.com
The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.http://www.thalesians.com/
Over the years, financial professionals around the world have looked to Wiley and the Wiley Finance series with its wide array of bestselling books for the knowledge, insights, and techniques that are essential to success in financial markets. As the pace of change in financial markets and instruments quickens, Wiley continues to respond. With critically acclaimed books by leading thinkers on value investing, risk management, asset allocation, and many other critical subjects, the Wiley Finance series provides the financial community with information they want. Written to provide professionals and individuals with the most current thinking from the best minds in the industry, it is no wonder that the Wiley Finance series is the first and last stop for financial professionals looking to increase their financial expertise.www.wiley.com www.wileyglobalfinance.com