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
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 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 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 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.
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.
The 2nd Machine Learning & AI in Quantitative Finance Conference USA: New York City 14th – 16th November
SPECIAL OFFER When two colleagues attend the 3rd goes free!
20% Early Bird Discount until Friday October 5th 2018
Pre-Conference Workshop day: Wednesday November 14th 2018
Machine Learning, AI, & FinTech in the Capital Markets by Sol Steinberg
The capital markets have changed forever, the machines are replacing decision making, order flow, risk management, valuation, and much more. Explore these changes and be prepared to add value in the transformation of the capital markets.
The Course: 09.00 – 17.30
The AI impact on capital markets has never been profound as it is in the present times. AI has certainly taken the finance world, especially banking and investment services by storm. Artificial Intelligence is a suite that comprises of a set of tools- like machine learning, natural language processing, deep neural networks etc. that are impacting almost every industry, in the most efficient way. At its core, AI is essentially a set of technologies that are meant to augment or perform human tasks, without their intervention. Over a few decades, these technologies have evolved to sense, learn, comprehend, and act. Such a progression now enable systems and software to acquire, identify, recognize, and an analytical database (both structured and unstructured), derive insights, envision the process, and then put them into the real-time use cases. In context to capital market, AI is enabling the machines to do algorithmic trading, qualitative analysis, automate trade execution, and manage risk. What turns AI set a disruption in almost every industry is its decision making ability, which is based on cognitive learning. In contrast to perpetuating up on the programmed responses, AI overcome the limitations, complexities, and challenges by teaching the system to learn through past experiences.
Despite being the hot buzzword on Wall Street these days, machine learning is still fairly misunderstood. It is not artificial intelligence itself, but rather a form of it in which computers fed extremely large data sets are able to learn as changes in that data occur without being explicitly programmed to do so. The data is just one part of the approach, what can be more challenging is making machine learning and data science a core capability among companies so that they instinctively take internal and external data sets and interpret it for patterns, risks, and opportunities. Machine learning is shifting your trading counterparty to engineers and quants, it is critical you understand this evolution.
Modern Market structure looking beyond 2020: The rise of alternative technology, marketplaces, and products such as exchange traded derivatives, and crypto currencies.
HFT, Connectivity, & AI in Trading- Have we hit a wall? How competitors have reached critical mass
Big Data in the financial eco-system: Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
Machine Learning Models: what is your best fit use in your business?
Machine learning in finance – Opportunities and challenges
Downtown Conference Center
157 William Street
New York, NY 10038
Tel: +1 212 618 6990
Sol Steinberg is a OTC Markets Subject Matter Expert and specializes in Risk Management, OTC derivatives, Market structure, Collateral, Trade Lifecycle, Valuation, Financial Technology Systems, Strategic development, and Monetization.
Sol is the founding principle of his firm, OTC partners. OTC partners is a boutique value add firm that specializes in research, content, development. Before starting OTC Partners Sol was a senior executive at the world’s leading clearing house LCH.Clearnet. Sol also spent nine years on the buy side and Citi, performing product development, risk management, and valuation for the OTC markets.
Sol has a wide-ranging network of asset managers, analytic providers, execution venues, regulatory, and government contacts. He used his eco system to successfully commercialize analytics, data, and other non commercialized intellectual property and had significant monetization success. He brought to market several initiatives, including institutional and commercial risk engines such as SMART tool, Risk Explorer, Global Market Risk System for Citi: the largest VaR engine in the world from 2004 to 2006, as well as developing CCP2 – a derivative education & certification program for leading consultancies. Sol also contributed to OTC industry’s clearing and default management policies for the cleared OTC swap markets as well as contributed to industry standard risk analytics in times of low market rates.
Waters Magazine’s award “Best risk analytics initiative 2012” & “Best risk analytics initiative (Sell Side) 2013”
FTF’s award for “Most cutting edge risk contribution 2013” for developing the SMART risk analytics tool.
Global nominee in 2012 for “Best Practices in Global Financial Risk Management” from PRMIA, Professional Risk Managers International Association.
Main Conference Day 1:
08.30 – 09.00: Registration and Morning Welcome Coffee
09.00 – 10.00: Keynote Speech: Machine Learning and AI in Finance: Applications, Cases and Research
MARCELO LABRE: Executive Director, Morgan Stanley
10.00 – 10.45: Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics
We review some new approaches from research and literature and Wells Fargo’s work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions
BERNHARD HIENTZSCH: Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo
MIQUEL NOGUER ALONSO: Adjunct Assistant Professor, COLUMBIA UNIVERSITY
In discrete time, option hedging and pricing amount to sequential risk minimization. In particular, a discrete-time version of the Black-Scholes-Merton (BSM) option pricing model can be formulated as a problem of dynamic Markowitz optimization of an option replicating (hedge) portfolio made of an underlying stock and cash. This talk shows how this problem can be approached using Reinforcement Learning (RL). Once the problem is posed as an RL problem, option pricing and hedging can be done without any model for the underlying stock dynamics, using instead model-free, data-driven RL methods such as Q-learning and Fitted Q Iteration. As a result, both option price and hedge are obtained by a well-defined and converging maximization problem that uses only market prices and option trading data (inter-temporal re-hedges and hedge losses in the replicating portfolio) to find the optimal option hedge and price. The model can learn when re-hedges in data are suboptimal/noisy, or even purely random. This means, in particular, that our RL model can learn the BSM model itself, if the world is according to BSM.
Computationally, the RL-based option pricing model is very simple, as it uses only basic linear algebra and linear regressions to compute the option price and hedge. The only tunable parameters in this approach are parameters defining the optimal hedge and price themselves. This approach does not need any model calibration (as there is no model anymore), and it automatically solves the volatility smile problem of the BSM model. We also discuss some extensions of this approach, including in particular an Inverse Reinforcement Learning setting, where inter-temporal losses from re-hedges are unobservable.
IGOR HALPERIN: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering
12.45 – 13.45: Lunch
TERRY BENZSCHAWEL: Founder and CEO, Lambda Financial, LLC.
ARIK BEN DOR: Managing Director and Head of Quantitative Equity Research, Barclays
KSENIA SHNYRA: Senior Advisor, Deloitte
16.30 – 17.30: Machine Learning & Ai in Quantitative Finance Panel
Main Conference Day 2:
09.00 – 09.45: Keynote Speech “AI-driven ESG / SDG Strategies for Investment and Risk Management”
The key discussion points will include:
RICHARD V. ROTHENBERG: Global AI Corporation & Research Affiliate, Lawrence Berkeley National Laboratory
JOSEPH SIMONIAN: Director of Quantitative Research, Portfolio Research & Consulting Group, Natixis Investment Managers
10.30 – 11.00: Morning Break and Networking Opportunities
Abstract: Machine learning is rapidly transforming the field of quantitative finance. In this talk, we discuss how two distinct subfields of machine learning, namely reinforcement learning and supervised learning, can be combined into a single model that harvests the power of reinforcement learning in handling multi-period problems with delayed rewards and costs, and simultaneously harvests the power of supervised-learning to learn the structure of a non-linear model with interactions. Our technique fuses the two within the framework of generalized policy iteration by generating training sets which are then used by the supervised learner to learn a better representation of the action-value function, which is then used to generate a better training set for the next iteration. We show that our method outperforms tabular Q-learning in a simulation involving trading a very illiquid asset, and can handle discrete as well as continuous predictors.
GORDON RITTER: Senior Portfolio Manager, GSA Capital
I describe specific opportunities and challenges of leveraging machine learning within context of quantitative investment and wealth management. Such applications include forecasting of financial time series, classification of alternative data, identification of market regimes, clustering-based portfolio diversification, assessment of risk factors.
CRISTIAN HOMESCU: Director, Portfolio Analytics Bank of America Merrill Lynch
12.30 – 13.30: Lunch
PETER CARR: Professor and Dept. Chair of FRE Tandon, New York University
AMIT SRIVASTAV:Executive Director, Quantitative Analytics Group (Model Risk), Morgan Stanley
14.45 – 15.00: Afternoon Break
MICHAEL BEAL: CEO, Data Capital Management
SHENGQUAN ZHOU:Quantitative Researcher, Bloomberg LP
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