Machine Learning Institute strap line
Head of Faculty
Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.
In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.
Francesca Lazzeri is a machine learning scientist on the cloud advocacy team at Microsoft. An expert in big data technology innovations and the applications of machine learning-based solutions to real-world problems, she has worked with these issues in a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation.
Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.
The former Managing Director in Citigroup’s Institutional Clients Business. Terry headed theCredit Trading Analysis group which develops and implements quantitative tools and strategiesfor credit market trading and risk management, both for Citi’s clients and for in-houseapplications. Terry received his Ph.D. in Experimental Psychology from Indiana University (1980)and his B.A. (with Distinction) from the University of Wisconsin (1975).
Recently, an intern at the AI Labs in Goldman Sachs, working as a Machine Learning Strats.Nowadays he is part of the Alan Turing Institute as a Enrichment Scheme Student. Itsmain research topics are related to Data Science, Machine Learning, Statistical Methods,Optimization, and Finance. A PhD Candidate in Computer Science at University CollegeLondon (UCL) in the topic of Financial Computing and Analytics. Adriano has a Bachelor’sDegree in Economics from UFRRJ and a Master’s in Electrical Engineering from PUC-Rio.
Zura Kakushadze received his Ph.D. in theoretical physics from Cornell University at 23, wasa Postdoctoral Fellow at Harvard University, and an Assistant Professor at C.N. Yang Institutefor Theoretical Physics at Stony Brook University. He received an Alfred P. Sloan FoundationFellowship in 2001. After expanding into quantitative finance, he was a Director at RBCCapital Markets, Managing Director at WorldQuant, Executive Vice President and substantialshareholder at Revere Data (now part of FactSet).
Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.
Ivan Zhdankin is a quantitative researcher with experience in diverse areas of quantitative finance, including risk modelling, XVA, and electronic trading across asset classes, including commodity futures and G10 and emerging market currencies. Ivan was consulting various banks in quantitative modeling and has recently joined JP Morgan as a quantitative analyst. He has become one of the first researchers to generate convincing results in electronic alpha with neural nets. He has a solid mathematical background from New Economic School and Moscow State University, where he studied under the celebrated Albert Shiryaev, one of the developers of modern probability theory.
Helyette GEMAN is a Professor of Mathematical Finance at Birkbeck – University of London and at Johns Hopkins University. She is a Graduate of Ecole Normale Supérieure in Mathematics, holds a Masters degree in Theoretical Physics, a PhD in Probability from the University Pierre et Marie Curie and a PhD in Finance from the University Pantheon Sorbonne.She has been a scientific advisor to a number of major energy and mining companies for the last 20 years, covering the trading of crude oil, natural gas, electricity as well as metals in companies such as EDF Trading, Louis Dreyfus or BHP Billiton and was named in 2004 in the Hall of Fame of Energy Risk.Prof Geman was previously the head of Research and Development at Caisse des Depots. She has published more than 140 papers in major finance journals including the Journal of Finance, Mathematical Finance, Journal of Financial Economics, Journal of Banking and Finance and Journal of Business. She has also written the book entitled Insurance and Weather Derivatives and is a Member of Honor of the French Society of Actuaries.
Piotr Karasinski is a pioneering quantitative analyst, best known for the Black–Karasinski short rate model which he co-developed with the late Fischer Black. His contributions to quantitative finance include models for interest rates, equity and hybrid products and random volatility.He is currently Senior Advisor at the European Bank for Reconstruction and Development. He is on the editorial board of the journal, Quantitative Finance.  Previously, he has held a number of positions at leading firms in New York and London including: Managing Director at HSBC, Director and Head Derivatives Research at Citibank, MD at Chemical Bank, Director at Deutsche Bank and Vice President at Goldman Sachs.He studied physics at Warsaw University (MSc 1978) and earned his PhD at Yale University (1984).https://en.wikipedia.org/wiki/Piotr_Karasinski
Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.
The MLI Python Primers are designed to take you from the very foundations to state-of-the-art use of modern Python libraries.
You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn to use distributed task queues (Celery), learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries (Scikit-Learn, Keras, TensorFlow, and Theano), and complete a realistic, real-life data science lab.
Advanced Python Features and Putting them to use in Practice.
Distributed Computing with Python.
Prior to the start of the MLI students are given access to our online resource in preparation for the certificate.
Additional MLI Learning Resource:
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 week: Tuesday 23rd April
Welcome to the MLI by the Head of Faculty:
Guest Lecturer introducing current market trends in Machine Learning.
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:
The module includes a combination of theoretical and hands-on lab assignments:
Module 1 includes weekly assignments.
Module 2 – Unsupervised Learning:
An important and challenging type of machine learning problem 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 Faculty:
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:
End of Module 3 Assignment.
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 Assignment over summer break.
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:
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:
Module 6 Assignments:
Please note that the Module 6 practical hands-on assignment will not be marked or count towards 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.).”
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.
NEXT COHORT STARTS: Tuesday 23rd April 2019
*Not to be used in conjunction with other offers
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.
MLI Flexible Payment Options:
The MLI offers several flexible payment options where candidates can pay for the course by instalments.
The MLI Certificate offers a global regional fee structure so please apply.
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Undergraduate/Postgraduate and other relevant qualifications (i.e. BSc; MBA; etc.)
Main subject(s) studied
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Personal statement / funding:
Personal statement: Why you want to undertake the MLI, how it will
benefit your future career/progression and your aspirations for the future?
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Source of Funding
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.
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.
The Machine Learning Institute Certificate in Finance (MLI) is powered by The Quants Hub Learning Platform.