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Head of Faculty
Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP ParibasDr. 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.
The former Managing Director in Citigroup’s Institutional Clients Business. Terry headed the Credit Trading Analysis group which develops and implements quantitative tools and strategies for credit market trading and risk management, both for Citi’s clients and for in-house applications. Some sample tools include models of corporate default and recovery values, relative value of corporate bonds, loans, and credit default swaps, credit portfolio optimization, credit derivative trades, capital structure arbitrage, measuring and hedging liquidity risk, and cross-credit-sector asset allocation.Terry received his Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine and was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering Programs at the University of California at Berkeley and the University of California at Los Angeles and Carnegie Mellon University’s Computational Finance Program.
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 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’.
Bachelor's Degree in Economics from UFRRJ (2008-2011) and Master's in Electrical Engineering from PUC-Rio, with the main subject in Support Decision Methods - Machine Learning, Statistics and Optimization (2012-2014). During the period 2014-2015 was a Research Assistant in R&D projects and Assistant Professor at PUC-Rio, and a Consultant at NanoBusiness Information and Innovation in the techmining area. Additionally, in 2015 worked as a Data Scientist at Sieve Price Intelligence, later acquired by B2W Digital SA, being responsible mainly for the automatic pricing strategies. Since 2016 is a PhD Candidate in Computer Science at University College London (UCL) in the topic of Financial Computing and Analytics. He interned during 2016-17 Nomura International plc in its Quant Strategies Desk (Fixed Income); during late 2017 and start 2018 he was at MindX as a Data Scientist, developing machine learning products for psychometrics assessment. Recently, he was 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. Its main research topics are related to Data Science, Machine Learning, Statistical Methods, Optimization, and Finance.
Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.
Your course is 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.
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.
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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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.