Level 2: Machine Learning Institute Certificate in Finance


  • Level 2 Starts: Tuesday 7th January 2020

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 Faculty:

  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
  • Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
Module 4 Neural Networks Perceptron Model 7-Jan-20
Module 4 Neural Networks Backpropagation 14-Jan-20
Module 4 Neural Networks Regularisation and Optimisation 21-Jan-20
Module 4 Neural Networks Network Architectures 28-Jan-20

End of Module 4 Assignment.

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:

  • Harsh Prasad: Vice President, Morgan Stanley
  • Blanka Horvath: Assistant Professor, Financial Mathematics King’s College London
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 5 Deep Learning Motivation and Examples 4-Feb-20
Module 5 Deep Learning Deep Feedforward 11-Feb-20
Module 5 Deep Learning Regularisation for Deep Nets 18-Feb-20
Module 5 Deep Learning Deep Learning Volatility & Advanced Optimisation Strategies 25-Feb-20

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:

  • Francesca Lazzeri: Machine Learning Scientist, Microsoft
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 6 Practical Applications Financial Time Series Data 3-Mar-20
Module 6 Practical Applications Time Series Analysis 10-Mar-20
Module 6 Practical Applications Natural Language Processing to Predict Bond Prices 17-Mar-20
Module 6 Practical Applications Risk Models for Quant Trading 24-Mar-20

Module 6 Assignments:

Please note that the Module 6 practical hands-on assignment will not be marked or count to 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.).”