Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 28th July 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 28-Jul-20
Module 4 Neural Networks Backpropagation 04-Aug-20
Module 4 Neural Networks Regularisation and Optimisation 01-Sep-20
Module 4 Neural Networks Network Architectures 8-Sep-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:

  • Blanka Horvath: Assistant Professor, Financial Mathematics King’s College London
  • Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 5 Deep Learning Motivation and Examples 15-Sep-20
Module 5 Deep Learning Reinforcement Learning: introduction 22-Sep-20
Module 5 Deep Learning Reinforcement Learning: implementation 29-Sep-20
Module 5 Deep Learning Deep Learning Volatility / Practical Lab Session 06-Oct-20

End of Module 5 Assignment.


Module 6 – Time Series:

Time series data is an invaluable source of information used for future strategy and planning operations everywhere from finance to education and healthcare. You will be walked through the core steps of building, training, and deploying your time series forecasting models. You’ll build a theoretical foundation as you cover the essential aspects of time series representations, modeling, and forecasting before diving into the classical methods for forecasting time series data.

Module 6 Faculty:

  • Francesca Lazzeri: Machine Learning Scientist, Microsoft
Module 6 Time Series Financial Time Series Data 13-Oct-20
Module 6 Time Series Time Series Analysis 20-Oct-20
Module 6 Time Series Practical Lab Session 27-Oct-20

End of Module 6 Quizzes.