Level 1: Machine Learning Institute Certificate in Finance (MLI)

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Dates:

• Level 1 Starts: Tuesday 17th May 2022
• Lectures Start at 18.00 UK Time

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ASSIGNMENTS:

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.

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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.

• Lecture week 1: Linear regression and regularization (17th May)
• Lecture week 2: Ensemble methods (24th May)
• Lecture week 3: Kernel methods (31th May)
• Lecture week 4: Introduction to Bayesian modelling (7th June)

End of Module 1 Assignment.

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Module 2 – 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.

• Lecture week 5: Machine Learning and the Perceptron (14th June)
• Lecture week 6: Backpropagation, Optimisation and Regularisation (21th June)
• Lecture week 7: Neural Networks in Banking and Finance (28th June)
• Lecture week 8: Deep learning volatility (5th July)

End of Module 2 Assignment.

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Module 3 – Unsupervised Learning and Alternative Data

• Lecture week 9: Introduction and dimensionality reduction (12th July)
• Lecture week 10: Clustering algorithms (19th July)
• Lecture week 11: PCA and autoencoders (26th July)
• Lecture week 12: Alternative data (2nd August)

End of Module 3 Assignment.

Summer Break

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Module 4 – Practitioner’s Approach to Machine Learning:

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.

• Lecture week 13: Hardware of machine learning (30th August)
• Lecture week 14: Reproducibility and Deployment of Data Science Workflows (6th September)
• Lecture week 15: Feature engineering and model tuning (13th September)
• Lecture week 16: Differential machine learning (20th September)

End of Module 4 Assignment.

End of MLI Level 1

Level 2: Machine Learning Institute Certificate in Finance (MLI)

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Dates:

• Level 2 Starts: Tuesday 6th September 2022
• Lectures Start at 18.00 UK Time

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Module 5 – Reinforcement Learning

• Lecture week 17: Reinforcement Learning: introduction (6th September)
• Lecture week 18: Reinforcement Learning: implementation (13th September)
• Lecture week 19: Reinforcement learning for market making and wealth management (20th September)
• Lecture week 20: Reinforcement learning for optimal order execution (27th September)

End of Module 5 Assignment.

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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.

• Lecture week 21: Financial time series data (4th October)
• Lecture week 22: Time series analysis (11th October)
• Lecture week 23: Practical lab session (18th October)
• Lecture week 24: Introduction to time series signatures (25th October)

End of Module 6 Quizzes.

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Module 7 – Natural Language Processing and Generative Deep Learning 

• Lecture week 25: Deep learning for text (1st November)
• Lecture week 26: Attention, transformers, and BERT (8th November)
• Lecture week 27: Generative modelling, variational autoencoders, and GANs (15th November)
• Lecture week 28: A data-driven market simulator for small data environments (22nd November)

End of Module 7 Assignment.

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Module 8 – Quantum Machine Learning     

• Lecture week 29: Introduction to quantum computing (29th November)
• Lecture week 30: Variational circuits as machine learning methods (6th December)
• Lecture week 31: Quantum models as kernel methods (13th December)
• Lecture week 32: Potential quantum advantages (20th December)

End of Module 8 Assignment.

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 3rd January 2023

Examination Date: Tuesday 17th January 2023

• Candidates will sit a formal examination on a computer. The exam is taken online by students globally.

Final Project:

Hand in Date: Friday 27th January 2023

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.