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

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Dates:

• Level 1 Starts: Tuesday 1st November 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 (1st Nov)
• Lecture week 2: Ensemble methods (8th Nov)
• Lecture week 3: Kernel methods (15th Nov)
• Lecture week 4: Introduction to Bayesian modelling (22nd Nov)

End of Module 1 Online Test

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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 (29th Nov)
• Lecture week 6: Backpropagation, Optimisation and Regularisation (6th Dec)
• Lecture week 7: Neural Networks in Banking and Finance (13th Dec)
• Lecture week 8: Deep learning volatility (20th Dec)

End of Module 2 Online Test

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Module 3 – Unsupervised Learning and Alternative Data

• Lecture week 9: Introduction and dimensionality reduction (3rd Jan)
• Lecture week 10: Clustering algorithms (10th Jan)
• Lecture week 11: PCA and autoencoders (17th Jan)
• Lecture week 12: Alternative data (24th Jan)

End of Module 3 Online Test

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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 (31st Jan)
• Lecture week 14: Reproducibility and Deployment of Data Science Workflows (7th Feb)
• Lecture week 15: Feature engineering and model tuning (14th Feb)
• Lecture week 16: Differential machine learning (21th Feb)

End of Module 4 Online Test

End of MLI Level 1

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

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Dates:

• Level 2 Starts: Tuesday 28th February 2023
• Lectures Start at 18.00 UK Time

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Module 5 – Reinforcement Learning

• Lecture week 17: Reinforcement Learning: introduction (28th Feb)
• Lecture week 18: Reinforcement Learning: implementation (7th Mar)
• Lecture week 19: Reinforcement learning for market making and wealth management (14th Mar)
• Lecture week 20: Reinforcement learning for optimal order execution (21st Mar)

End of Module 5 Online Test

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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 (28th Mar)
• Lecture week 22: Time series analysis (4th Apr)
• Lecture week 23: Practical lab session (11th Apr)
• Lecture week 24: Introduction to time series signatures (18th Apr)

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 (25th Apr)
• Lecture week 26: Attention, transformers, and BERT (2nd May)
• Lecture week 27: Generative modelling, variational autoencoders, and GANs (9th May)
• Lecture week 28: A data-driven market simulator for small data environments (16th May)

End of Module 7 Online Test

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Module 8 – Quantum Machine Learning     

• Lecture week 29: Introduction to quantum computing (23rd May)
• Lecture week 30: Variational circuits as machine learning methods (30th May)
• Lecture week 31: Quantum models as kernel methods (6th June)
• Lecture week 32: Potential quantum advantages (13th June)

End of Module 8 Online Test

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 20th June 2023

Examination Date: Tuesday 4th July 2023

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

Final Project:

Hand in Date: Friday 28th July 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.