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

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Dates:

• Level 1 Starts: Tuesday 18th April 2023
• 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 (18th Apr)
• Lecture week 2: Ensemble methods (25th Apr)
• Lecture week 3: Kernel methods (2nd May)
• Lecture week 4: Introduction to Bayesian modelling (9th May)

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 (16th May)
• Lecture week 6: Backpropagation, Optimisation and Regularisation (23rd May)
• Lecture week 7: Neural Networks in Banking and Finance (30th May)
• Lecture week 8: Deep learning volatility (6th Jun)

End of Module 2 Online Test

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Module 3 – Unsupervised Learning and Alternative Data

• Lecture week 9: Introduction and dimensionality reduction (13th Jun)
• Lecture week 10: Clustering algorithms (20th Jun)
• Lecture week 11: PCA and autoencoders (27th Jun)
• Lecture week 12: Alternative data (4th Jul)

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 (11th Jul)
• Lecture week 14: Reproducibility and Deployment of Data Science Workflows (18th Jul)
• Lecture week 15: Feature engineering and model tuning (25th Jul)
• Lecture week 16: Differential machine learning (1st Aug)

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 8th August 2023
• Lectures Start at 18.00 UK Time

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Module 5 – Reinforcement Learning

• Lecture week 17: Reinforcement Learning: introduction (8th Aug)
• Lecture week 18: Reinforcement Learning: implementation (15th Aug)
• Lecture week 19: Reinforcement learning for market making and wealth management (22th Aug)
• Lecture week 20: Reinforcement learning for optimal order execution (29th Aug)

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 (5th Sept)
• Lecture week 22: Time series analysis (12th Sept)
• Lecture week 23: Practical lab session (19th Sept)
• Lecture week 24: Introduction to time series signatures (26th Sept)

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 (3rd Oct)
• Lecture week 26: Attention, transformers, and BERT (10th Oct)
• Lecture week 27: Generative modelling, variational autoencoders, and GANs (17th Oct)
• Lecture week 28: A data-driven market simulator for small data environments (24th Oct)

End of Module 7 Online Test

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Module 8 – Quantum Machine Learning     

• Lecture week 29: Introduction to quantum computing (31st Oct)
• Lecture week 30: Variational circuits as machine learning methods (7th Nov)
• Lecture week 31: Quantum models as kernel methods (14th Nov)
• Lecture week 32: Potential quantum advantages (21st Nov)

End of Module 8 Online Test

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 28th November 2023

Examination Date: Tuesday 12th December 2023

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

Final Project:

Hand in Date: Friday 5th January 2024

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.