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

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Dates:

• Level 1 Starts: Tuesday 10th October 2023
• Lectures Start at 18.00 UK Time

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Module Tests:

Students are required to take an on-line multiple choice test at the end of each module, counting towards the final grade. After completion of Module 8 students sit the unseen written examination, held in the examination suite for London-based students. All other students sit the exam virtually at their own desktop. The exam is taken on the same day worldwide.

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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.

Module 1 deals with the fundamentals of machine learning. Practical exercises, examples and projects will follow in later modules. Module 1 will include a mock test, to assist students with the end of module online tests.

• Lecture 1: Introduction to The MLI / Linear regression and regularization
• Lecture 2: Ensemble methods
• Lecture 3: Kernel methods
• Lecture 4: Introduction to Bayesian modelling

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 5: Machine Learning and the Perceptron
• Lecture 6: Backpropagation, Optimisation and Regularisation
• Lecture 7: Neural Networks in Banking and Finance
• Lecture 8: Predict Corporate Bond Spread Changes
• Lecture 9: Deep learning volatility

End of Module 2 Online Test

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Module 3 – Unsupervised Learning and Alternative Data

• Lecture 10: Introduction and dimensionality reduction
• Lecture 11:  Clustering algorithms
• Lecture 12: PCA and autoencoders
• Lecture 13: Alternative data

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 14: Hardware of machine learning
• Lecture 15: Reproducibility and Deployment of Data Science Workflows
• Lecture 16: Feature engineering and model tuning
• Lecture 17: Differential machine learning

End of Module 4 Online Test.

End of MLI Level 1

Level 1 Faculty Q&A Sessions: 18.00 GMT & 18.00 EST

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

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Dates:

• Level 2 Starts: Tuesday 6th February 2024
• Lectures Start at 18.00 UK Time

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Module 5 – Reinforcement Learning

• Lecture 18: Reinforcement Learning: introduction
• Lecture 19: Reinforcement Learning: implementation
• Lecture 20: Reinforcement learning for market making and wealth management
• Lecture 21: Reinforcement learning for optimal order execution

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 22: Financial time series data
• Lecture 23: Time series analysis
• Lecture 24: Practical lab session
• Lecture 25: Introduction to time series signatures

End of Module 6 Quizzes.

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Module 7 – NLP, Generative AI & Large Language Models

• Lecture 26: Training LLMs for Quant Finance
• Lecture 27:  GPT-3: Key Concepts & Use Cases
• Lecture 28: Deep learning for text
• Lecture 29: Foundation NLP Models for ESG data extraction
• Lecture 30: Attention, transformers, and BERT
• Lecture 31: Generative modelling, variational autoencoders, and GANs
• Lecture 32: A data-driven market simulator for small data environments

End of Module 7 Online Test

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Module 8 – Quantum Machine Learning     

• Lecture 33: Introduction to quantum computing
• Lecture 34: Variational circuits as machine learning methods
• Lecture 35: Quantum models as kernel methods
• Lecture 36: Potential quantum advantages

End of Module 8 Online Test

Level 2 Faculty Q&A Sessions: 18.00 GMT & 18.00 EST

Lifelong Learning: Half day workshop: Large Language Models in Financial Markets

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 30th April 2024

Examination Date: Tuesday 14th May 2024

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

Final Project:

Hand in Date: Friday 28th June 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.