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.

The module includes a combination of theoretical and hands-on lab assignments.


MODULE 2 – UNSUPERVISED LEARNING

An important and challenging type of machine learning problems in finance is learning in the absence of ‘supervision’, or without labelled examples.

In this module, we first introduce the theoretical framework of hidden variable models. This family of models is then used to explore the two important areas of dimensionality reduction and
clustering algorithms.

There are theoretical and applied lab assignments with financial data sets.


MODULE 3 – PRACTITIONERS APPROACH TO ML

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.


LAB 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. The following are examples of the type of topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

 

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 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 6 – ADVANCED TOPICS

In this module, candidates will be exposed to a selection of some of the latest machine learning and AI topics relevant to the financial services industry.

Financial timeseries data presents particular challenges when it comes to applying machine learning techniques. These challenges and approaches to deal with them will be covered.

Also, building on the previous module, deep models for timeseries based on the RNN architecture and Long Short-Term Memory will be presented.

Since the lectures are delivered by industry practitioners from leading institutions, the candidates will be encouraged to use the solid technical foundations built throughout the programme to interact and confidently debate about the problems and approaches presented.


LAB 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. The following are examples of the topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention



FINAL EXAMINATION

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students. 



FINAL PROJECT SUBMISSION

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.

 

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.

The module includes a combination of theoretical and hands-on lab assignments.


MODULE 2 – UNSUPERVISED LEARNING

An important and challenging type of machine learning problems in finance is learning in the absence of ‘supervision’, or without labelled examples.

In this module, we first introduce the theoretical framework of hidden variable models. This family of models is then used to explore the two important areas of dimensionality reduction and
clustering algorithms.

There are theoretical and applied lab assignments with financial data sets.


MODULE 3 – PRACTITIONERS APPROACH TO ML

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.


LAB 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. The following are examples of the type of topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

 

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 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 6 – ADVANCED TOPICS

In this module, candidates will be exposed to a selection of some of the latest machine learning and AI topics relevant to the financial services industry.

Financial timeseries data presents particular challenges when it comes to applying machine learning techniques. These challenges and approaches to deal with them will be covered.

Also, building on the previous module, deep models for timeseries based on the RNN architecture and Long Short-Term Memory will be presented.

Since the lectures are delivered by industry practitioners from leading institutions, the candidates will be encouraged to use the solid technical foundations built throughout the programme to interact and confidently debate about the problems and approaches presented.


LAB 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. The following are examples of the topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention



FINAL EXAMINATION

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students. 



FINAL PROJECT SUBMISSION

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.

 

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.

The module includes a combination of theoretical and hands-on lab assignments.


Module 2 – Unsupervised Learning

An important and challenging type of machine learning problems in finance is learning in the absence of ‘supervision’, or without labelled examples.

In this module, we first introduce the theoretical framework of hidden variable models. This family of models is then used to explore the two important areas of dimensionality reduction and
clustering algorithms.

There are theoretical and applied lab assignments with financial data sets.


Module 3 – Practitioners Approach to ML

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.


LAB 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. The following are examples of the type of topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

 

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 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 6 – Advanced Topics

In this module, candidates will be exposed to a selection of some of the latest machine learning and AI topics relevant to the financial services industry.

Financial timeseries data presents particular challenges when it comes to applying machine learning techniques. These challenges and approaches to deal with them will be covered.

Also, building on the previous module, deep models for timeseries based on the RNN architecture and Long Short-Term Memory will be presented.

Since the lectures are delivered by industry practitioners from leading institutions, the candidates will be encouraged to use the solid technical foundations built throughout the programme to interact and confidently debate about the problems and approaches presented.


LAB 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. The following are examples of the topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention



FINAL EXAMINATION

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students. 



FINAL PROJECT SUBMISSION

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.

  

Exclusive content from the Machine Learning Python Primer.