Level 1: Machine Learning Institute Certificate in Finance
Dates:
- Level 1 Starts: Tuesday 1st October 2019
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.
Introduction: Tuesday 1st October
Welcome to the MLI Faculty:
- Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
- Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
- Adriano Soares Koshiyama: The Alan Turing Institute
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 Faculty:
- Adriano Soares Koshiyama: The Alan Turing Institute
The module includes a combination of theoretical and hands-on assignments:
| Module 1 |
Supervised Learning |
Learning from Data |
1-Oct-19 |
| Module 1 |
Supervised Learning |
Linear Models |
8-Oct-19 |
| Module 1 |
Supervised Learning |
Kernel Models |
15-Oct-19 |
| Module 1 |
Supervised Learning |
Ensemble Learning |
22-Oct-19 |
Module 1 includes weekly 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.
Module 2 Faculty:
- Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
- Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
There are theoretical and applied assignments with financial data sets.
| Module 2 |
Unsupervised Learning |
Introduction |
29-Oct-19 |
| Module 2 |
Unsupervised Learning |
Dimensionality Reduction |
5-Nov-19 |
| Module 2 |
Unsupervised Learning |
Clustering Algorithms |
12-Nov-19 |
| Module 2 |
Unsupervised Learning |
Applications |
19-Nov-19 |
End of Module 2 Assignment.
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.
Module 3 Faculty:
- Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas
- Ivan Zhdankin: Associate, Quantitative Analyst, JP Morgan Chase & Co
| Module 3 |
Practitioner’s Approach |
Problem setup and data pipeline |
26-Nov-19 |
| Module 3 |
Practitioner’s Approach |
Feature Engineering |
3-Dec-19 |
| Module 3 |
Practitioner’s Approach |
Model tuning |
10-Dec-19 |
| Module 3 |
Practitioner’s Approach |
Dealing with time series data |
17-Dec-19 |
End of Module 3 Assignment.
Level 2: Machine Learning Institute Certificate in Finance
Dates:
- Level 2 Starts: Tuesday 7th January 2020
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 4 Faculty:
- Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
- Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
| Module 4 |
Neural Networks |
Perceptron Model |
7-Jan-20 |
| Module 4 |
Neural Networks |
Backpropagation |
14-Jan-20 |
| Module 4 |
Neural Networks |
Regularisation and Optimisation |
21-Jan-20 |
| Module 4 |
Neural Networks |
Network Architectures |
28-Jan-20 |
End of Module 4 Assignment.
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 5 Faculty:
- Harsh Prasad: Vice President, Morgan Stanley
- Blanka Horvath: Assistant Professor, Financial Mathematics King’s College London
- Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
| Module 5 |
Deep Learning |
Motivation and Examples |
4-Feb-20 |
| Module 5 |
Deep Learning |
Deep Feedforward |
11-Feb-20 |
| Module 5 |
Deep Learning |
Regularisation for Deep Nets |
18-Feb-20 |
| Module 5 |
Deep Learning |
Deep Learning Volatility & Advanced Optimisation Strategies |
25-Feb-20 |
End of Module 5 Assignment.
Module 6 – Practical Applications:
In this module, candidates will be exposed to a selection of some of the latest practical machine learning and AI applications relevant to the financial services industry. Financial time series data presents particular challenges when it comes to applying machine learning techniques. The challenges and approaches to deal with them will be covered in this module.
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 apply and debate the problems and approaches presented.
Module 6 Faculty:
- Francesca Lazzeri: Machine Learning Scientist, Microsoft
- Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
| Module 6 |
Practical Applications |
Financial Time Series Data |
3-Mar-20 |
| Module 6 |
Practical Applications |
Time Series Analysis |
10-Mar-20 |
| Module 6 |
Practical Applications |
Natural Language Processing to Predict Bond Prices |
17-Mar-20 |
| Module 6 |
Practical Applications |
Risk Models for Quant Trading |
24-Mar-20 |
Module 6 Assignments:
Please note that the Module 6 practical hands-on assignment will not be marked or count to the final MLI assessment.
Risk Models for Quant Trading Assignment: “The assignment will amount to running a horserace backtest comparing various risk model constructions discussed in the lecture by using them to optimize quant trading alphas of the student’s choice. To facilitate the completion of the assignment, it will provide links to the source code for the risk model constructions as well as backtesting, which the student can adapt and tweak (in the computer language of his or her choosing) for the purpose of completing the assignment. The student will report and debate the results on the forum of the horserace backtest (return-on-capital, Sharpe ratio, cents-per-share, etc.) along with the pertinent information (backtesting period used, data source, description of the alphas, etc.).”
