Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.
Your course is designed to take you from the very foundations to state-of-the-art use of modern Python libraries.
You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn to use distributed task queues (Celery), learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries (Scikit-Learn, Keras, TensorFlow, and Theano), and complete a realistic, real-life data science lab.
Syllabus:
- The fundamentals of the Python programming language and Jupyter notebooks
- Jupyter notebooks
- The Python syntax
- Data types, duck typing
- Data structures: lists, sets, and dictionaries
- Data types
- Advanced Python features; distributed tasks queues with Celery
- List comprehensions
- Lambdas
- Objects
- The Global Interpreter Lock (GIL)
- Multithreading and multiprocessing
- Distributed task queues with Celery
- Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
- Multidimensional arrays in NumPy
- Linear algebra and optimisation with SciPy
- Data visualisation in Matplotlib
- Time series data
- Dealing with Pandas DataFrames
- Machine Learning with Scikit-Learn; Deep Learning with Keras, TensorFlow, and Theano
- Overview of machine learning
- Introduction to Scikit-Learn
- Keras and TensorFlow
- Introduction to Theano
Level 1: Machine Learning Institute Certificate in Finance
Dates:
- Level 1 Starts: Tuesday 23rd April 2019
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 1 |
Supervised Learning |
Learning from Data |
23-Apr-19 |
| Module 1 |
Supervised Learning |
Linear Models |
30-Apr-19 |
| Module 1 |
Supervised Learning |
Kernel Models |
7-May-19 |
| Module 1 |
Supervised Learning |
Ensemble Learning |
14-May-19 |
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 2 |
Unsupervised Learning |
Introduction |
21-May-19 |
| Module 2 |
Unsupervised Learning |
Dimensionality Reduction |
28-May-19 |
| Module 2 |
Unsupervised Learning |
Clustering Algorithms |
4-Jun-19 |
| Module 2 |
Unsupervised Learning |
Applications |
11-Jun-19 |
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 |
Practitioner’s Approach |
Problem Setup and Data Pipeline |
18-Jun-19 |
| Module 3 |
Practitioner’s Approach |
Feature Engineering |
25-Jun-19 |
| Module 3 |
Practitioner’s Approach |
Exploration, Maximum Value Hypothesis |
2-Jul-19 |
| Module 3 |
Practitioner’s Approach |
Model Tuning |
9-Jul-19 |
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
Level 2: Machine Learning Institute Certificate in Finance
Dates:
- Level 2 Starts: Tuesday 16th July 2019
- Examination: Tuesday 26th November 2019
- Final Project Hand in Friday 3rd 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 |
Neural Networks |
Perceptron Model |
16-Jul-19 |
| Module 4 |
Neural Networks |
Backpropagation |
23-Jul-19 |
| Module 4 |
Neural Networks |
Regularisation and Optimisation |
30-Jul-19 |
| Module 4 |
Neural Networks |
Network Architectures |
6-Aug-19 |
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 |
Deep Learning |
Motivation and Examples |
3-Sep-19 |
| Module 5 |
Deep Learning |
Deep Feedforward |
10-Sep-19 |
| Module 5 |
Deep Learning |
Regularisation for Deep Nets |
17-Sep-19 |
| Module 5 |
Deep Learning |
Advanced Optimisation Strategies |
24-Sep-19 |
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.
| Module 6 |
Advanced Topics |
Advanced Topic 1 |
1-Oct-19 |
| Module 6 |
Advanced Topics |
Advanced Topic 2 |
8-Oct-19 |
| Module 6 |
Advanced Topics |
Advanced Topic 3 |
15-Oct-19 |
| Module 6 |
Advanced Topics |
Advanced Topic 4 |
22-Oct-19 |
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:
DATE: Tuesday 26th November 2019
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:
DATE: Friday 3rd January 2020
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.
