Machine Learning Institute CertificateStarts Tuesday 17th May 2022

Enhanced MLI syllabus

  • The MLI is now comprised of 2 levels
  • 5 Primers
  • 8 modules over 32 lecture weeks
  • Module assignments
  • Practical final project and a final exam which can be taken from any global location online using our live invigilation platform.

Mathematics for Machine Learning

Tuesday 3rd May 2022: 09.00 – 17.00

Overview

  • Calculus
  • Linear Algebra
  • Analytic Geometry
  • Matrix Decompositions
  • Vector Calculus
  • Probability
  • Probability Distributions
  • Continuous Optimization

Paul Bilokon:

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Financial Derivatives and Risk

Thursday 5th May 2022: 09.00 – 12.30

  • Risk
  • Pricing methodologies and arbitrage
  • Trees and option pricing
  • The Ito calculus
  • The Black-Scholes model
  • The practical pricing of a European option
  • Interest rate derivatives
  • Introduction to QuantLib

Claudio Albanese:

Founder, Global Valuation

Claudio Albanese: Founder, Global Valuation

Algorithmic Trading

Thursday 5th May 2022: 13.30 – 17.00

  • Trading fundamentals
  • Trading strategies and backtesting
  • OHLCV data and limit order books
  • The microstructure of financial markets
  • Dynamic portfolio management
  • Optimal execution
  • Cryptocurrency trading
  • The technology stack

Paul Bilokon:

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Python for Data Science and Artificial Intelligence

Tuesday 10th May 2022: 09.00 – 17.00

Overview

Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.

The MLI Python Primers are 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

Paul Bilokon:

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Advanced Python Techniques

Thursday 12th May 2022: 09.00 – 17.00

09.00 – 12.30: Advanced Python Features and Putting them to use in Practice

  • Algorithmics and graph theory
  • Prime numbers
  • Cryptography
  • Blockchain
  • Distributed Computing with Python

13.30 – 17.00: High Performance Python

 Outline syllabus 

 

·         Profiling 

·         The use of NumPy and SciPy over pure python 

·         The importance of optimised NumPy and SciPy 

·         The use of Cython and ctypes to integrate compiled code 

·         Just In Time Compilation using Numba  

·         Distributed computing frameworks  

·         Numerical precision and speed 

·         Using specialised instead of generalised algorithms 

·         NAG Library for Python  

 

Abstract 

Python is a superb prototyping language that allows us to develop high quality data analyses and simulations in a relatively short amount of time.  The cost we pay for this is performance.  Python is essentially an interpreted and single threaded language which puts severe limitations on its speed.  In this session we will learn a range of techniques that will allow us to discover which parts of our Python code are slow and what we can do to speed things up. 

Paul Bilokon:

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. Paul Bilokon is CEO and Founder of Thalesians Ltd and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Jonathan Boyle:

Jonathan Boyle: Software Engineer, NAG

Software Engineer, NAG

Jonathan is a scientist and research software engineer with over 15 years’ experience of high-performance computing. He has contributed to various software projects written in C, C++, Fortran and Python. Most recently, Jonathan has been working at NAG as a software engineer on the EU funded POP project. This work offers services to improve the performance of parallel software, written in a range of languages (including Python), designed to run on HPC hardware, including the world’s largest supercomputers.

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


Dates:

  • Level 1 Starts: Tuesday 17th May 2022
  • Lectures Start at 18.00 UK Time

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.


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 (17th May)
  • Lecture week 2: Ensemble methods (24th May)
  • Lecture week 3: Kernel methods (31th May)
  • Lecture week 4: Introduction to Bayesian modelling (7th June)

End of Module 1 Assignment.


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 (14th June)
  • Lecture week 6: Backpropagation, Optimisation and Regularisation (21th June)
  • Lecture week 7: Neural Networks in Banking and Finance (28th June)
  • Lecture week 8: Deep learning volatility (5th July)

End of Module 2 Assignment.


Module 3 – Unsupervised Learning and Alternative Data

  • Lecture week 9: Introduction and dimensionality reduction (12th July)
  • Lecture week 10: Clustering algorithms (19th July)
  • Lecture week 11: PCA and autoencoders (26th July)
  • Lecture week 12: Alternative data (2nd August)

End of Module 3 Assignment.

Summer Break


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 (9th August)
  • Lecture week 14: Reproducibility and Deployment of Data Science Workflows (16th August)
  • Lecture week 15: Feature engineering and model tuning (23th August)
  • Lecture week 16: Differential machine learning (30th August)

End of Module 4 Assignment.

End of MLI Level 1

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


Dates:

  • Level 2 Starts: Tuesday 6th September 2022
  • Lectures Start at 18.00 UK Time

Module 5 – Reinforcement Learning

  • Lecture week 17: Reinforcement Learning: introduction (6th September)
  • Lecture week 18: Reinforcement Learning: implementation (13th September)
  • Lecture week 19: Reinforcement learning for market making and wealth management (20th September)
  • Lecture week 20: Reinforcement learning for optimal order execution (27th September)

End of Module 5 Assignment.


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 (4th October)
  • Lecture week 22: Time series analysis (11th October)
  • Lecture week 23: Practical lab session (18th October)
  • Lecture week 24: Introduction to time series signatures (25th October)

End of Module 6 Quizzes.


Module 7 – Natural Language Processing and Generative Deep Learning 

  • Lecture week 25: Deep learning for text (1st November)
  • Lecture week 26: Attention, transformers, and BERT (8th November)
  • Lecture week 27: Generative modelling, variational autoencoders, and GANs (15th November)
  • Lecture week 28: A data-driven market simulator for small data environments (22nd November)

End of Module 7 Assignment.


Module 8 – Quantum Machine Learning     

  • Lecture week 29: Introduction to quantum computing (29th November)
  • Lecture week 30: Variational circuits as machine learning methods (6th December)
  • Lecture week 31: Quantum models as kernel methods (13th December)
  • Lecture week 32: Potential quantum advantages (20th December)

End of Module 8 Assignment.

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 3rd January 2023

Examination Date: Tuesday 17th January 2023

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

Final Project:

Hand in Date: Friday 27th January 2023

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.

Course Email Reminder

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Get in touch

Email: enquiries@mlinstitute.org

Tel: +44 (0) 1273 201 352

World Business Strategies Ltd Werks Central
15 -17 Middle Street
Brighton
BN1 1AL
United Kingdom