Machine Learning Institute CertificateStarts Tuesday 18th April 2023

Enhanced MLI syllabus

  • The MLI is now comprised of 2 levels
  • 5 Primers
  • 8 modules over 32 lecture weeks
  • End of Module Online Tests
  • 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 4th April 2023: 09.00 – 17.00

Overview

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

Paul Bilokon:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

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 6th April 2023: 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

Paul Bilokon:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

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.

Algorithmic Trading

Thursday 6th April 2023: 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:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

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 11th April 2023: 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:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

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 13th April 2023: 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:

CEO, Thalesians, Visiting Professor, Imperial College

Paul Bilokon: CEO, Thalesians, Visiting Professor, Imperial College

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.

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


Dates:

  • Level 1 Starts: Tuesday 18th April 2023
  • 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 (18th Apr)
  • Lecture week 2: Ensemble methods (25th Apr)
  • Lecture week 3: Kernel methods (2nd May)
  • Lecture week 4: Introduction to Bayesian modelling (9th May)

End of Module 1 Online Test


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 (16th May)
  • Lecture week 6: Backpropagation, Optimisation and Regularisation (23rd May)
  • Lecture week 7: Neural Networks in Banking and Finance (30th May)
  • Lecture week 8: Deep learning volatility (6th Jun)

End of Module 2 Online Test


Module 3 – Unsupervised Learning and Alternative Data

  • Lecture week 9: Introduction and dimensionality reduction (13th Jun)
  • Lecture week 10: Clustering algorithms (20th Jun)
  • Lecture week 11: PCA and autoencoders (27th Jun)
  • Lecture week 12: Alternative data (4th Jul)

End of Module 3 Online Test


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 (11th Jul)
  • Lecture week 14: Reproducibility and Deployment of Data Science Workflows (18th Jul)
  • Lecture week 15: Feature engineering and model tuning (25th Jul)
  • Lecture week 16: Differential machine learning (1st Aug)

End of Module 4 Online Test.

End of MLI Level 1

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


Dates:

  • Level 2 Starts: Tuesday 8th August 2023
  • Lectures Start at 18.00 UK Time

Module 5 – Reinforcement Learning

  • Lecture week 17: Reinforcement Learning: introduction (8th Aug)
  • Lecture week 18: Reinforcement Learning: implementation (15th Aug)
  • Lecture week 19: Reinforcement learning for market making and wealth management (22th Aug)
  • Lecture week 20: Reinforcement learning for optimal order execution (29th Aug)

End of Module 5 Online Test


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 (5th Sept)
  • Lecture week 22: Time series analysis (12th Sept)
  • Lecture week 23: Practical lab session (19th Sept)
  • Lecture week 24: Introduction to time series signatures (26th Sept)

End of Module 6 Quizzes.


Module 7 – Natural Language Processing and Generative Deep Learning 

  • Lecture week 25: Deep learning for text (3rd Oct)
  • Lecture week 26: Attention, transformers, and BERT (10th Oct)
  • Lecture week 27: Generative modelling, variational autoencoders, and GANs (17th Oct)
  • Lecture week 28: A data-driven market simulator for small data environments (24th Oct)

End of Module 7 Online Test


Module 8 – Quantum Machine Learning     

  • Lecture week 29: Introduction to quantum computing (31st Oct)
  • Lecture week 30: Variational circuits as machine learning methods (7th Nov)
  • Lecture week 31: Quantum models as kernel methods (14th Nov)
  • Lecture week 32: Potential quantum advantages (21st Nov)

End of Module 8 Online Test

Examination & Final Project

Final Examination: 

Examination Preparation Session: Tuesday 28th November 2023

Examination Date: Tuesday 12th December 2023

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

Final Project:

Hand in Date: Friday 5th January 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.

  • Discount Structure
  • Early bird discount
    25% until 24th February 2023

  • Early bird discount
    15% until 31st March 2023

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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