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Python Primers: Python for Data Science and Artificial Intelligence

Python Primers: 

Python for Data Science and Artificial Intelligence

Date: Tuesday 9th April 2019

Live and Online: 09.00 – 17.00


Advanced Python Techniques 

Date: Tuesday 16th April 2019

Live and Online: 09.00 – 12.30


Python for Data Science and Artificial Intelligence

Tuesday 9th April 2019: 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

Advanced Python Techniques

Tuesday 16th April 2019: 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

Level 1: Machine Learning Institute Certificate in Finance 

Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Level 1 Starts: Tuesday 23rd April 2019

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

Introduction week: Tuesday 23rd April

Welcome to the MLI by the Head of Faculty:

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

Guest Lecturer introducing current market trends in Machine Learning.

  • To be confirmed

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 lab assignments:

Module 1 Supervised Learning Learning from Data 30-Apr-19
Module 1 Supervised Learning Linear Models 7-May-19
Module 1 Supervised Learning Kernel Models 14-May-19
Module 1 Supervised Learning Ensemble Learning 21-May-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, JPMorgan Chase & Co

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

Module 2 Unsupervised Learning Introduction 28-May-19
Module 2 Unsupervised Learning Dimensionality Reduction 4-Jun-19
Module 2 Unsupervised Learning Clustering Algorithms 11-Jun-19
Module 2 Unsupervised Learning Applications 18-Jun-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, JPMorgan Chase & Co
Module 3 Practitioner’s Approach Problem setup and data pipeline 25-Jun-19
Module 3 Practitioner’s Approach Feature Engineering 2-Jul-19
Module 3 Practitioner’s Approach Model tuning 9-Jul-19
Module 3 Practitioner’s Approach Dealing with time series data 16-Jul-19

 

End of Module 3 Assignment.

Level II: Machine Learning Institute Certificate in Finance

Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 23rd 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 2 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 23-Jul-19
Module 4 Neural Networks Backpropagation 30-Jul-19
Module 4 Neural Networks Regularisation and Optimisation 6-Aug-19
Module 4 Neural Networks Network Architectures 3-Sep-19

 

Module 4 Assignment over summer break.


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 10-Sep-19
Module 5 Deep Learning Deep Feedforward 17-Sep-19
Module 5 Deep Learning Regularisation for Deep Nets 24-Sep-19
Module 5 Deep Learning Deep Learning Volatility & Advanced Optimisation Strategies 1-Oct-19

 

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:

  • Nick Firoozye: Honorary Senior Lecturer – Computer Science, University College London
  • Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC
Module 6 Practical Applications Financial Time Series Data 8-Oct-19
Module 6 Practical Applications Overfitting, Data Snooping, and Rehash 15-Oct-19
Module 6 Practical Applications Natural Language Processing to Predict Bond Prices 22-Oct-19
Module 6 Practical Applications Risk Models for Quant Trading 29-Oct-19

 

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.).”


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.

Examination

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

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.

  • Discount Structure
  • Super early bird discount
    25% until February 22nd 2019

  • Early bird discount
    15% until March 29th 2019

Only two days left to claim this discount!

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