Enhanced MLI syllabus
- The MLI is now comprised of 2 levels
- Self-paced Primers & Introduction Suite on Registration
- 8 modules over 36 lectures
- End of Module Online Tests (Students get two attempts at each module test)
- Practical final project and a final exam which can be taken from any global location online using our live invigilation platform.
Self-paced Primers & Introduction Suite
On registration students have access to the MLI Primers & Introduction Suite. This is a self-paced portal on the mathematics for machine learning and python techniques, The MLI faculty will conduct two, 2 hour Q&A sessions before the MLI module 1 starts.
Self-paced: Mathematics for Machine Learning
Q&A Session: Tuesday 7th May 2024: 18.00 – 20.00
Overview
- Calculus
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability
- Probability Distributions
- Continuous Optimization
- Risk and Pricing
Self-paced: Python for Data Science and Artificial Intelligence
Q&A Session: Thursday 9th May 2024: 18.00 – 20.00
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
Self-paced: Advanced Python Techniques
Q&A Session: Thursday 9th May 2024: 18.00 – 20.00
Advanced Python Features and Putting them to use in Practice
- Algorithmics and graph theory
- Prime numbers
- Cryptography
- Blockchain
- Distributed Computing with Python
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.
Level 1: Machine Learning Institute Certificate in Finance (MLI)
Dates:
- Level 1 Starts: Tuesday 14th May 2024
- Lectures Start at 18.00 UK Time
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 1: Statistical estimation theory and introduction to machine learning
- Lecture 2: Linear regression
- Lecture 3: Regularized linear regression and ensemble methods
- Lecture 4: Introduction to Bayesian modelling
Mock Module Test:
Module 1 deals with the fundamentals of machine learning. Practical exercises, examples and projects will follow in later modules. Module 1 will include a mock test, to assist students with the end of module online tests.
End of Module 1 Online Test
Module 2 – Deep Learning:
- Lecture 5: Machine learning: Origins and Challenges
- Lecture 6: Neural Networks
- Lecture 7: Building Neural Networks
- Lecture 8: Deep learning, machine intelligence and consciousness
- Lecture 9: Deep learning volatility
Module 2 Optional Practical Mini Projects:
- Simulate a “Conscious” Computer
The main intent is to see if students can simulate a conscious computer and have it respond to a few basic questions. More complicated functions are welcome.
- Predict Corporate Bond Spread Changes
The purpose of this project is to provide students with intuition regarding linear regression, non-linear regression and neural networks.
End of Module 2 Online Test
Module 3 – Unsupervised Learning and Alternative Data
- Lecture 10: Introduction and dimensionality reduction
- Lecture 11: Clustering algorithms
- Lecture 12: PCA and autoencoders
- Lecture 13: Alternative data
End of Module 3 Online Test
Module 4 – Practitioner’s Approach to Machine Learning:
- Lecture 14:Explainable AI and accelerated computing in portfolio construction
- Lecture 15: Reproducibility and Deployment of Data Science Workflows
- Lecture 16: Feature engineering and model tuning
- Lecture 17: Differential machine learning
End of Module 4 Online Test.
End of MLI Level 1
Level 1 Faculty Q&A Sessions: 18.00 UK Time
Practical Examples:
Throughout the MLI, students will receive practical examples such as post-class homework, exercises, and mini-projects to enhance their understanding of the module lectures. These exercises are not mandatory and will not receive a grade. Students are encouraged to share their approaches and ideas on the forum, recommended solutions will be provided by the faculty for comparison.
Module Tests:
Students are required to take an on-line multiple choice test at the end of each module, counting towards the final grade. Students get two attempts at each module test.
The second module test attempt can be taken at any point before the final examination, module tests do not need to be taken in any particular order.
After completion of Module 8 students sit the unseen written examination virtually at their own desktop. The exam is taken on the same day worldwide. The MLI is a career-enhancing professional qualification, that can be taken worldwide.
Level 2: Machine Learning Institute Certificate in Finance (MLI)
Dates:
- Level 2 Starts: Tuesday 13th August 2024
- Lectures Start at 18.00 UK Time
Module 5 – Reinforcement Learning
- Lecture 18: Reinforcement Learning: introduction
- Lecture 19: Reinforcement Learning: implementation
- Lecture 20: Reinforcement learning for market making and wealth management
- Lecture 21: Reinforcement learning for optimal order execution
End of Module 5 Online Test
Module 6 – Time Series:
- Lecture 22: Financial time series data
- Lecture 23: Time series analysis
- Lecture 24: Practical lab session
- Lecture 25: Introduction to time series signatures
End of Module 6 Quizzes.
Module 7 – NLP, Generative AI & Large Language Models
- Lecture 26: Training LLMs for Quant Finance
- Lecture 27: Generative Modeling with Normalizing Flows: Foundations and Applications
- Lecture 28: Deep learning for text
- Lecture 29: Foundation NLP Models for ESG data extraction
- Lecture 30: Attention, transformers, and BERT
- Lecture 31: Generative modelling, variational autoencoders, and GANs
- Lecture 32: A data-driven market simulator for small data environments
End of Module 7 Online Test
Module 8 – Quantum Machine Learning
- Lecture 33: Introduction to quantum computing
- Lecture 34: Variational circuits as machine learning methods
- Lecture 35: Quantum models as kernel methods
- Lecture 36: Potential quantum advantages
End of Module 8 Online Test
Level 2 Faculty Q&A Sessions: 18.00 UK Time
Practical Examples:
Throughout the MLI, students will receive practical examples such as post-class homework, exercises, and mini-projects to enhance their understanding of the module lectures. These exercises are not mandatory and will not receive a grade. Students are encouraged to share their approaches and ideas on the forum, recommended solutions will be provided by the faculty for comparison.
Module Tests:
Students are required to take an on-line multiple choice test at the end of each module, counting towards the final grade. Students get two attempts at each module test.
The second module test attempt can be taken at any point before the final examination, module tests do not need to be taken in any particular order.
After completion of Module 8 students sit the unseen written examination virtually at their own desktop. The exam is taken on the same day worldwide. The MLI is a career-enhancing professional qualification, that can be taken worldwide.
Examination & Final Project
Final Examination:
Examination Preparation Session: Tuesday 12th November 2024
Examination Date: Tuesday 26th November 2024
- Candidates will sit a formal examination on a computer. The exam is taken online by students globally.
Final Project:
Hand in Date: Friday 13th December 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.
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