Welcome to you all !
This GitHub Home page contains all the materials for the course Machine Learning and Finance 2022 at Imperial College Business School.
Instructors
- Hachem Madmoun
- Arnaud de Servigny
Getting Started
- The scripts are written as Jupyter notebooks and run directly in Google Colab.
- If you prefer to run the scripts on your machine, please follow the instructions in the following link: Installation instruction
Prerequisites
- Basic knowledge in probability theory and calculus.
- Proficiency in some programming language, preferably Python.
Syllabus
| Date | Start | End | Lectures topics | Lectures | Quiz | Quiz Solution | Programming Session | Optional Reading |
|---|---|---|---|---|---|---|---|---|
| 04/12/22 04/12/22 |
09:00 14:00 |
12:00 17:00 |
Fundamentals of Machine Learning | Lecture_1 | Quiz1 link Quiz1 pdf |
Code1 |
Optional_reading | |
| 04/22/22 | 18:00 | 20:00 | Additional Python Session | Practical Implementation | No quiz | No quiz | Code_Python |
|
| 04/26/22 04/26/22 |
09:00 14:00 |
12:00 17:00 |
Supervised Learning Algorithms | Lecture_2 | Quiz2 link Quiz2 pdf |
Code2 |
||
| 05/03/22 05/03/22 |
09:00 14:00 |
12:00 17:00 |
Practical Implementation : Credit risk dataset | Practical Implementation | No quiz | No quiz | Code3 |
|
| 05/10/22 05/10/22 |
09:00 14:00 |
12:00 17:00 |
Introduction to Neural Networks. | Lecture_4 | Quiz4 link Quiz4 pdf |
Code4 |
Optional_reading | |
| 05/17/22 05/17/22 |
09:00 14:00 |
12:00 17:00 |
Introduction to Unsupervised Learning: Creating word vectors using the GloVe approach. | Practical Implementation | Quiz5 link Quiz5 pdf |
Code5 |
GloVe reference | |
| 05/24/22 05/24/22 |
09:00 14:00 |
12:00 17:00 |
Neural Networks for sequences. | Lecture_6 | Quiz6 link Quiz6 pdf |
Code6 |
||
| 05/31/22 05/31/22 |
09:00 14:00 |
12:00 17:00 |
Practical Implementation: Sentiment Analysis | Practical Implementation | Preprocessing Creating_training_Dataset Training_Process |
Preprocessing Creating_training_Dataset Training Process |
Code7 |
|
| 06/07/22 06/07/22 |
09:00 14:00 |
12:00 17:00 |
Attention mechanisms and Transformers | Lecture_8 | RNN_Applications_link Alignment_link Attention_Weights_link RNN_Applications_pdf Alignment_pdf Attention_Weights_pdf |
Finishing Programming Session 7 | Optional Reading pdf |
|
| 06/14/22 06/14/22 |
09:00 14:00 |
12:00 17:00 |
Dummy exam and revision elements | Review_link Review_pdf |
Introducing_the_problem_link Self_Attention_link Mock_Exam_link Introducing_the_problem_pdf Self_Attention_pdf Mock_Exam_pdf |
Finishing Programming Session 7 |
Module Outline Information
Module Description
The module is structured around 9 sessions of 3 hours each. The sessions are comprised of lectures and practical implementation sessions. Students will be expected to devote an equivalent amount of learning time outside of class, in private and group study of module material. Some of the teaching format will employ Python.
Module Aims & Objectives
The module will introduce the main subareas of Machine Learning in order to tackle various problem tasks. It is practicularly focused on a deeper understanding of sequence modeling using neural networks and attention mechanisms.
Learning Outcomes
The objectives of this module are:
- Develop knowledge on the roadmap for building machine learning systems.
- Get familiar with traditional Machine Learning algorithms and more advanced techniques including Deep Learning.
- Get a good understanding of the basic concepts of Supervised Learning, Unsupervised Learning and Sequence Models.
- Develop skills to process sequential data, especially in the context of Natural Language Processing.
- Practice supervised learning by predicting loan default risk.
- Practice sentiment analysis with state-of-the-art algorithms for sequential data.
Assessment
- Coursework : 50%
- Exam : 50%
Coursework breakdown
| Assignment | Quiz Coursework | Quiz Coursework Solutions | Slides | Type | Weighting | Date Released to students | Date Due |
|---|---|---|---|---|---|---|---|
| Coursework_2022 | Preprocessing Creating_Training_Dataset Training_Process |
Preprocessing Creating_Training_Dataset Training_Process |
Quizzes_Slides | Group project | 50 % | 05/24/22 | 06/06/2022 |
Past Courseworks and Exams
| Year | Coursework | Exam |
|---|---|---|
| 2022 | Coursework_2022 Solution_2022 |
Exam_2022 |
| 2021 | Coursework_2021 Solution_2021 |
Exam_2021 Solution_2021 |
| 2020 | Coursework_2020 Solution_2020 |
Exam_2020 Solution_2020 |
Contact
Please feel free to contact us if you have any questions or require further information at: h.madmoun@imperial.ac.uk