CDT Workshop ML 4 Time-series

The Oxford EPSRC CDT in Health Data Science

HDS-M05: Module - Machine Learning for Time Series
November 4 - 8, 2024

Lab Course Designers

CHI Lab,
The Institute of Biomedical Engineering,
Department of Engineering Science,
University of Oxford

Lab Overview

This repository provides a comprehensive introduction to the fundamentals of applying machine learning (ML) to medical time-series data. You’ll explore how ML techniques for time-series data differ from traditional image-based or static models and discover key pre-processing steps specific to time-series data. The module also covers strategies for framing problems and tasks within a temporal context.

Throughout this course, you will be introduced to essential time-series models, including the Autoregressive (AR) process, Recurrent Neural Networks (RNNs), and Transformers—fundamental tools for applying ML to healthcare data. In the latter part of the course, we address privacy considerations in healthcare, examining how techniques like federated learning and dataset condensation can support the democratization of healthcare data.

Lab Schedule: ML 4 Time-series

Lab 1: Essential Methodology - Pre-processing, basic predictive modelling & autogressive modelling (lab materials)

Lab 2: Recurrent Neural Networks (lab materials)

Lab 3.1: Graph Neural Networks (lab materials)
Lab 3.2: Wearable (lab materials

Lab 4: Multi-task learning and Meta-learning for time-series (lab materials)

Further lecture materials can be found on canvas.ox.ac.uk

Data Access

The accompanying pre-processed data for this module can be downloaded via canvas.ox.ac.uk

Setup instructions on the Virtual Machines

Load and initialize Anaconda. This needs to be done only once (you may not need to run this if you already see (bash) written in front of your prompt).
```
module load Anaconda3
conda init bash
```
Exit and re-login so that the above takes effect.
Create an anaconda environment from the provided requirements YAML file:
```
conda env create -f lab1Env2024.yml
```
You are now ready to use the environment:
```
conda activate lab1Env2024
```
In future logins, you only need to run this last command.

How to run Jupyter notebooks remotely

In your remote machine, launch a Jupyter notebook with a specified port, e.g. 9000:

jupyter-notebook --no-browser --port=9000

This will output something like:

To access the notebook, open this URL:
http://localhost:9000/?token=
b3ee74d492a6348430f3b74b52309060dcb754e7bf3d6ce4

On your local machine, perform port-forwarding, e.g. the following forwards the remote port 9000 to the local port 8888:
```
ssh -N -f -L localhost:8888:localhost:9000 username@remote_address
```
Note: You can use the same port numbers for both local and remote.
Finally, copy the URL from step 1. Then in your local machine, open Chrome and paste the URL, but change the port to the local port (or do nothing else if you used the same port). You should be able see the notebooks now.

AnshThakur/CDT-TimeSeries