Machine Learning for Neuroscience

Department of Brain Sciences
Imperial College London &
Care Research and Technology Centre
The UK Dementia Research Institute

Contributors:

Francesca Palermo	Nan Fletcher-Lloyd	Alexander Capstick	Yu Chen

Virtual environment settings:

YML file
(for more informaiton and for creating an environment from an environment.yml file, see this link)

Lab experiments:

To run the lab experiments, you can use Anaconda, Visual Studio Code, or upload the Jupyuter Notebooks directly to Google CoLab. For more information on how to use Google Colab with GitHub, see this link.

Mathematical symbols and notations:

Notations

Basics of Matrix Algebra

Slides and notes:

Updated after the lectures: Annoated slides

Tutorials:

   Python for Beginners     

    Machine Learning for Beginners (introduction to scikit-learn)     

   Summary

1. Introduction to Machine Learning
   Notes
   Slides
   Lab Notebook     
   Lab Questions
   

2. Regression Models and Linear Prediction
   Notes
   Slides
   Lab Notebook    
   Lab Notebook (run)     
   

3. Probability and Information Theory
   Notes
   Slides
   Notebook: PDF and CDF Example     
   

4. Bayesian Models
   Notes
   Slides
   Lab Notebook     
   Lab Notebook (run)    
   

5. Support Vector Machines and Ensemble Models
   Notes
   Slides
   Lab Notebook    
   Lab Notebook (run)    
   

6. Neural Networks
   Notes
   Slides
   Notebook: Sigmoid Function    
   Lab Notebook and introduction to Pytorch  
   Lab Notebook and introduction to Pytorch (run)  
   

7. Convolutional Neural Networks
   Notes
   Slides
   CNN Example for Edge Detection    
   Lab Notebook (working with CIFAR10 dataset)     
   Lab Notebook (working with Alzheimer MRI Preprocessed Dataset)
   Alzheimer's Disease- Preprocessed MRI Dataset
   

8. Applications in neuroscience and neuroscience inspired models
   Notes
   Slides
   

9. Seminar - Ethical considerations and responsible machine learning
   Summary
   Notes and slides
   

10. Final Project
    Notes
    

11. Summary and overview
    Slides

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Optional lectures (May 2023)

This optional series focus on generative AI models and cover a range of recent models in this domain, including Transformers -with a brief overview of Large Language Models (LLMs), Generative Pre-trained Transformer (GPT)-, Variational Autoencoders (VAE) and Diffusion models.

Acknowledgement: The content for the slides is adapted from Understanding Deep Learning, Simon J.D. Prince, https://udlbook.github.io/udlbook/

I. Transformers
Slides
Annotated Slides
Notebook (Sample Transformer)  
Notebook (OpenAI GPT sample)  
Video recording

II. Variational Autoencoders
Slides
Notebook  
Video recording

III. Diffusion models
Slides
Notebook  
Video recording
Video on YouTube
Hugging Face- Train a diffusion model

Licensing

The contents of this repository are shared under under a Creative Commons Attribution 4.0 International License.

Software elements are additionally licensed under the BSD (3-Clause) License.