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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    Open In Colab   Open In kaggle

    Machine Learning for Beginners (introduction to scikit-learn)    Open In Colab   Open In kaggle

   Summary

  1. Introduction to Machine Learning
    Notes
    Slides
    Lab Notebook    Open In Colab   Open In kaggle
    Lab Questions

  2. Regression Models and Linear Prediction
    Notes
    Slides
    Lab Notebook   Open In Colab   Open In kaggle
    Lab Notebook (run)    Open In Colab   Open In kaggle

  3. Probability and Information Theory
    Notes
    Slides
    Notebook: PDF and CDF Example    Open In Colab   Open In kaggle

  4. Bayesian Models
    Notes
    Slides
    Lab Notebook    Open In Colab   Open In kaggle
    Lab Notebook (run)   Open In Colab   Open In kaggle

  5. Support Vector Machines and Ensemble Models
    Notes
    Slides
    Lab Notebook   Open In Colab   Open In kaggle
    Lab Notebook (run)   Open In Colab   Open In kaggle

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

  7. Convolutional Neural Networks
    Notes
    Slides
    CNN Example for Edge Detection   Open In Colab   Open In kaggle
    Lab Notebook (working with CIFAR10 dataset)    Open In Colab   Open In kaggle
    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


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)   Open In Colab
Notebook (OpenAI GPT sample)   Open In Colab
Video recording

II. Variational Autoencoders
Slides
Notebook   Open In Colab
Video recording

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

Licensing

CC BY 4.0

CC BY 4.0 BSD-3

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.