ML4NS

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Machine Learning for Neuroscience Module, Spring 2023 (https://www.imperial.ac.uk/study/courses/postgraduate-taught/translational-neuroscience/)

ML4NS 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Machine Learning for Neuroscience
 Department of Brain Sciences 
 Imperial College London &#38; 
 Care Research and Technology Centre 
 The UK Dementia Research Institute

**Contributors:**

|<img src="https://github.com/PBarnaghi/ML4NS/blob/main/Misc/images/FP.png" width="120" height="120"> [Francesca Palermo](https://francescapalermo.github.io/) 	|<img src="https://github.com/PBarnaghi/ML4NS/blob/main/Misc/images/NFL.png" width="120" height="120"> [Nan Fletcher-Lloyd](https://github.com/NVFL) 	|<img src="https://github.com/PBarnaghi/ML4NS/blob/main/Misc/images/AC.png" width="120" height="120"> [Alexander Capstick](https://alexcapstick.github.io/) 	|<img src="https://github.com/PBarnaghi/ML4NS/blob/main/Misc/images/YC.png" width="120" height="120"> [Yu Chen](https://scholar.google.com/citations?user=M_lKV1sAAAAJ&hl=en) 	|
|---	|---	|---	|---	|

**Virtual environment settings:**

[YML file](./Virtual%20Environment%20Settings/virtual_env.yml) 
(for more informaiton and for creating an environment from an environment.yml file, see this [link](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file))

**Lab experiments:**

To run the lab experiments, you can use [Anaconda](https://docs.anaconda.com/ae-notebooks/user-guide/basic-tasks/apps/jupyter/index.html), [Visual Studio Code](https://code.visualstudio.com/docs/datascience/jupyter-notebooks), or upload the Jupyuter Notebooks directly to Google CoLab. 
For more information on how to use Google Colab with GitHub, see this [link](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=-pVhOfzLx9us).

**Mathematical symbols and notations:**

[Notations](./Misc/Mathematical_Notations.pdf) 
[Basics of Matrix Algebra](./Misc/Matrix_Algebra.pdf)

**Slides and notes:**

Updated after the lectures: [Annoated slides](https://github.com/PBarnaghi/ML4NS/tree/main/Annotated%20slides)

Tutorials:

&nbsp;&nbsp;&nbsp;[Python for Beginners](./00-%20Tutorials/Python%20for%20Beginners%20Tutorial/Python%20for%20Beginners%20(run).ipynb) &nbsp;&nbsp;  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1PCHpLzcLowkWnoovYm7DpJud4xRQwO2l/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/python-for-beginners)

&nbsp;&nbsp;&nbsp; [Machine Learning for Beginners (introduction to scikit-learn)](./00-%20Tutorials/Machine%20Learning%20for%20Beginners%20Tutorial%20and%20Assessment/Machine%20Learning%20for%20Beginners%20(run).ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1kv2p2G0uWfrCBxLtget9n8FJapoxAzuG/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/ml4beginners)

&nbsp;&nbsp;&nbsp;[Summary](./00-%20Tutorials/ML4Neuroscience_tutorials.pdf)

1. Introduction to Machine Learning
 [Notes](./01-%20Introduction%20to%20ML/01_Introduction_to_Machine_Learning.pdf)
 [Slides](./01-%20Introduction%20to%20ML/Lecture1_Introduction.pptx)
 [Lab Notebook](./00-%20Tutorials/Machine%20Learning%20for%20Beginners%20Tutorial%20and%20Assessment/Machine%20Learning%20for%20Beginners.ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1kv2p2G0uWfrCBxLtget9n8FJapoxAzuG/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/ml4beginners)
 [Lab Questions](./00-%20Tutorials/Machine%20Learning%20for%20Beginners%20Tutorial%20and%20Assessment/Machine%20Learning%20for%20Beginners%20Assessment.ipynb)

2. Regression Models and Linear Prediction
 [Notes](./02-%20Regression%20models%20and%20linear%20prediction/02-Linear_Models.pdf)
 [Slides](./02-%20Regression%20models%20and%20linear%20prediction/ML4NuerScience_Linear_models.pptx)
 [Lab Notebook](./02-%20Regression%20models%20and%20linear%20prediction/Lab/Linear_models_lab.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1N5z8Stt3XX5bknr8Inl64e0ABHlYfY3V/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/linear-models)
 [Lab Notebook (run)](./02-%20Regression%20models%20and%20linear%20prediction/Lab/Linear_models_lab_run.ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1o2WBo7CrEnr0eX0T-OPbHYlnggcaorJh/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/linear-models-run)

3. Probability and Information Theory
 [Notes](./03-%20Probability%20and%20Information%20Theory/03_Probability_and_Information_Theory.pdf)
 [Slides](./03-%20Probability%20and%20Information%20Theory/ML4NuerScience_Probability_info_theory.pptx)
 [Notebook: PDF and CDF Example](./03-%20Probability%20and%20Information%20Theory/PDF_CDF.ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1XLpKFD1JhIA7xQYCwiCclkzxVuvkOa3u/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/code/payambarnaghi/pdf-and-cdf-sample)

4. Bayesian Models 
 [Notes](./04-%20Bayesian%20Models/04-Bayesian_models.pdf)
 [Slides](./04-%20Bayesian%20Models/ML4NuerScience_BayesianModels.pptx)
 [Lab Notebook](./04-%20Bayesian%20Models/Lab/Probability%20and%20Bayesian%20Theory.ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1Iz280hqkfS0jFLhL0pWWi95RxujHDLgP/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/code/payambarnaghi/probability-and-bayesian-theory)
 [Lab Notebook (run)](./04-%20Bayesian%20Models/Lab/Probability%20and%20Bayesian%20Theory%20(run).ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1Xncy9PS_bz2GzY744V8uQ26u7m2iJ6B8/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/probability-and-bayesian-theory-run)
 
5. Support Vector Machines and Ensemble Models
 [Notes](./05-%20Ensemble%20models-SVM/05-Ensemble%20models_and_Kernel-based_models.pdf)
 [Slides](./05-%20Ensemble%20models-SVM/ML4NuerScience_ensemble%20models_kernel_models.pptx)
 [Lab Notebook](./05-%20Ensemble%20models-SVM/Lab/SVM_DT_RF_Lab.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1aruY0xQYgN7YQOCdoGayjNgFfVYUaooU/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/svm-decision-trees-random-forest)
 [Lab Notebook (run)](./05-%20Ensemble%20models-SVM/Lab/SVM_DT_RF_lab_run.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1JkjkGZxIXtAp6Clh33fLGSdnNSAGFi-q/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/svm-decision-trees-random-forest-run)

6. Neural Networks
 [Notes](./06-%20Neural%20Networks/06_Neural_Networks.pdf) 
 [Slides](./06-%20Neural%20Networks/ML4NuerScience_NeuralNets.pptx)
 [Notebook: Sigmoid Function](./06-%20Neural%20Networks/sigmoid.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1E_S03sV0QssvMfzEn-R8whAjULzbtD-d/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/sigmoid)
 [Lab Notebook and introduction to Pytorch](./06-%20Neural%20Networks/Lab/neural_network_lab.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1yRfxh2JFjRjbiytEv6qDtpQjfKln6OEU/view?usp=share_link) 
 [Lab Notebook and introduction to Pytorch (run)](./06-%20Neural%20Networks/Lab/neural_network_lab_run.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1E7ueGh1GgUMaKmqsgiT_DPw1I5mQKPkR/view?usp=share_link)
 
7. Convolutional Neural Networks
 [Notes](./07-%20Convolutional%20Neural%20Networks/07-Convolutional_Neural_Networks%20(CNNs).pdf) 
 [Slides](./07-%20Convolutional%20Neural%20Networks/ML4NuerScience_CNN.pptx) 
 [CNN Example for Edge Detection](./07-%20Convolutional%20Neural%20Networks/CNN_edge_detection_sample.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1uUT81LUetPZ15MdU1PrveZLhuMqh6vxU/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/ccn-edge-detection)
 [Lab Notebook (working with CIFAR10 dataset)](./07-%20Convolutional%20Neural%20Networks/Lab/Lab07%20-%20Convolutional%20Neural%20Network.ipynb) &nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1FY3t-4qndg4WxZQn7vJ9K1VC1tPpjKCI/view?usp=share_link) &nbsp; [![Open In kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/payambarnaghi/convnet)
 [Lab Notebook (working with Alzheimer MRI Preprocessed Dataset)](./07-%20Convolutional%20Neural%20Networks/Lab/Lab07-MRI_Dementia.ipynb) 
 [Alzheimer's Disease- Preprocessed MRI Dataset](https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset) 
 
8. Applications in neuroscience and neuroscience inspired models 
 [Notes](./08-%20Applicaitons%20in%20neurosience%20and%20neuroscience%20inspired%20ML/08_Applications.pdf) 
 [Slides](./08-%20Applicaitons%20in%20neurosience%20and%20neuroscience%20inspired%20ML/ML4NuerScience_Applications.pptx)

9. Seminar - Ethical considerations and responsible machine learning 
 [Summary](./09-%20Seminar%20-%20Ethical%20considerations/09-%20Ethical_considerations.pdf) 
 [Notes and slides](./09-%20Seminar%20-%20Ethical%20considerations/readme.md)

10. Final Project 
 [Notes](./10-%20Final%20Project) 
 
11. Summary and overview 
 [Slides](./11-%20Summary/ML4NuerScience_Summary.pptx)
 
<hr>

[](#GenerativeAI)
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](./12-%20Advanced%20topics%20(optional)/Transformers/ML4NuerScience_transformers.pptx) 
 [Annotated Slides](./12-%20Advanced%20topics%20(optional)/Transformers/ML4NuerScience_transformers.pptx) 
 [Notebook (Sample Transformer)](./12-%20Advanced%20topics%20(optional)/Transformers/Code/transformer.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1_0tNoO5V106R0JyBjSC4uDeL81HG3HCI/view?usp=share_link) 
 [Notebook (OpenAI GPT sample)](./12-%20Advanced%20topics%20(optional)/Transformers/Code/GPT_example.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1qX_b6iorlqtL9SCtbl6E_9_0ulnnH2Sn/view?usp=share_link) 
 [Video recording](https://imperiallondon-my.sharepoint.com/personal/pbarnagh_ic_ac_uk/_layouts/15/stream.aspx?id=%2Fpersonal%2Fpbarnagh%5Fic%5Fac%5Fuk%2FDocuments%2FRecordings%2FML4Neuroscience%20%2D%20Transformers%2D20230509%5F100633%2DMeeting%20Recording%2Emp4&ga=1)

II. Variational Autoencoders 
 [Slides](./12-%20Advanced%20topics%20(optional)/VAE/ML4NuerScience_VAE.pptx) 
 [Notebook](./12-%20Advanced%20topics%20(optional)/VAE/code/vae.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1UTR6oWeJOGIZap8rHLuT2uGFGxSnwody/view?usp=share_link) 
 [Video recording](https://imperiallondon-my.sharepoint.com/personal/pbarnagh_ic_ac_uk/_layouts/15/stream.aspx?id=%2Fpersonal%2Fpbarnagh%5Fic%5Fac%5Fuk%2FDocuments%2FRecordings%2FML4Neuroscience%20%2D%20Transformers%2D20230510%5F103834%2DMeeting%20Recording%2Emp4&referrer=Teams%2ETEAMS%2DELECTRON&referrerScenario=p2p%5Fns%2Dbim&ga=1) 
 
III. Diffusion models 
 [Slides](./12-%20Advanced%20topics%20(optional)/Diff_Modles/ML4NuerScience_diffusion.pptx) 
 [Notebook](./12-%20Advanced%20topics%20(optional)/Diff_Modles/code/denoising_diffusion.ipynb)&nbsp;&nbsp; [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1GFxk0a8zD-6D6ByimEjcKVK2L4eIM4Rh/view?usp=share_link) 
 [Video recording](https://imperiallondon-my.sharepoint.com/personal/pbarnagh_ic_ac_uk/_layouts/15/stream.aspx?id=%2Fpersonal%2Fpbarnagh%5Fic%5Fac%5Fuk%2FDocuments%2FRecordings%2FML4Neuroscience%20%2D%20Transformers%2D20230512%5F100623%2DMeeting%20Recording%2Emp4&ga=1)
 [Video on YouTube](https://www.youtube.com/watch?v=dF5ocaOcQt8&feature=youtu.be)
 [Hugging Face- Train a diffusion model](https://huggingface.co/docs/diffusers/main/en/tutorials/basic_training)

## Licensing

[![CC BY 4.0][cc-by-image]][cc-by]

[![CC BY 4.0][cc-by-shield]][cc-by] [![BSD-3][bsd-3-shield]][bsd-3]

The contents of this repository are shared under under a [Creative Commons Attribution 4.0 International License][cc-by].

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

[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg?style=plastic&logo=appveyor&color=blue

[bsd-3]: https://opensource.org/licenses/BSD-3-Clause
[bsd-3-shield]: https://img.shields.io/pypi/l/qsurface?style=plastic&logo=appveyor&color=blue

ML4NS

ML4NS 최신버젼 다운로드

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