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machine-learning-applications

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notes for Machine Learning -- Applications course

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# machine-learning-applications
notes for Machine Learning -- Applications course

[timeline and grading](rules.pdf)

## lecture notes:
- [machine learning fundamentals](https://drive.google.com/file/d/1rTykp3Q3LSMrvouS1PCWwjdKNsW0uaJY/view?usp=sharing)
- [deep neural networks](https://drive.google.com/file/d/1V7Lf-F7PXAvxHVU7rc_7DMA0xduNcCRW/view?usp=sharing)
- [decision trees and ensemble methods](https://drive.google.com/file/d/1mefPD7BHD-Qb7pyoCEOynCBuUdhs-Pro/view?usp=sharing)
- [convolutional neural networks](https://drive.google.com/file/d/1fGrlxENrbOEAaq0pUP7b2rcaa-7Ioxs8/view?usp=sharing)
- [computer vision: traditional methods](https://drive.google.com/file/d/1n2MOt2PkNl_4-wGW3q9o95zLqPaF3WYO/view?usp=sharing)
- [computer vision: deep-learning methods](https://drive.google.com/file/d/1wiWTQniLMvZVWVqxcSsoNdE57vneVf-2/view?usp=sharing)
- [recurrent neural networks](https://drive.google.com/file/d/1mBBBH6bfWt3gP_y3exfIHpnuVbi8x4QB/view?usp=sharing)
- [attention and transformers](https://drive.google.com/file/d/1PYARx84U70I_v-0UMalmJdrYfbtK9q6u/view?usp=sharing)
- [autoencoders and GANs](https://drive.google.com/file/d/1ub8-l1em4A22HCeephXdbNPEZCBJCw3Y/view?usp=sharing)
- [graph- and group-equvariant- neural networks](https://drive.google.com/file/d/1IAOaZSrGka6yIboazptO29aMYYnMy9Yw/view?usp=sharing)
- [self- and semi-supervised learning, synthetic data](https://drive.google.com/file/d/1e7cKVR90kfm1oT11cy9hlBknMmGt9XC4/view?usp=sharing)

## laboratory classes
0. Preliminary problems
[Colab notebook](preliminary_problems.ipynb)

1. Handwritten digits classification using MNIST dataset with Pytorch
- models: perceptron, deep fully-connected network, generic CNN
- various activations,
- overfitting,
- regularization, early stopping

[Colab notebook](mnist_in_3_flavours.ipynb)

![overfitted model](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/Deep.png)

2. ECG signal classification
- classifiers comparison: SVM, decision trees, random forests
- feature vectors

[Colab notebook](ecg_classification.ipynb)

![ecg arrhythimas](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/signals.png)

3. Image classification using deep CNNs
- VGG, ResNet

[Colab notebook](advancedCNNs.ipynb)

![example results for VGG](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/VGGs.png)

4. Regularization
- L2 and L1 regularization implemented by hand

[Colab notebook](regularization.ipynb)

![regularization results](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/regularization.png)
![regularization results](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/regularization1.png)

5. Augmentation in image processing, two separated tasks:
- take MNIST or CIFAR dataset, apply some simple geometric transformations (see e.g. [lecture](CV2.pdf)), and check if such dataset extending improves accuracy (take some CNN model from previous labs):
    * use simple transformations (e.g. flip, rotate, translate, scale) using [scikit-image](https://scikit-image.org/docs/dev/api/skimage.transform.html), or [open-cv](https://docs.opencv.org/4.x/da/d6e/tutorial_py_geometric_transformations.html)
    * or use [albumentations](https://github.com/albumentations-team/albumentations) library, demo: https://demo.albumentations.ai/
    > * example of combining *albumentations* with pytorch *Dataset* is presented [here](pytorch_albumentations.ipynb)
    * in case of MNIST verify if applying flips or rotations > 45 deg improve accuracy or not, why?
- play with one-shot style transfer that might be also used for images augmentation (e.g. see [here](https://www.nature.com/articles/s41598-022-09264-z)), understand the idea, and run some exemplary code on your own images
> * papers:
>   * [Gatys original paper](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf)
>   * https://arxiv.org/pdf/1904.11617v1.pdf 
> * code:
>   * https://github.com/limingcv/Photorealistic-Style-Transfer 
>   * https://github.com/KushajveerSingh/Photorealistic-Style-Transfer

6. Transformer network
- self-attention mechanism
- and Transformer encoder implemented from scratch

[Colab notebook](simple_Transformer.ipynb)

![attention map](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/attention_head.png)

7. Convolutional GAN on MNIST
- generative adversarial network model: generator & discriminator 
- training GANs

[Colab notebook](GAN_on_MNIST.ipynb)

![example results for GAN model](https://raw.githubusercontent.com/jarek-pawlowski/machine-learning-applications/main/generated_mnist.png)

## proposed seminars topics
- [list of proposed topics](seminars_topics.pdf)
- [link to form with seminar dates](https://docs.google.com/spreadsheets/d/1yV1DoIH8dCWvYZrYkYNTnBPbCys-nYlkhG-Fmo4VJBw/edit?usp=sharing)

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