jarek-pawlowski / advanced-machine-learning

notes for Advanced Machine Learning course

advanced-machine-learning

notes for Advanced Machine Learning course

timeline and grading

lecture notes:

• machine learning fundamentals
• decision trees
• deep neural networks
• convolutional neural networks
• computer vision meets deep learning: part1, part2
• recurrent neural networks and attention
• autoencoders and GANs

laboratory classes

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

2. ECG signal classification

• classifiers comparison: SVM, decision trees, random forests
• feature vectors

Colab notebook

3. Image classification using deep CNNs

• VGG, ResNet

Colab notebook

4. Augmentation in image processing, two separated tasks:

• take MNIST or CIFAR dataset, apply some simple geometric transformations (see e.g. lecture), and check if such dataset extending improves accuracy (take some CNN model from previous labs)
• play with one-shot style transfer, understand the idea and run some exemplary code

• papers:
  • Gatys original paper
  • https://arxiv.org/pdf/1904.11617v1.pdf
• code:
  • https://github.com/limingcv/Photorealistic-Style-Transfer
  • https://github.com/KushajveerSingh/Photorealistic-Style-Transfer

proposed seminars topics

• list of proposed topics
• link to form with seminars dates