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Contents

Foundations

Learning from data

  1. What machine learning is , essential
  2. Data as points in space, coming soon
  3. Models and loss , essential
  4. Nearest neighbours , essential

Derivatives and gradients

  1. The derivative as a slope , essential
  2. Partial derivatives and the gradient , essential
  3. The chain rule , essential, coming soon

Linear regression and gradient descent

  1. Gradient descent , essential
  2. Linear regression with many features , essential
  3. Stochastic gradient descent , essential, coming soon
  4. Features and basis functions, coming soon

Classical machine learning

Classification

  1. Logistic regression , essential
  2. Cross-entropy loss , essential, coming soon
  3. More than two classes, coming soon
  4. Naive Bayes, coming soon

Support vector machines and kernels

  1. Support vector machines, coming soon
  2. Kernels, coming soon

Generalisation and evaluation

  1. Bias and variance of a model , essential
  2. Validation and cross-validation , essential, coming soon
  3. Learning curves, coming soon
  4. Measuring a classifier , essential, coming soon

Clustering and dimensionality reduction

  1. k-means clustering , essential
  2. Gaussian mixtures, coming soon
  3. Dimensionality reduction, coming soon

Regularisation

  1. Ridge regression , essential, coming soon
  2. The lasso and sparse models, coming soon
  3. Early stopping, coming soon

Decision trees and ensembles

  1. Decision trees , essential
  2. Random forests, coming soon
  3. Boosting, coming soon

Neural networks

Neural networks

  1. The perceptron , essential
  2. Neurons and activation functions , essential, coming soon
  3. Multilayer networks , essential, coming soon
  4. Backpropagation , essential, coming soon
  5. Training a network, coming soon

Training deep networks

  1. Momentum and adaptive optimisers, coming soon
  2. Loss landscapes, coming soon
  3. Regularising networks, coming soon

Networks for images and sequences

  1. Convolutional layers , essential, coming soon
  2. Pooling and receptive fields, coming soon
  3. Recurrent networks, coming soon
  4. Embeddings, coming soon
  5. Attention , essential, coming soon
  6. Transformers, coming soon

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Signal processing
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