CS 229 - Machine Learning

My twin brother Afshine and I created this set of illustrated Machine Learning cheatsheets covering the content of the CS 229 class, which I TA-ed in Fall 2018 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Machine Learning.

Cheatsheets

Supervised Learning
Supervised Learning
  • Loss function, gradient descent, likelihood
  • Linear models, Support Vector Machines, generative learning
  • Tree and ensemble methods, k-NN, learning theory
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Unsupervised Learning
Unsupervised Learning
  • Expectation-Maximization, k-means, hierarchical clustering
  • Clustering assessment metrics
  • Principal component analysis, independent component analysis
PDF Web
Deep Learning
Deep Learning
  • Architecture, activation function, backpropagation, dropout
  • Convolutional layer, batch normalization, types of gates
  • Markov decision processes, Bellman equation, Q-learning
PDF Web
Tips and tricks
Tips and tricks
  • Confusion matrix, accuracy, precision, recall, F1 score, ROC
  • R squared, Mallow's CP, AIC, BIC
  • Cross-validation, regularization, bias/variance tradeoff, error analysis
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Refreshers

Probabilities and Statistics
Probabilities and Statistics
  • Axioms of probability, permutation, Bayes' rule, independence, conditional probability
  • Random variable, expectation, variance
  • Central Limit Theorem, parameter estimation
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Linear Algebra and Calculus
Linear Algebra and Calculus
  • Main matrices, multiplication, transpose, inverse, trace, determinant
  • Eigenvalue, eigenvector, norm, gradient, Hessian
  • Positive semi-definite matrix, spectral theorem, singular-value decomposition
PDF Web

Would you like to see this set of cheatsheets in your native language? You can help us translating them on GitHub!