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Teaching

CS 229 ― Machine Learning
Star


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.

Cheatsheet


Supervised Learning
  • • Loss function, gradient descent, likelihood
  • • Linear models, Support Vector Machines, generative learning
  • • Tree and ensemble methods, k-NN, learning theory
Unsupervised Learning
  • • Expectation-Maximization, k-means, hierarchical clustering
  • • Clustering assessment metrics
  • • Principal component analysis, independent component analysis
Deep Learning
  • • Architecture, activation function, backpropagation, dropout
  • • Convolutional layer, batch normalization, types of gates
  • • Markov decision processes, Bellman equation, Q-learning
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

Refresher


Probabilities and Statistics
  • • Axioms of probability, permutation, Bayes' rule, independence, conditional probability
  • • Random variable, expectation, variance
  • • Central Limit Theorem, parameter estimation
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