Topics in Machine Learning

The Learning Machine covers a broad range of topics in machine learning, including essential mathematical pre-requisities and diverse applications.

Curriculum

  • Pre-requisites
    • Probability and statistics
    • Linear algebra
    • Numerical optimization
  • Fundamentals
    • Model selection
    • Bias-variance trade-off
    • No free lunch theorem
  • Classification
    • Perceptron learning algorithm
    • Naive Bayes
    • Decision trees
    • Random forests
    • Nearest Neighbor
    • Logistic regression
  • Regression
    • Linear least squares regression
    • Linear basis function models
    • Nearest neighbor
    • Random forest
    • Bayesian linear regression
    • Multiple regression
  • Neural networks
    • Feed-forward neural networks
    • Backpropagation
    • Regularization
    • Bayesian neural networks
  • Kernel methods
    • Dual representations
    • Radial basis function networks
    • Gaussian processes
    • Support vector machines
  • Graphical models
    • Bayesian networks
    • Conditional independence
    • D-separation
    • Markov random fields
    • Inference in graphical models
  • Clustering
    • K-means
    • Hierarchical agglomerative clustering
    • Gaussian mixture models
  • Inference techniques
    • Expectation maximization
    • Variational inference
  • Sampling methods
    • Rejection sampling
    • Importance sampling
    • Markov chain Monte Carlo (MCMC)
    • Metropolis-Hastings algorithm
    • Gibbs sampling
  • Continuous latent variable models
    • Principal component analysis
    • Kernel PCA
    • Independent component analysis
  • Sequential data
    • Hidden markov models
    • Linear dynamical systems
    • Particle filters
  • Ensemble methods
    • Boosting
    • Bagging
  • Topic models
    • Probabilistic latent semantic analysis (PLSA)
    • Latent Dirichlet allocation (LDA)
    • Supervised-LDA
  • Deep neural networks
    • Multilayer perceptrons
    • Recurrent neural networks
    • Convolutional neural networks
    • Long short term memory (LSTM)
    • DNNs for vision
    • DNNs for natural language processing
    • DNNs for speech
  • Reinforcement learning
    • Multi-armed bandits
    • Finite Markov Decision Processes
    • Policy iteration
    • Value iteration
    • Temporal difference learning
    • Deep reinforcement learning
  • Applications
    • Text-mining
    • Anomaly detection
    • Collaborative filtering
    • Natural language processing
    • Computer vision