1. Supervised Learning
  2. Classification
  3. Ensemble methods
  4. Boosting
Machine Learning Algorithms
  • Home
  • Introduction to MLA
  • Unsupervised Learning
    • Representation learning
      • Principal component analysis (PCA)
      • Kernel PCA
    • Clustering
      • K-means clustering
      • Kernel K-means clustering
    • Estimation
      • Maximum likelihood estimation (MLE)
      • Bayesian estimation
      • Gaussian mixture model (GMM)
      • Expectation-maximization (EM) algorithm
  • Supervised Learning
    • Regression
      • Least squares regression
      • Kernel Least squares regression
      • Bayesian view of least squares regression
      • Ridge regression
      • LASSO regression
    • Classification
      • K-nearest neighbors (KNN)
      • Decision tree
      • Generative models
        • Naive Bayes
      • Discriminative models
        • Perceptron
        • Logistic regression
        • Support vector machines (SVMs)
      • Ensemble methods
        • Bagging
        • Boosting
  • Deep Learning
    • Notations
    • Neural Networks
      • Introduction
      • Activations
      • Forward Propagation
      • Backpropagation
      • Gradient Descent
    • Practical DL
      • Weight Initializations
  • Reinforcement Learning
    • Multi-Armed Bandit
  • Basic Cognitive Processes
    • Foundations of Psychological Concepts and Theories-I
    • Foundations of Psychological Concepts and Theories-II
    • Understanding Research Designs
    • Vision and Perception
    • Perception Theories and Sensory Processing
    • Attention and Memory Mechanisms
    • Memory Systems and Processes
    • Memory and Brain Function
  • Others
    • FastPitch HiFi-GAN Pipeline
  • About me
Bagging
Notations