CS-GY 6923: Machine Learning
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  • Introduction
  • What is Machine Learning?
  • Types of Machine Learning
    • Supervised Learning
      • Notations
      • Probabilistic Modeling
        • Naive Bayes Classifier
      • Linear Regression
      • Nearest Neighbor
      • Evaluating a Classifier
      • Parametric Estimation
        • Bayesian Approach to Parameter Estimation
        • Parametric Estimation for Simple Linear Regression
        • Parametric Estimation for Multivariate Linear Regression
        • Parametric Estimation for Simple Polynomial Regression
        • Parametric Estimation for Multivariate Polynomial Regression
      • Bias and Variance of an Estimator
      • Bias and Variance of a Regression Algorithm
        • Model Selection
      • Logistic Regression
      • Decision Trees
        • Using Decision Trees for Regression
        • Bias and Variance
      • Dimensionality Reduction
      • Neural Networks
        • Training a Neuron
        • MLP
          • Regression with Multiple Outputs
          • Advice/Tricks and Issues to Train a Neural Network
        • Deep Learning
      • Support Vector Machines
      • Ensemble Learning
    • Unsupervised Learning
      • K-Means Clustering
      • Probabilistic Clustering
    • Reinforcement Learning
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  1. Types of Machine Learning

Unsupervised Learning

In this kind of learning, we do not have labeled training data.

The most common unsupervised problem is clustering. There are also other unsupervised problems such as dimensionality reduction.

Some applications of clustering include:

  • color quantization (16 million colors to 256 colors)

  • directed marketing (different ads for different groups of people)

  • pre-processing stage in supervised learning (in certain cases where it is clear that the examples are coming from well-defined groups)

PreviousEnsemble LearningNextK-Means Clustering

Last updated 5 years ago

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