CS-GY 6923: Machine Learning
1.0.0
1.0.0
  • 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
Powered by GitBook
On this page
  • Classification
  • Regression

Was this helpful?

  1. Types of Machine Learning

Supervised Learning

In this kind of learning, we assume that the training examples are labeled.

There are two kinds of Supervised Learning problems:

  • Classification

  • Regression

Classification

It refers to the task of assigning a label to a given example i.e. to categorize it into one of multiply categories. Ex. spam/not spam, family car/not family car

Here, the output is discrete.

Regression

It refers to the task of predicting a real-valued output. Ex. prediction house value, temperature

Here, the output is continuous.

PreviousTypes of Machine LearningNextNotations

Last updated 5 years ago

Was this helpful?