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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  • Main Use Cases
  • How is Machine Learning Getting Better?

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What is Machine Learning?

Machine Learning is the field of study that deals with making computers "learn" from data/experience.

Main Use Cases

Machine Learning is mainly useful in cases where:

  • there is no human expertise available to solve a problem

  • there is human expertise, but humans cannot explain the expertise well enough (if at all) to be able to hard-code a program

  • the solution changes with time

  • a customized solution is required, ex. targeted marketing/advertising

How is Machine Learning Getting Better?

Advances in ML are mainly owed to:

  • the increased amounts of data

  • the development of better algorithms

  • the availability of cheap and more powerful computing resources

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Last updated 5 years ago

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