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

Was this helpful?

Introduction

This book contains my notes for the "Machine Learning" (CS-GY 6923) course that I had taken at NYU Tandon School of Engineering in the Fall of 2018, towards my MS in Computer Science.

The contents of this book have been acquired from several sources, including the presentation slides provided by Prof. Lisa Hellerstein.

Some prerequisites include an understanding of:

  • Probability and Statistics

  • Simple Differentiation and Integration (especially for polynomials)

  • Partial Differentiation

  • Chain Rule

  • Linear Algebra

  • Eigenvectors and Eigenvalues

NextWhat is Machine Learning?

Last updated 5 years ago

Was this helpful?