Programming Books

Do Math For Machine Learning & Artificial Intelligence

Download Math For Machine Learning free in PDF. This is the first textbook in math for machine learning. This book will get you started machine learning in a smooth way. This book preparing you for more advance topics and dispelling the belief that machine learning is complicated, difficult and intimidating.

This book not only explain what kind of math is involved and the confusing notation, it also introduce you directly to the foundational topics in machine learning.

You learn these topics from this notes

1.Introduction

2. Linear Regression

  • The Least Squares Method
  • Linear Algebra For Least Squares Problems
  • Example: Linear Regression
  • Problem Set: Linear Regression

3. Linear Discriminant Analysis

  • Classification
  • The Posterior Probability Function
  • Modeling The Posterior Probability Function
  • Estimated The Linear Discriminant Analysis

4. Logistic Regression

  • Estimated The Posterior Probability Function
  • The Multivariate Newton-Raphson Method
  • Maximizing The Log-Likelihood Function
  • Example: Logistic Function 

5. Artificial Neural Networks

  • Forward Propagation
  • Choosing Activation Function
  • Estimating The Output Function
  • Error Function For Regression
  • Error Function For Binary Classification

6. Maximal Margin Classifier

  • Definition Of Separating Hyper plane and Margin
  • Definition Of Maximal Margin Classifier
  • Reformulating The Optimizing Classifier
  • KKT Conditions
  • Primal And Dual Problems

7. Support Vector Classifier

  • Slack Variables: Point Of Correct Side Of Hyper Plane
  • Slack Variables: Point Of Wrong Side Of Hyper Plane
  • The Coefficient For The Soft Margin Classifier
  • Classifying Test Point 

8. Support Vector Machine Classifier

  • Enlarging The Feature Space
  • The Kernel Trick
  • Support Vector Machine Classifier Example 1
  • Support Vector Machine Classifier Example 2
  • Summary: Support Vector Machine Classifier 

   

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