1 Machine Learning and Graph: An introduction

 

This chapter covers:

  • An introduction to machine learning
  • An introduction to graphs
  • The role of graphs in machine learning applications

Machine Learning is a large branch in the Artificial Intelligence field. It was born in 1959, when Arthur Samuel, an IBM computer scientist, wrote the first computer program to play checkers [Samuel, 1959]. He had a clear idea in mind:

“Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort.”

He wrote the first program by assigning a score to each board position based on a fixed formula. It worked quite well, but in a second approach he had the program execute thousands of games against itself and used the results to refine the board scoring. Eventually the program reached the proficiency of a human player and Machine Learning took its first steps.

Machine Learning is the field of study in computer science that allows computer programs to learn from data.

Who should read this book

Who shouldn’t read this book

1.1   Introduction to Machine Learning

1.1.1   Machine Learning Project Lifecycle

Business Understanding

Data Understanding

Data Preparation

Modelling

Evaluation

Deployment

1.1.2   Algorithm taxonomies

Supervised vs Unsupervised Learning

Batch versus Online Learning

Instance-Based versus Model-Based Learning

Active Versus Passive Learning

1.2   Machine Learning Challenges

1.2.1   The source of truth

1.2.2   Performance

1.2.3   Storing the model

1.2.4   Real time

1.3   Graphs

1.3.1   What is a graph?

1.3.2   Representing Graphs

1.3.3   Graph as model of networks

1.3.4   Property graph model

1.4   The role of graph in the machine learning

1.4.1   Data Management

1.4.2   Data Analysis

1.4.3   Data Visualization

1.5   Summary

1.6   References

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