1 Machine learning and graphs: An introduction

Machine learning is a core branch of artificial intelligence: it is the field of study in computer science that allows computer programs to learn from data. The term was coined 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.

Samuel wrote his initial program by assigning a score to each board position based on a fixed formula. This program 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.

1.1 Machine learning project life cycle

1.1.1 Business understanding

1.1.2 Data understanding

1.1.3 Data preparation

1.1.4 Modeling

1.1.5 Evaluation

1.1.6 Deployment

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 Graphs as models of networks

1.4 The role of graphs in machine learning

1.4.1 Data management