Introduction

This chapter provides a brief overview of statistical learning and its relation to classical statistics using as reference the two statistical cultures of Breiman: data modeling (inference) and algorithm modeling (predictive). Research in neural networks involves different groups of scientists in neuro-sciences, psychology, engineering, computer science, and mathematics. All these groups pose different questions: neuroscientists and psychologists want to know how the animal brain works, engineers and computer scientists want to build intelligent machines and mathematicians want to understand the fundamentals properties of networks as complex systems. The chapter also provides an overview of the key concepts discussed in this book. The book describes data analysis techniques using algorithm modeling of artificial neural networks. It also alerts statisticians to the parallel works of neural network researchers in the area of data science.