Abstract
This chapter aims to be a smooth introduction to the basic concepts of machine learning, and, building on them, explain some to the latest advanced techniques. After a brief historical perspective, we overview the two currently most popular machine learning frameworks—deep learning and probabilistic graphical models. We conclude the chapter with practical advices about machine learning experiments which are necessary to know for a beginner. A good understanding of these fundamentals opens up a wide portfolio of opportunities for predictive models in transportation, and is hopefully a good basis for the remainder of this book.
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