This chapter provides an overview of the basic principles of ML, outlining the fundamental concepts that need to be applied correctly for a broad range of radar applications. We expect the reader to have background knowledge of basic linear algebra and probability theory, which form the foundations of ML. In Section 2.1, we describe the concept of learning from data and introduce the main categories of ML, namely, supervised and unsupervised learning. We also present different tasks that ML can tackle under each category and provide relevant radar-based examples. In Section 2.2, we briefly describe the various components of an ML algorithm. We present several fundamental techniques of supervised and unsupervised learning in Section 2.3. In Section 2.4, we define various performance assessment metrics and describe the design and evaluation of a learning algorithm. More recent learning approaches, such as variants of deep neural networks (DNNs), and more specific ML tools related to the various radar applications will follow in subsequent chapters of this book.
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