Machine learning techniques can be successfully applied to a wide range of important problems, including speech recognition, natural language processing, bioinformatics, stock market analysis, information security, and the homework problems. This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book focuses on the machine learning applications specifically on malware. Most machine learning techniques do ultimately rest on some fancy math. For example, hidden Markov models (HMM) build on a foundation of discrete probability, principal component analysis (PCA) is based on sophisticated linear algebra. Lagrange multipliers are used to show how and why a support vector machine (SVM) really works, and statistical concepts abound. The book considers the author's industrious students' research projects. It reviews the necessary linear algebra, and relevant math and statistics topics as needed. The book explores some knowledge of differential calculus—specifically, finding the maximum and minimum of "nice" functions.