Introduction to Machine Learning with Applications in Information Security-CRC(2018).epub
For the past several years, I’ve been teaching a class on “Topics in Information Security.” Each time I taught this course, I’d sneak in a few more machine learning topics. For the past couple of years, the class has been turned on its head, with machine learning being the focus, and information security only making its appearance in the applications. Unable to find a suitable textbook, I wrote a manuscript, which slowly evolved into this
In my machine learning class, we spend about two weeks on each of the major topics in this book (HMM, PHMM, PCA, SVM, and clustering). For each of these topics, about one week is devoted to the technical details in Part I, and another lecture or two is spent on the corresponding applications in Part II. The material in Part I is not easy—by including relevant applications, the material is reinforced, and the pace is more reasonable.
I also spend a week covering the data analysis topics in Chapter 8 and several of the mini topics in Chapter 7 are covered, based on time constraints and student interest.1
Machine learning is an ideal subject for substantive projects. In topics classes, I always require projects, which are usually completed by pairs of students, although individual projects are allowed. At least one week is allocated to student presentations of their project results.
A suggested syllabus is given in Table 1. This syllabus should leave time for tests, project presentations, and selected special topics. Note that the applications material in Part II is intermixed with the material in Part I. Also note that the data analysis chapter is covered early, since it’s relevant to all of the applications in Part II.