Curriculum descant: machine learning for the masses

read additional material on other forms of machine learning to broaden their view of what the field includes, while not becoming overwhelmed with details of implementation. Useful data sets can often be obtained from faculty members in other departments on campus. Faculty members in other departments are likely to use other approaches (such as logistic regression) when analyzing their data, and some will welcome having a student try an entirely different approach to gain insight into the problem. The students, in turn, are able to see their computer science talents as useful relative to questions in another discipline. Another reason the course is attractive is that a teacher can give students the opportunity to use and modify large software systems. Several machine learning systems are available for downloading, for example, Students can be exposed to a culture where data sets and code are shared and can be expected to become oriented to a large system and make changes or extensions to a specific part of such a system. An undergraduate course in machine learning can be offered to a wide range of students, with minimal prerequisites, for example, to students who have completed a data structures course. Not even an artificial intelligence course need be a prerequisite; however, key topics such as search, heuristics, and representation must be introduced. Whereas a graduate level machine learning course is often structured to read scores of journal papers that describe different systems (perhaps working with three or four systems), an undergraduate-level course can eschew the " breadth " of machine learning and adopt a specific focus, Machine Learning for the Masses curriculum descant F ormerly considered an esoteric subfield of computer science, machine learning is now seeing broad use in computer science applications. It is used, for example, in search engines, computer games, adaptive user interfaces, personalized assistants, Web bots, and scientific applications. However, few colleges and universities require a course in machine learning as part of an undergraduate major in computer science. It is time for us as computer science educators to recast an introduction to machine learning concepts as a staple of a computer science education. Many possible flavors of machine learning might be emphasized, and in a one-course introduction to the field, a student must chart a course consistent with the educational environment and the instructor's background. For example, you might focus a course on a particular approach to machine …