Teaching Pattern Recognition: A Multidisciplinary Experience

The solution to many open problems in science and engineering requires approaches that are multidisciplinary in nature.Therefore, state-of-the-art education needs to prepare prospective scientists and engineers to not only explore the boundaries within their own disciplines, but to also understand the basics of other disciplines.Accomplishing this important mission requires careful planning, selection of appropriate topics, and development of realistic educational objectives to promote cooperation and integration between students with various backgrounds.Aiming for such a goal, in the spring of 2013, a graduate level course on "pattern recognition" was piloted in the Computer Engineering and Computer Science department of California State University, Long Beach.The course was offered under the name "CECS 590-Special Topics in Computer Science" and several graduate students from various backgrounds (Biology, Mathematics and Computer Science) were enrolled.Throughout the semester, students learned about different machine learning techniques and algorithms, and implemented multidisciplinary projects which required the application of those methods in order to solve real-world problems in biomedical science.In this paper we present the results of our pilot offering.We provide details about the course objectives, structure, assessment tools and outcomes.We also discuss some of the challenges confronted in teaching such a multidisciplinary class and the approaches undertaken to address those issues.Hence, the presented results of our pilot offering could be of value to other multidisciplinary educators.