This is a first course in computer programming for students with no prior experience. Students will learn the core process of programming: given a problem statement, how does one design an algorithm to solve that particular problem and then implement the algorithm in a computer program? The course will also introduce elementary programming concepts like basic control concepts (such as conditional statements and loops) and a few essential data types (e.g., integers and doubles). Exposure to programming will be through a self-contained user-friendly programming environment, widely used by the scientific and engineering communities, such as Matlab. The course will cover problems from all fields of science, engineering, and business.
This is a course on more complex data structures, and algorithm design and analysis, using the C language. Topics include: advanced and/or balanced search trees; hashing; further asymptotic complexity analysis; standard algorithm design techniques; graph algorithms; complex sort algorithms; and other "classic" algorithms that serve as examples of design techniques.
The aim of this course is to integrate knowledge of basic mathematics with the problems involving specification, design, and computation. By the end of the course, the student should be able to: use sets, functions, lists, and relations in the specification and design of problems; use properties of arithmetic, modular arithmetic (sum, product, exponentiation), prime numbers, greatest common divisor, factoring, Fermat?s little theorem; use binary, decimal, and base-b notation systems and translation methods; use induction to design and verify recursive programs; and implement in Scheme all algorithms considered during the course.
Schaefer School of Engineering & Science
Research & Education
Stevens Institute of Technology, Ph.D in Computer Science, 2013
Stevens Institute of Technology, MS in Computer Science, 2008
University of Delaware, BEE 2002
M. Burlick, O. Koteoglou, L. Karydas, and G. Kamberov. (2013). "Leveraging Crowsourced Data for Creating Temporal Segmentation Ground Truths of Subjective Tasks", Computer Vision and Pattern Recognition Workshop on Ground Truth.
Matt Burlick, Dimitrios Dimitriadis, Eric Zavesky. (2013). "On the Improvement of Multimodal Voice Activity Detection", Interspeech.
G. Kamberov, M. Burlick, L. Karydas and O. Koteoglou. (2012). "SCAR: Dynamic adaptation for person detection and persistence analysis in unconstrained videos", International Symposium on Visual Computing.
George Kamberov, Gerda Kamberova, Matt Burlick, Lazaros Karydas, Bart Luczynski. (2011). "Track Analysis, Data Cleansing, and Labeling", International Symposium on Visual Computing.
Barry Bunin, Alexander Sutin, George Kamberov, Heui-Seol Roh, Bart Luczynski, and Matt Burlick. (2008). "Fusion of acoustic measurements with video surveillance for estuarine threat detection", SPIE.