Program Announcement 2013 - 14 - Institute of Business ...

Description of Courses170CSE651 Advanced Analysis of AlgorithmsThe objective of this course is to read advanced topicsof the core area of computer science which enablesstudents to focus on the study of abstract models ofcomputation. These abstract models allow the studentsto assess via formal reasoning what could be achievedthrough computing when they are using it to solveproblems in science and engineering. The courseexposes students to the computability theory, as wellas to the complexity theory. The goal is to allow themto answer fundamental questions about problems,such as whether they can or not be computed, and ifthey can, how efficiently.The course defines advanced computation models andtheir properties, and the necessary mathematicaltechniques to prove more advanced attributes of thesemodels. The students will be able to express computerscience problems as mathematical statements and toformulate proofs. Upon completion of this course thestudents are expected to become proficient in keytopics of theory of computation, and to have theopportunity to explore the current topics in this area.CSE652 Knowledge Discovery and Data MiningThis course will offer a comprehensive coverage ofwell known Data Mining topics including classification,clustering, and association rules. A number of specificalgorithms and techniques under each category willbe discussed. Methods for feature selection,dimensionality reduction and performance evaluationwill also be covered. Students will be exposed torelevant Data Mining research.CSE655 Probabilistic ReasoningThis course provides an in-depth analysis of BayesianBelief Networks which have become the tool of choicefor reasoning under uncertainty. The course focuseson the syntax and semantics of Bayesian Networksand how to use BNs to model and analyze uncertainsituations. Models that aim to integrate time anduncertainty such as dynamic Bayesian networks,dynamic Influence Nets, Markov Nets as well as thelearning of structure and parameters of a Bayesiannetwork will be discussed in detail. The course isintended for graduate level CS students and a significantamount of time will be spend on the current researchissues in the field of probabilistic reasoning. Studentsare expected to use various softwares and developtheir own tool to implement various reasoning andlearning algorithms.CSE656 Computational IntelligenceThe course presents the fundamentals of computationalintelligence. Topics included are evolutionaryalgorithms, particle swarm optimization, ant colonyoptimization, artificial immune systems, artificialneural networks, fuzzy systems and hybridization ofthe above techniques. The course focuses on theapplications of these techniques on real world problemsand shows how they are designed, implemented andanalyzed for a given a problem domain. Real worldapplications include portfolio optimization in financialdomain, control and strategy optimization in robotics,route optimization in path planning and network trafficflows, and so on. A significant amount of time will bespent on the current research issues in computationalintelligence. This is a programming intensive courseand students are expected to spend a significant amountof time on implementing all the techniques studied inthe course and applying them on a significantly complexproblem of their choice.CSE660 Computer VisionComputer Vision is a study in algorithms, tools andtechniques that enable computers to extractinformation out of digital images. Computer Vision isbeing used extensively for driving autonomous vehicles,planetary exploration, life-saving medical imagingtechniques, enhancing the Human-Computer-Interaction experience and preservation of accumulatedhuman wisdom by digitizing books. This course willoffer an introduction to the core concepts, someindustrial-strength tools and recent breakthroughs inComputer Vision. By the end of this course, studentsshould be able to analyze problems in Computer Vision,break them into simpler problems, associate thesesimpler problems to established solution techniques,design efficient algorithms using these establishedtechniques and make computer programs based onthese algorithms.On the research front, the Faculty of CS contributedsignificantly towards top quality journal research papersindexed by ISI Thompson. In addition, the TRLs WirelessSensor Networks (WSN) Lab expanded its portfolio of sensorequipment to include a complete set of new sensorequipment from Hanbeck which included acceleration, GPS,pressure and humidity sensors apart from the earlier lightand temperature sensors from Crossbow……………. Dr. Sayeed Ghani