Module manual for the course of studiesApplied Computer Science (M.Sc.)Faculty of Computer Scienceversion 08/2020
ContentObligatory . 3Agile Software Development . 4Computer Graphics 1 . 6Computational Intelligence . 8Distributed Systems .10IT Security .12Mobile Systems.13Service-oriented Networks .15Signals & Systems .16Web Applications .18Optional Section I .19IT-Security (adv. chapters). .20Distributed Systems Advanced Chapters. .21Semantic Technologies in Distributed Systems .22Software Quality.23Text Mining Search .24Optional Section II .26eBusiness .27Human-Computer Interaction .29Image Processing 1 .31Image Processing 2 .32Media Production 1 .33Module description "Applied Computer Science (M.Sc.)"ContentPage 2
ObligatoryModule description "Applied Computer Science (M.Sc.)"ContentPage 3
Module NameAgile Software DevelopmentModule ResponsibilityProf. Dr. EnglmeierQualification TargetsKnowing/Perceiving: Students learn basic concepts and methods of agilesoftware development. Based on their knowledge acquired in the Bachelorcourse project management they better understand how to adopt theconcept of Agility in Project Management. The course addresses inparticular the SCRUM methodology.Applying: The students also learn tools supporting agile projectmanagement.Analyzing/Evaluating: The course applies and reflects traditional projectmanagement tools in the light of agility. This contrasts the two approachesand highlights the differences and the applicability of agility to differentproject settings.Synthesizing: The course trains also the use of Agile Project Managementtools. The students set up a project in teams and manage their fictive work.They are encouraged to link their project management with a project theycomplete in a different course during the same nthesizeBasics xxxAgile LifecyclexxxxxxMeasuringperformancexxxxxxModule description "Applied Computer Science (M.Sc.)"ContentPage 4
Module Contents1. Understanding Agile Values and Principles Agile Methodologies and Frameworks Agile Project Management Model2. Adopting the Agile Approach Initiating an Agile Project Creating Vision and Charting a Project Agile Contracts Agile Documentation3. SCRUM Fundamental Concepts (User Stories, Iteration, Sprints, Backlogs, ) Roles and team development Communication4. Agile Lifecycle Phase models Release planning5. Performance measurementTeaching MethodsLectures (2 hours/week), Exercise (2 hours/week)Requirements forParticipationProgramming skillsLiterature / Multimediabased Teaching MaterialHighsmith, J.: “Agile Project Management: Creating Innovative Products”,2nd Edition, Pearson Education/Addison Wesley Professional.Stenbeck, J.: PMI-ACP and Certified Scrum Professional Exam Prep andDesk Reference.Cohn, M.: “User Stories Applied”, Addison-Wesley, 2004.Online Courses of ACM addressing User Stories und User-Centred DesignApplicabilityMaster Applied Computer ScienceEffort/Total WorkloadTotal 150 hours. Attendance: 60 hours; Self-study: 20 hours; Practical work:70 hoursECTS/ Emphasis of the 5 CP (Emphasis of the Grade for the final Grade 5/120)Grade for the final GradePerformance RecordProject workSemester2nd semesterFrequency of the courseOnce during the academic year (summer semester)DurationOne semesterType of CourseObligatory course from the area software engineeringModule description "Applied Computer Science (M.Sc.)"ContentPage 5
Module NameComputer Graphics 1 (Computergraphik 1)Module ResponsibilityProf. Hartmut Seichter, PhDQualification TargetsTopicsDigital ImagesDisplay Systems3D ModelRepresentationsImage SynthesisMethodsTexturingLighting ModelsShading ModelsApplicationsModule ContentsStudents are able to understand the connection between visual computingtechniques and the underlying mathematical concepts and the physiognomy ofhuman beings, especially the visual system. Students further can distinguish thedifferences between image synthesis methods and related techniques. Studentswill learn basic techniques of real-time 3D visualization and apply them inexercises.KnowUnderstandApply Analyze Assess SynthesizeXXXXXXXxXXXXXXXXXXTeaching MethodsX Literature / Multimediabased Teaching Material Effort / Total WorkloadECTS / Emphasis of theGrade for the final GradePerformance RecordXXXXXXXXXXXComputer graphics is a melting pot of computer science technologies to presentdigital content efficiently to users. Topics in this course: Basic knowledge of the human visual system and perceptualpsychological concept. Image generation and storage CG in professional application and entertainment Display technologies 3D model representations Transformationpipeline: homogenous coordinates and transformations Scenegraphs and realtime rendering APIs Image syntesis methods: Rasterization, Raytracing and beyond. Geometry and Images: samplingmethods and anti-aliasing strategies Texturing, Surfaces and Materials Rendering-Equation and Shadingmodels Lighting models Introduction to scientific and information visualization Graphical User InterfacesLecture (2 SWS), Exercises (2 SWS)Requirements forParticipationApplicabilityXProgramming with OOPBasic knowledge of linear algebraFoley, James D, Andries Van Dam, Steven K Feiner, John F Hughes,and Richard L Phillips. Introduction to Computer Graphics. Vol. 55.Addison-Wesley Reading, 1994. FolienMaster Applied Computer Science, Master Angewandte MedieninformatikTotal 150 hours. Attendance: 60 hours, Self-Study: 45 hours, Exam Preparation:45 hours5 CP (Emphasis of the Grade for the final Grade 5/120)Oral ExamModule description "Applied Computer Science (M.Sc.)"ContentPage 6
Semester1st SemesterFrequency of OccurrenceOnce during the academic year (winter semester)DurationType of CourseOne SemesterObligatory course from the area of software engineeringModule description "Applied Computer Science (M.Sc.)"ContentPage 7
Module NameComputational IntelligenceModule ResponsibilityProf. Dr. Martin GolzQualification TargetsThe students will get the opportunity to- Analyse typical problems of sub-symbolic data and knowledge processing,- Conceive the process chain of adaptive data analytics,- Comprehend and apply methods of the process chain,- Comprehend and apply methods of validation,- Know basic assumptions and models of empirical inference,- Know some of the mathematical background issues.ContentsBasics of statistical inferenceProcess chain of adaptive data analyticsStatistical learning theoryMultivariate regression analysisLinear discriminant analysisKernel function discriminant analysisLinear and non-linear adaptive filteringDeep learningModule ContentsKnow &ComprehendXXXXXXXXApplyAnalyse &EvaluateXXXXXXXXXXXXSynthesise1. Introduction1.1. Five types of statistical inference1.2. Typical applications1.3. Process chain2. Statistical learning theory2.1. Empirical risk minimisation2.2. PAC learning2.3. General learning model2.4. Learning with uniform convergence2.5. Bias complexity trade-off2.6. Vapnik Chervonenkis dimension3. Multivariate, linear regression analysis3.1. Introduction3.2. Model3.3. Principle of maximal a-posteriori probability4. Linear discriminant analysis (LDA)4.1. Introduction4.2. Multi-class LDA4.3. Least squares LDA4.4. Fisher LDA5. Kernel function discriminant analysis5.1. Introduction5.2. Theorem of Cover5.3. Dual representation5.4. Generation of kernel functions5.5. Radial basis function networks5.6. Recursive least squares minimisation5.7. Gaussian processesModule description "Applied Computer Science (M.Sc.)"ContentPage 8
5.8. Applications6. Adaptive Filter6.1. Linear adaptive filtering6.1.1. Least squares algorithm (LS)6.1.2. Recursive LS algorithm (RLS)6.1.3. Extended RLS algorithm (Ex-RLS)6.2. Non-linear adaptive filtering6.2.1. Reproducing kernel Hilbert space (RKHS)6.2.2. Kernel function LS filtering6.3. Applications7. Deep learning7.1. Characterisation7.2. Representation learning7.3. Deep auto-encoder7.4. Restricted Boltzmann machines7.5. ApplicationsTeaching methods- Frontal lectures witho Digital presentation slides,o Demonstration programs- Exercises held in the computer poolo Programming with MATLABo Clarification of open issuesRequirements forParticipationNo formal suppositionsBasic knowledge in linear algebra, analysis, statisticsLiteratureThe following books are recommended:- Nielsen (2015) Neural networks and deep learning. Determination press- Mohri, Rostamizadeh (2012) Foundations of machine learning. MIT press- Bishop (2006) Pattern recognition & machine learning. Springer- Duda, Hart, Stork (2001) Pattern classification. WileyApplicabilityThis module is an obligatory subject.An appropriation to similar majors is possible under stipulation of theirexamination regulations.Effort / Total Workload180 hours, including 60 hours in presence and 120 hours self-instructionECTS / Emphasis of theGrade for the final Grade5 CP (Emphasis of the Grade for the final Grade 5/120)Performance RecordOral examination (30 minutes)Semester2nd SemesterFrequency of OccurrenceOnce a yearDurationOne semesterType of CourseObligatory subjectModule description "Applied Computer Science (M.Sc.)"ContentPage 9
Module NameDistributed Systems (Verteilte Systeme)Module ResponsibilityProf. Dr. Erwin NeuhardtQualification TargetsStudents learn about important architectures which are relied on in thedevelopment of distributed systems. They know about the properties of differentarchitectures. They learn about the different technologies for communicationand cooperation in distributed systems and are able to apply these technologiesin real world rchitecturexxxSocketsxxxxxRPC / le ContentsConcepts and technologies for the development of distributed Systems:- architectures and properties of distributed systems: client serverarchitectures, transparency- Programming concepts for the communication in distributed systems:sockets, remote procedure call, remote method invocation, componentbased distributed systems, message based distributed systems- Concurrent programming: java thread, synchronization andcoordination, concurrent data structures, java executor frameworkTeaching MethodsLecture (2 hours/week), tutorial (2 hours/week)Requirements forParticipationSkills and knowledge in Java programming and software engineering (at least10 ECTS)Literature / Multimediabased Teaching MaterialAndrew S. Tanenbaum, Maarten van Steen, Distributed Systems, Published byMaarten von Steen, 2017Jendrock, E. et al.: The Java EE Tutorial, Enterprise Java Beans, online ondocs.oracle.comw/o author: Sockets, Java Remote Method Invocation, Concurrency, online ondocs.oracle.comBrian Goetz, Joshua Bloch, Joseph Bowbeer, Doug Lea, David Holmes, TimPeierls, Java Concurrency in Practice, Addison-Wesley, 2006David A. Chappell, Richard Monson-Haefel, Java Message Service, O’Reilly2009Master of Applied Computer Science, Master Angewandte MedieninformatikApplicabilityEffort / Total WorkloadECTS / Emphasis of theGrade for the final GradePerformance RecordSemesterTotal 150 hours. Attendance: 60 hours, Self-Study: 60 hours, Exam Preparation30 hours5 ECTS (Emphasis of the Grade for the final Grade 5/120)Written examination on PC1st semesterFrequency of OccurrenceDurationOnce during the academic yearOne semesterModule NameIT SecurityModule description "Applied Computer Science (M.Sc.)"ContentPage 10
Module ResponsibilityProf. Ralf C. Staudemeyer, Ph.D.Qualification TargetsIn this course students will learn how to determine the level of security of acomputer system or service, specify vulnerabilities, and to estimate the potentialdamage resulting from a successful attack. It covers the basic principles andkey concepts for the operation of secure and (mostly) distributed systems, whichincludes partial components from operating systems and computer networks.The focus of this course is to deepen the understanding of network attacks andthe cryptographic techniques to ensure integrity and confidentiality ofinformation. Topics include various sub-components like cryptographic keymanagement, biometrics, authentication in distributed systems, and basicsecurity protocols and standards.Module ContentsThe course starts with a general introduction into IT-Security, Cryptography andPrivacy-Enhancing Technologies. The main focus of this course is oncryptographic algorithms and security protocols. Principally this module treats aselection of the following topics: Selected Attacks (attacks analysis, protection mechanisms) Cryptographic Algorithms (AES, RSA, ECC, MACs, signatures) Cryptographic Key Management (Diffie-Hellman key exchange,certificates, public-key infrastructure) Digital Identity (multi-factor authentication, challenge-responseprotocols, authentication in distributed systems) Mobile Security (mobile networks, Internet-of-Things, SmartCities) Network Security (security protocols, virtual private networks, secureInternet services) User-tools for IT-Security and Privacy in daily practise (email, web, chat,filesystems)This module is under constant development to reflect the most recentdevelopments.Teaching MethodsRequirements forParticipationLiterature / Multimediabased Teaching MaterialLecture (2 hours/week), Exercise (2 hours/week)Decent programming skills and basic knowledge in IT-security Eckert, C. (2018). IT-Sicherheit. Berlin, München, Boston. De Gruyter.Stallings, W. (2016). Cryptography and network security, principles andpractices (7th edition). Prentice Hall.Paar, C., & Pelzl, J. (2010). Understanding Cryptography. Berlin,Heidelberg: Springer Berlin Heidelberg.Schneier, B. (1996), Applied Cryptography, John Wiley & Sons.Hoglund, G, & McGraw , G. (2004). Exploiting Software, how to breakcode, Addison Wesley.Selected sources announced in the lecture.ApplicabilityMaster of Applied Computer ScienceEffort/Total WorkloadTotal 150 hours. Attendance: 60 hours, Self-Study incl. exam preparation: 90h.ECTS/ Emphasis of theGrade for the final GradePerformance Record5 CP (Emphasis of the Grade for the final Grade 5/120) successfully completed exercisesoral exam or written exam ( 14 participants)Module description "Applied Computer Science (M.Sc.)"ContentPage 11
SemesterFrequency of OccurrenceDurationType of Course1st semesterannually (WS)one semesterObligatory course from the area IT-SecurityModule description "Applied Computer Science (M.Sc.)"ContentPage 12
Module NameMobile Systems (Mobile Systeme)Module ResponsibilityProf. Dr. Michael CebullaQualification TargetsStudents learn about substantial concepts and technologies for thedevelopment of smart, mobile applications. One focus area consists in theprogramming with sensor SensoricsxxxxxActivity RecognitionxxxxxxxxxxxxTrack & TraceModule ContentsConcepts and technologies for the development of advanced mobileapplications. Special focus lies on the contextual dependencies of systembehavior and the communication between different components. The followingtopics are examined:- Location-based Services: application of different localization serviceswith different properties, services for the visualization of geographicaldata, management of geographical data, geofencing, location-basedsocial networking (lbsn)- Communication in mobile applications: bluetooth, NFC, http etc.- Acquisition of environmental data using sensoric interfaces- Activity Recognition- Track & Trace-applications: acquisition of position data andenvironmental data, collection and management of data, automatedsituation monitoring and recognitionTeaching MethodsLecture (2 hours/week), excercise (2 hours/week)Requirements forParticipationSkills and Knowledge in Programming with Java and AndroidLiterature /Bill Philips, Chris Stewart, Brian Hardy, Kristin Marsiciano, AndroidMultimediabased TeachingProgramming – The big Nerd Ranch Guide (2nd Edition), Big Nerd Ranch.MaterialThomas Künneth, Android 5 - Apps entwickeln mit dem Android SDK, GalileoPress, Bonn 2012Greg Milette, Adam Stroud, Professional Android Sensor Programming, JohnWiley, Indianapolis 2012ApplicabilityMaster of Applied Computer ScienceEffort/Total WorkloadECTS / Emphasis of theGrade for the final GradPerformance RecordSemester150 hours: 60 hours presence, 45 hours self-study, 45 hours preparation ofexam5 CP (Emphasis of the Grade for the final Grade 5/120)Frequency of OccurrenceOnce a yearWritten exam2nd semesterModule description "Applied Computer Science (M.Sc.)"ContentPage 13
DurationType of CourseOne semesterObligatory course from the area distributed and mobile systemsModule description "Applied Computer Science (M.Sc.)"ContentPage 14
Module NameService-Oriented NetworksModule ResponsibilityProf. Dr.-Ing. Heinz-Peter HöllerQualification TargetsStudents- will get advanced knowledge on the requirements of multimediastreams in networks,- should be able to cor
The following books are recommended: - Nielsen (2015) Neural networks and deep learning. Determination press - Mohri, Rostamizadeh (2012) Foundations of machine learning. MIT press - Bishop (2006)
This handbook supplement applies to students entering the fourth year of their degree in Computer Science, Mathematics & Computer Science or Computer Science . Undergraduate Course Handbook 1.2 Mathematics & Computer Science The Department of Computer Science offers the following joint degrees with the Department of Mathematics: BA .
Bachelor of Science Source : FSG HEA Office. 1. AS750 Master of Science (Biology) 2. AS780 Master of Science 3. AS751 Master of Science (Applied Biology) . AS760 Master of Science (Applied Physics) 13. AS761 Master of Science (Polymer Science & Technology) 14. AS762 Master of Science (Materials Science &Tec
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