CP7017 DATA VISUALIZATION TECHNIQUES

CP7017        DATA VISUALIZATION TECHNIQUES

UNIT I           CORE SKILLS FOR VISUAL ANALYSIS 

Information visualization – effective data analysis – traits of meaningful data – visual perception – making abstract data visible – building blocks of information visualization – analytical interaction – analytical navigation – optimal quantitative scales – reference lines and regions – trellises and crosstabs – multiple concurrent views – focus and context – details on demand – over-plotting reduction – analytical patterns – pattern examples  

UNIT II            TIME-SERIES, RANKING, AND DEVIATION ANALYSIS 

Time-series analysis – time-series patterns – time-series displays – time-series best practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best practices – deviation analysis – deviation analysis displays – deviation analysis best practices  

UNIT III         DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS 

Distribution analysis – describing distributions – distribution patterns – distribution displays – distribution analysis best practices – correlation analysis – describing correlations – correlation patterns – correlation displays – correlation analysis techniques and best practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate analysis techniques and best practices  

UNIT IV        INFORMATION DASHBOARD DESIGN I 

Information dashboard – categorizing dashboards – typical dashboard data – dashboard design issues and best practices – visual perception – limits of short-term memory – visually encoding data – Gestalt principles – principles of visual perception for dashboard design  

UNIT V        INFORMATION DASHBOARD DESIGN II 

Characteristics of dashboards – key goals in visual design process – dashboard display media – designing dashboards for usability – meaningful organization – maintaining consistency – aesthetics of dashboards – testing for usability – case studies: sales dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard  

REFERENCES: 

1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis", Analytics Press, 2009.  

2. Stephen Few, "Information dashboard design: The effective visual communication of data", O'Reilly, 2006. 

3. Edward R. Tufte, "The visual display of quantitative information", Second Edition, Graphics Press, 2001. 

4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013. 

5. Ben Fry, "Visualizing data: Exploring and explaining data with the processing environment", O'Reilly, 2008.  

6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking business intelligence beyond reporting", Wiley, 2010. 

7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley, 2011.

 

IF7202 CLOUD COMPUTING

IF7202       CLOUD COMPUTING

UNIT I   CLOUD ARCHITECTURE AND MODEL

Technologies for Network-Based System – System Models for Distributed and Cloud Computing – NIST Cloud Computing Reference Architecture. Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) – Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management – Computing on demand.  

UNIT II  VIRTUALIZATION

Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization - Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O Devices  - Virtual Clusters and Resource management – Virtualization for Data-center Automation.  

UNIT III CLOUD INFRASTRUCTURE

Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture Development – Design Challenges - Inter Cloud Resource Management – Resource Provisioning and Platform Deployment – Global Exchange of Cloud Resources.  

UNIT IV PROGRAMMING MODEL

Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support - Google App Engine, Amazon AWS -  Cloud Software Environments -Eucalyptus, Open Nebula, OpenStack, Aneka, CloudSim   

UNIT V SECURITY IN THE CLOUD

Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security – Security Governance – Risk Management – Security Monitoring – Security Architecture Design – Data Security – Application Security – Virtual Machine Security - Identity Management and Access Control – Autonomic Security.

REFERENCES: 

1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012. 

2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation, Management, and Security”, CRC Press, 2010. 

3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical Approach”, TMH, 2009. 

4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley India,2011. 

5. George Reese, “Cloud Application Architectures: Building Applications and Infrastructure in the Cloud” O'Reilly 

6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and Processes”, Elsevier/Morgan Kaufmann, 2005. 

7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud Computing – A Business Perspective on Technology and Applications”, Springer. 

8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to Secure Cloud Computing”, Wiley – India, 2010. 

9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”, TMGH,2013. 

10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011 

11. Michael Miller, Cloud Computing, Que Publishing,2008 

12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010 

Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things

CP7016 EMBEDDED SOFTWARE DEVELOPMENT

CP7016       EMBEDDED SOFTWARE DEVELOPMENT

UNIT I       PROCESSORS AND INSTRUCTION SETS

Introduction to embedded computing – overview of embedded system design process – instruction sets of processors: ARM, PIC, TI C55x, TI C64x – programming I/O – modes and exceptions – coprocessors – memory system – CPU performance – CPU power consumption  

UNIT II        EMBEDDED COMPUTING PLATFORM

Basic computing platforms – CPU Bus – memory devices and systems – choosing a platform – development environments – debugging – consumer electronics architecture – platform-level performance analysis – design example: Audio Player  

UNIT III       PROGRAM DESIGN AND ANALYSIS

Components for embedded programs – models of programs – Assembly, linking, and loading – compiler optimizations – program-level performance analysis – performance optimization – program-level energy optimization – optimizing program size – program validation and testing – design example: Digital Still Camera  

UNIT IV      PROCESSES AND OPERATING SYSTEMS

Multiples tasks and multiple processes – multirate systems – pre-emptive RTOS – priority-based scheduling – inter-process communication – evaluating OS performance – processes and power optimization – Case study:  Real-time and embedded Linux – design example: Telephone answering machine  

UNIT V       SYSTEM DESIGN, NETWORKS, AND MULTIPROCESSORS 

 System design methodologies – requirements analysis – specifications – architecture design – quality assurance – distributed embedded systems – shared-memory multiprocessors – design example: Video accelerator 

REFERENCES: 

1. Marilyn Wolf, “Computers as Components: Principles of Embedded Computing Systems Design”, Third Edition, Morgan Kaufmann, 2012. 

2. Christopher Hallinan, “Embedded Linux Primer: A Practical Real-World Approach”, Second Edition, Prentice Hall, 2010. 

3. Karim Yaghmour et al., “Building Embedded Linux Systems”, O’Reilly, 2008. 

4. Arnold S. Berger, “Embedded Systems Design: An Introduction to Processes, Tools, and Techniques”, CMP Books, 2001. 

5. David E. Simon, “An embedded Software Primer”, Addison-Wesley, 1999. 

CP7015 MODEL CHECKING AND PROGRAM VERIFICATION

 CP7015      MODEL CHECKING AND PROGRAM VERIFICATION

UNIT I            AUTOMATA AND TEMPORAL LOGICS

Automata on finite words – model checking regular properties – automata on infinite words – Buchi automata – Linear Temporal Logic (LTL) – automata based LTL model checking – Computational Tree Logic (CTL) – CTL model checking – CTL* model checking  

UNIT II        TIMED AND PROBABILISTIC TREE LOGICS

Timed automata – timed computational tree logic (TCTL) – TCTL model checking – probabilistic systems – probabilistic computational tree logic (PCTL) – PCTL model checking – PCTL* - Markov decision processes  

UNIT III        VERIFYING DETERMINISTIC AND RECURSIVE PROGRAMS

Introduction to program verification – verification of “while” programs – partial and total correctness – verification of recursive programs – case study: binary search – verifying recursive programs with parameters  

UNIT IV       VERIFYING OBJECT-ORIENTED AND PARALLEL PROGRAMS

Partial and total correctness of object-oriented programs – case study: Insertion in linked lists – verification of disjoint parallel programs – verifying programs with shared variables – case study: parallel zero search – verification of synchronization – case study: the mutual exclusion problem 

UNIT V       VERIFYING NON-DETERMINISTIC AND DISTRIBUTED PROGRAMS

Introduction to non-deterministic programs – partial and total correctness of non-deterministic programs – case study: The Welfare Crook Problem – syntax and semantics of distributed programs – verification of distributed programs – case study: A Transmission Problem – introduction to fairness

REFERENCES: 

1. C. Baier, J.-P. Katoen, and K. G. Larsen, “Principles of Model Checking”, MIT Press, 2008. 

2. E. M. Clarke, O. Grumberg, and D. A. Peled, “Model Checking”, MIT Press, 1999. 

3. M. Ben-Ari, “Principles of the SPIN Model Checker”, Springer, 2008. 

4. K. R. Apt, F. S. de Boer, E.-R. Olderog, and A. Pnueli, “Verification of Sequential and Concurrent Programs”, Third Edition, Springer, 2010. 

5. M. Huth and M. Ryan, “Logic in Computer Science --- Modeling and Reasoning about Systems”, Second Edition, Cambridge University Press, 2004. 

6. B. Berard et al., “Systems and Software Verification: Model-checking techniques and tools”, Springer, 2010. 

7. J. B. Almeida, M. J. Frade, J. S. Pinto, and S. M. de Sousa, “Rigorous Software Development: An Introduction to Program Verification”, Springer, 2011. 

CP7014 SOFTWARE ARCHITECTURES

CP7014    SOFTWARE ARCHITECTURES

UNIT I          ARCHITECTURAL DRIVERS

Introduction – Standard Definitions of Software Architecture– Architectural structures – Influence of software architecture on organization – Architecture Business Cycle – Functional requirements – Technical constraints – Quality Attributes – Quality Attribute Workshop (QAW) – Documenting Quality Attributes – Six part scenarios  

UNIT II           ARCHITECTURAL VIEWS AND DOCUMENTATION

Introduction – Standard Definitions for views – Structures and views- Perspectives: Static, dynamic and physical and the accompanying views – Representing views-available notations – Good practices in documentation– Documenting the Views using UML – Merits and Demerits of using visual languages – Need for formal languages -  Architectural Description Languages – ACME  

UNIT III       ARCHITECTURAL STYLES

Introduction – Data flow styles – Call-return styles – Shared Information styles – Event styles – Case studies for each style 

UNIT IV       ARCHITECTURAL DESIGN

Approaches for architectural design – System decomposition – Attributes driven design – Architecting for specific quality attributes – Performance, Availability – Security – Architectural conformance  

UNIT V      ARCHITECTURE EVALUATION AND SOME SPECIAL TOPICS

Need for evaluation – Scenario based evaluation against the drivers – ATAM and its variations – Case studies in architectural evaluations – SOA and Web services – Cloud Computing – Adaptive structures

REFERENCES: 

1. Len Bass, Paul Clements, and Rick Kazman, “Software Architectures Principles and Practices”, 2n Edition, Addison-Wesley, 2003. 

2. Anthony J Lattanze, “Architecting Software Intensive System. A Practitioner's Guide”, Auerbach Publications, 2010. 

3. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little, Paulo Merson, Robert Nord, and Judith Stafford, “Documenting Software Architectures. Views and Beyond”, 2nd Edition, Addison-Wesley, 2010. 

4. Paul Clements, Rick Kazman, and Mark Klein, “Evaluating software architectures: Methods and case studies.”, Addison-Wesley, 2001. 

5. David Garlan and Mary Shaw, “Software architecture: Perspectives on an emerging discipline”, Prentice Hall, 1996. 

6. Rajkumar Buyya, James Broberg, and Andrzej Goscinski, “Cloud Computing. Principles and Paradigms”, John Wiley & Sons, 2011 

7. Mark Hansen, “SOA Using Java Web Services”, Prentice Hall, 2007 

8. David  Garlan, Bradley  Schmerl, and Shang-Wen Cheng, “Software Architecture-Based SelfAdaptation,” 31-56. Mieso K Denko, Laurence Tianruo Yang, and Yan Zang (eds.), “Autonomic Computing and Networking”. Springer Verlag, 2009. 

CP7013 DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS

CP7013      DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS

UNIT I           INTRODUCTION

 Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-SIMD PRAM Computer – Relation between PRAM Models – SIMD Algorithms – MIMD Algorithms – Selection – Desirable Properties for Parallel Algorithms - Parallel Algorithm for Selection – Analysis of Parallel Algorithms.  

UNIT II        SORTING AND SEARCHING

 Merging on the EREW and CREW Models - Fast Merging on EREW - Sorting Networks – Sorting on a Linear Array – Sorting on CRCW, CREW, EREW Models – Searching a Sorted Sequence – Searching a Random Sequence.

UNIT III         ALGEBRAIC PROBLEMS

Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by Matrix Multiplications – Matrix by Vector multiplication.   

UNIT IV        GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS

Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems - Sequential Tree Traversal - Basic Design Principles – Algorithm – Analysis.  

UNITV         DECISION AND OPTIMIZATION PROBLEMS

 Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack Problem- The Bit Complexity of Parallel Computations. 

REFERENCES: 

1. Selim G. Akl, “The Design and Analysis of Parallel Algorithms”, Prentice Hall, New Jersey, 1989. 2. Michael J. Quinn, “Parallel Computing : Theory & Practice”, Tata McGraw Hill Edition, 2003. 

3. Justin R. Smith, “The Design and Analysis of Parallel Algorithms”, Oxford University Press, USA , 1993. 4. Joseph JaJa, “Introduction to Parallel Algorithms”, Addison-Wesley, 1992. 

NE7202 NETWORK AND INFORMATION SECURITY

NE7202         NETWORK AND INFORMATION SECURITY

UNIT I  INTRODUCTION

An Overview of Computer Security-Security Services-Security Mechanisms-Security AttacksAccess Control Matrix, Policy-Security policies, Confidentiality policies, Integrity policies and Hybrid policies.  

UNIT II  CRYPTOSYSTEMS & AUTHENTICATION

Classical Cryptography-Substitution Ciphers-permutation Ciphers-Block Ciphers-DES- Modes of Operation- AES-Linear Cryptanalysis, Differential Cryptanalysis- Hash Function - SHA 512- Message Authentication Codes-HMAC - Authentication Protocols -  

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UNIT III  PUBLIC KEY CRYPTOSYSTEMS 

 Introduction to Public key Cryptography- Number theory- The RSA Cryptosystem and Factoring Integer- Attacks on RSA-The ELGamal  Cryptosystem- Digital Signature Algorithm-Finite FieldsElliptic Curves Cryptography- Key management – Session and Interchange keys, Key exchange and generation-PKI   

UNIT IV  SYSTEM IMPLEMENTATION

Design Principles, Representing Identity, Access Control Mechanisms, Information Flow and Confinement Problem Secure Software Development: Secured Coding - OWASP/SANS Top Vulnerabilities - Buffer Overflows  - Incomplete mediation - XSS - Anti Cross Site Scripting Libraries - Canonical Data Format - Command Injection - Redirection - Inference – Application Controls 

UNIT V  NETWORK SECURITY

Secret Sharing Schemes-Kerberos- Pretty Good Privacy (PGP)-Secure Socket Layer (SSL)Intruders – HIDS- NIDS - Firewalls - Viruses  

REFERENCES: 

1. William Stallings, “Cryptography and Network Security: Principles and Practices”, Third Edition, Pearson Education, 2006. 

2. Matt Bishop ,“Computer Security art and science ”, Second Edition, Pearson Education, 2002 

3. Wade Trappe and Lawrence C. Washington, “Introduction to Cryptography with Coding Theory” Second Edition, Pearson Education, 2007 

4. Jonathan Katz, and Yehuda Lindell, Introduction to Modern Cryptography, CRC Press, 2007 

5. Douglas R. Stinson, “Cryptography Theory and Practice”, Third Edition, Chapman & Hall/CRC, 2006  

6. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education, First Edition, 2006.  7. Network Security and Cryptography, Menezes Bernard, Cengage Learning, New Delhi, 2011 8. Man Young Rhee, Internet Security, Wiley, 2003 9. OWASP top ten security vulnerabilities: http://xml.coverpages.org/OWASP-TopTen.pdf 

CP7012 COMPUTER VISION

CP7012    COMPUTER VISION

UNIT I          IMAGE PROCESSING FOUNDATIONS

Review of image processing techniques – classical filtering operations – thresholding techniques – edge detection techniques – corner and interest point detection – mathematical morphology – texture   

UNIT II          SHAPES AND REGIONS

Binary shape analysis – connectedness – object labeling and counting – size filtering – distance functions – skeletons and thinning – deformable shape analysis – boundary tracking procedures – active contours – shape models and shape recognition – centroidal profiles – handling occlusion – boundary length measures – boundary descriptors – chain codes – Fourier descriptors – region descriptors – moments  

UNIT III         HOUGH TRANSFORM

Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line localization – line fitting – RANSAC for straight line detection – HT based circular object detection – accurate center location – speed problem – ellipse detection – Case study:  Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial matched filtering – GHT for ellipse detection – object location – GHT for feature collation  

UNIT IV        3D VISION AND MOTION

Methods for 3D vision – projection schemes – shape from shading – photometric stereo – shape from texture – shape from focus – active range finding – surface representations – point-based representation – volumetric representations – 3D object recognition – 3D reconstruction – introduction to motion – triangulation – bundle adjustment – translational alignment – parametric motion – spline-based motion – optical flow – layered motion  

UNIT V       APPLICATIONS

Application: Photo album – Face detection – Face recognition – Eigen faces – Active appearance and 3D shape models of faces Application: Surveillance – foreground-background separation – particle filters – Chamfer matching, tracking, and occlusion – combining views from multiple cameras – human gait analysis Application: In-vehicle vision system: locating roadway – road markings – identifying road signs – locating pedestrians  

REFERENCES: 

1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012. 

2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011. 

3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge University Press, 2012. 

4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer Vision”, Third Edition, Academic Press, 2012. 

5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt Publishing, 2012. 

6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for analyzing images”, O'Reilly Media, 2012.   

CP7011 REAL TIME SYSTEMS

CP7011   REAL TIME SYSTEMS

UNIT I             INTRODUCTION

Real-time systems – Applications – Basic Model – Characteristics – Safety and Reliability –  RealTime tasks – Timing Constraints – Modelling Timing Constraints. 

UNIT II           SCHEDULING REAL-TIME TASKS

Concepts – Types of RT Tasks and their Characteristics – Task Scheduling – Clock-Driven Scheduling – Hybrid Schedulers -  Event-Driven Scheduling – EDF Scheduling – RMA – Issues with RMA – Issues in Using RMA in Practical Situations  

  

UNIT III         RESOURCE SHARING  Resource Sharing Protocol – Handling Task Dependencies – Multiprocessor Task Allocation – Dynamic Allocation of Tasks – FaultTolerant Scheduling of Tasks – Clocks in Distributed RT Systems – Centralized and Distributed Clock Synchronization.

  

UNIT IV       COMMERCIAL RT OPERATING SYSTEMS                             9 Time Services – Features of RT OS – Unix as a RT OS – Unix Based RT OS – Windows as a RT OS – POSIX – Survey of RTOS: PSOS – VRTX – VxWorksAMONG RT TASKS & SCHEDULING RT TASKS

Resource Sharing Among RT Tasks – Priority Inversion – PIP – HLP – PCP – Types of Priority Inversions Under PCP – Features of PCP – Issues in using – QNX - µC/OS-II – RT Linux – Lynx – Windows CE – Benching RT Systems.  

UNIT  V       RT COMMUNICATION & DATABASES

Examples of Applications Requiring RT Communication – Basic Concepts – RT Communication in a LAN – Soft & Hard RT Communication in a LAN – Bounded Access Protocols for LANs – Performance Comparison – RT Communication Over Packet Switched Networks – QoS Framework – Routing – Resource Reservation – Rate Control – QoS Models - Examples Applications of RT Databases – RT Databases – Characteristics of Temporal Data – Concurrency Control in RT Databases – Commercial RT Databases.

REFERENCES: 

1. Rajib Mall, "Real-Time Systems: Theory and Practice," Pearson, 2008. 

2. Jane W. Liu, "Real-Time Systems" Pearson Education, 2001. 

3. Krishna and Shin, "Real-Time Systems," Tata McGraw Hill. 1999.  

4. Alan C. Shaw, “Real-Time Systems and Software”, Wiley, 2001. 

5. Philip Laplante, “Real-Time Systems Design and Analysis”, 2nd Edition, Prentice Hall of India. 

6. Resource Management in Real-time Systems and Networks, C. Siva Ram Murthy and G. Manimaran, MIT Press, March 2001. 

CP7010 CONCURRENCY MODELS

CP7010     CONCURRENCY MODELS

UNIT I             FSP AND GRAPH MODELS

Concurrency and issues in concurrency – models of concurrency – graphical models – FSP & LTSA – modeling processes with FSP – concurrency models with FSP – shared action – structure diagrams – issues with shared objects – modeling mutual exclusion – conditional synchronization – modeling semaphores – nested monitors – monitor invariants  

UNIT II           SAFETY AND LIVENESS PROPERTIES

Deadlocks – deadlock analysis in models – dining philosophers problem – safety properties – single-lane bridge problem – liveness properties – liveness of the single-lane bridge – readerswriters problem – message passing – asynchronous message passing models – synchronous message passing models – rendezvous  

UNIT III         CONCURRENCY ARCHITECTURES AND DESIGN

Modeling dynamic systems – modeling timed systems – concurrent architectures – Filter pipeline – Supervisor-worker model – announcer-listener model – model-based design – from requirements to models – from models to implementations – implementing concurrency in Java – program verification  

UNIT IV          LINEAR TEMPORAL LOGIC (LTL) 

Syntax of LTL – semantics of LTL – practical LTL patterns – equivalences between LTL statements – specification using LTL – LTL and FSP – Fluent proposition – Temporal propositions – Fluent Linear Temporal Logic (FLTL) – FLTL assertions in FSP – Database ring problem  

UNIT V          PETRI NETS

 Introduction to Petri nets – examples – place-transition nets – graphical and linear algebraic representations – concurrency & conflict – coverability graphs – decision procedures – liveness – colored Petri nets (CPN) – modeling & verification using CPN – non-hierarchical CPN – modeling protocols – hierarchical CPN – timed CPN – applications of Petri Nets  

REFERENCES: 

1. Jeff Magee & Jeff Kramer, “Concurrency: State Models and Java Programs”, Second Edition, John Wiley, 2006. 

2. M. Huth & M. Ryan, “Logic in Computer Science – Modeling and Reasoning about Systems”, Second Edition, Cambridge University Press, 2004. 

3. B. Goetz, T. Peierls, J. Bloch, J. Bowbeer, D. Holmes, and D. Lea, “Java Concurrency in Practice”, Addison-Wesley Professional, 2006. 4. Wolfgang Reisig, “Petri Nets: An Introduction”, Springer, 2011. 5. K. Jensen and L. M. Kristensen, “Colored Petri Nets: Modeling and Validation of Concurrent Systems”, Springer, 2009. 6. Wolfgang Reisig, “Understanding Petri Nets: Modeling Techniques, Analysis Methods, Case Studies”, Springer, 2013.

CP7009 MACHINE LEARNING TECHNIQUES

CP7009      MACHINE LEARNING TECHNIQUES

UNIT I        FOUNDATIONS OF LEARNING

Components of learning – learning models – geometric models – probabilistic models – logic models – grouping and grading – learning versus design – types of learning – supervised – unsupervised – reinforcement – theory of learning – feasibility of learning – error and noise – training versus testing – theory of generalization – generalization bound – approximationgeneralization tradeoff – bias and variance – learning curve      

UNIT II           LINEAR MODELS

Linear classification – univariate linear regression – multivariate linear regression – regularized regression – Logistic regression – perceptrons – multilayer neural networks – learning neural networks structures – support vector machines – soft margin SVM – going beyond linearity – generalization and overfitting – regularization – validation 

UNIT III         DISTANCE-BASED MODELS

Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical clustering – k-d trees – locality sensitive hashing – non-parametric regression – ensemble learning – bagging and random forests – boosting – meta learning  

UNIT IV      TREE AND RULE MODELS

Decision trees – learning decision trees – ranking and probability estimation trees – regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists – descriptive rule learning – association rule mining – first-order rule learning  

UNIT V        REINFORCEMENT LEARNING

Passive reinforcement learning – direct utility estimation – adaptive dynamic programming – temporal-difference learning – active reinforcement learning – exploration – learning an actionutility function – Generalization in reinforcement learning – policy search – applications in game playing – applications in robot control    

REFERENCES:  

1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook Publishers, 2012. 

2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of data”, Cambridge University Press, 2012. 

3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012. 

4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007. 

5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press, 2012. 

6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning”, MIT Press, 2012. 

7. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997. 

8. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition, Prentice Hall, 2009. 

CP7008 SPEECH PROCESSING AND SYNTHESIS

CP7008        SPEECH PROCESSING AND SYNTHESIS

UNIT I             FUNDAMENTALS OF SPEECH PROCESSING

Introduction – Spoken Language Structure – Phonetics and Phonology – Syllables and Words – Syntax and Semantics – Probability, Statistics and Information Theory – Probability Theory – Estimation Theory – Significance Testing – Information Theory.   

UNIT II         SPEECH SIGNAL REPRESENTATIONS AND CODING

Overview of Digital Signal Processing – Speech Signal Representations – Short time Fourier Analysis – Acoustic Model of Speech Production – Linear Predictive Coding – Cepstral Processing – Formant Frequencies – The Role of Pitch – Speech Coding – LPC Coder.  

UNITIII         SPEECH RECOGNITION

Hidden Markov Models – Definition – Continuous and Discontinuous HMMs – Practical Issues – Limitations. Acoustic Modeling – Variability in the Speech Signal – Extracting Features – Phonetic Modeling – Adaptive Techniques – Confidence Measures – Other Techniques. 

UNITIV          TEXT ANALYSIS

Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis – Homograph Disambiguation – Morphological Analysis – Letter-to-sound Conversion – Prosody – Generation schematic – Speaking Style – Symbolic Prosody – Duration Assignment – Pitch Generation    

UNIT V         SPEECH SYNTHESIS 

Attributes – Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic Modification of Speech – Source-filter Models for Prosody Modification – Evaluation of TTS Systems.

   

REFERENCES: 

1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001. 

2. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education, 2002. 

3. Lawrence Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”, Prentice Hall Signal Processing Series, 1993. 

4. Sadaoki Furui, “Digital Speech Processing: Synthesis, and Recognition, Second Edition, (Signal Processing and Communications)”, Marcel Dekker, 2000. 

5. Joseph Mariani, “Language and Speech Processing”, Wiley, 2009.  

CP7007 SOFTWARE REQUIREMENTS ENGINEERING

CP7007        SOFTWARE REQUIREMENTS ENGINEERING

UNIT I         DOMAIN UNDERSTANDING

Introduction  – Types of requirements – Requirements engineering process – Validating requirements – Requirements and design – Requirements and test cases – introduction to business domain – Problem analysis  – Fish bone diagram – Business requirements – Business process modeling – Business use cases – Business modeling notations – UML Activity diagrams. 

  

UNIT II       REQUIREMENTS ELICITATION

Introduction – Understanding stakeholders' needs – Elicitation techniques – interviews, questionnaire, workshop, brainstorming, prototyping – Documenting stakeholders' needs    

UNIT III          FUNCTIONAL REQUIREMENTS

Introduction – Features and Use cases – Use case scenarios – Documenting use cases – Levels of details – SRS documents. 

UNIT IV          QUALITY ATTRIBUTES AND USER EXPERIENCE 

Quality of  solution – Quality attributes – Eliciting quality attributes – Quality attribute workshop (QAW) – Documenting quality attributes – Six part scenarios – Usability requirements – Eliciting and documenting usability requirements – Modeling user experience – Specifying UI design  

UNIT V           MANAGING REQUIREMENTS

Defining scope of the project – Context diagram – Managing requirements – Requirements properties – Traceability – Managing changes – Requirements metrics – Requirements management tools.  

REFERENCES: 

1. Axel van Lamsweerde, "Requirements Engineering", Wiley, 2009 

2. Gerald Kotonya, Ian Sommerville, "Requirements Engineering: Processes and Techniques", John Wiley and Sons, 1998 

3. Dean Leffingwell and Don Widrig, “Managing Software Requirements: A Use Case Approach (2nd Edition) ”, Addison-wesley, 2003 

4. SEI Report, “Quality Attributes Workshop”, http://www.sei.cmu.edu/library/abstracts/reports/03tr016.cfm , 2003 

5. J Nielsen, “Usability Engineering”, Academic Press, 1993   

CP7006 PARALLEL PROGRAMMING PARADIGMS

CP7006     PARALLEL PROGRAMMING PARADIGMS

UNIT I   FOUNDATIONS OF PARALLEL PROGRAMMING

Motivation for parallel programming - Concurrency in computing – basics of processes, multiprocessing, and threads – cache – cache mappings – caches and programs – virtual memory – instruction level parallelism – hardware multi-threading – SIMD – MIMD – interconnection networks – cache coherence – shared-memory model – issues in shared-memory model – distributed-memory model – issues in distributed-memory model – hybrid model – I/O – performance of parallel programs – parallel program design 

UNIT II         MESSAGE PASSING PARADIGM

Basic MPI programming – MPI_Init and MPI_Finalize – MPI communicators – SPMD programs – message passing – MPI_Send and MPI_Recv – message matching – MPI I/O – parallel I/O – collective communication – MPI_Reduce – MPI_Allreduce – broadcast – scatter – gather – allgather – derived types – remote memory access – dynamic process management – MPI for grids – performance evaluation of MPI programs  

UNIT III       SHARED MEMORY PARADIGM: PTHREADS

Basics of Pthreads – thread synchronization – critical sections – busy-waiting – mutexes – semaphores – barriers and condition variables – read-write locks – Caches, cache coherence and false sharing – thread safety – Pthreads case study   

UNIT IV       SHARED MEMORY PARADIGM: OPENMP

Basic OpenMP constructs – scope of variabls – reduction clause – parallel for directive – loops in OpenMP – scheduling loops – synchronization in OpenMP – Case Study:  Producer-Consumer problem – cache issues – threads safety in OpenMP – OpenMP best practices  

 34

UNIT V      GRAPHICAL PROCESSING PARADIGMS: OPENCL AND CUDA                9 Introduction to CUDA – CUDA programming examples – CUDA execution model – CUDA memory hierarchy – CUDA case study - introduction to OpenCL – OpenCL programming examples – Programs and Kernels – Buffers and Images – Event model – OpenCL case study  

REFERENCES: 

1. Peter S. Pacheco, “An introduction to parallel programming”, Morgan Kaufmann, 2011. 

2. M. J. Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill, 2003. 

3. W. Gropp, E. Lusk, and R. Thakur, “Using MPI-2: Advanced features of the message passing interface”, MIT Press, 1999. 

4. W. Gropp, E. Lusk, and A. Skjellum, “Using MPI:  Portable parallel programming with the message passing interface”, Second Edition, MIT Press, 1999. 

5. B. Chapman, G. Jost, and Ruud van der Pas, “Using OpenMP”, MIT Press, 2008. 

6. D. R. Butenhof, “Programming with POSIX Threads”, Addison Wesley, 1997. 

7. B. Lewis and D. J. Berg, “Multithreaded programming with Pthreads”, Sun Microsystems Press, 1998. 

8. A. Munshi, B. Gaster, T. G. Mattson, J. Fung, and D. Ginsburg, “OpenCL programming guide”, Addison Wesley, 2011. 

9. Rob Farber, “CUDA application design and development”, Morgan Haufmann, 2011. 

NE7002 MOBILE AND PERVASIVE COMPUTING

NE7002      MOBILE AND PERVASIVE COMPUTING

UNIT I    INTRODUCTION

Differences between Mobile Communication and Mobile Computing – Contexts and Names – Functions – Applications and Services – New Applications – Making Legacy  Applications Mobile Enabled – Design Considerations –  Integration of Wireless and Wired Networks – Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive Computing – Categories of Pervasive Devices   

UNIT II  3G AND 4G CELLULAR NETWORKS

Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment – Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack – CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion – 4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane – PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP 

  

UNIT III           SENSOR AND MESH NETWORKS

Sensor Networks – Role in Pervasive Computing – In Network Processing and Data Dissemination – Sensor Databases – Data Management in Wireless Mobile Environments – Wireless  Mesh Networks – Architecture – Mesh Routers – Mesh Clients – Routing – Cross Layer Approach – Security Aspects of Various Layers in WMN – Applications of Sensor and Mesh networks   

UNIT IV  CONTEXT AWARE COMPUTING   & WEARABLE COMPUTING

Adaptability – Mechanisms for Adaptation -  Functionality and Data – Transcoding – Location Aware Computing – Location Representation – Localization Techniques – Triangulation and Scene Analysis – Delaunay Triangulation and Voronoi graphs – Types of Context – Role of Mobile Middleware – Adaptation and Agents – Service Discovery Middleware Health BAN- Medical and Technological Requirements-Wearable Sensors-Intra-BAN communications  

UNIT V            APPLICATION DEVELOPMENT

Three tier architecture  - Model View Controller Architecture - Memory Management – Information Access Devices – PDAs and Smart Phones – Smart Cards and Embedded Controls – J2ME – Programming for CLDC – GUI in MIDP – Application Development ON Android and iPhone   

REFERENCES: 

1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology, Applications and Service Creation”, 2nd ed, Tata McGraw Hill, 2010. 

2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010. 

3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’, Morgan Kaufmann Publishers, 2006. 

4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005 

5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile Internet Applications’, Pearson Education, 2003 

6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann Publishers, 2004 

7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003 

8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010. 

9. Mohammad s. Obaidat et al, “Pervasive Computing and Networking”,John wiley 

10. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”, Wiley, 2009 

11. Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert “Fundamentals of Mobile and Pervasive Computing, “, McGraw-Hill, 2005 

  

CP7005 RANDOMIZED ALGORITHMS

CP7005    RANDOMIZED ALGORITHMS

UNIT I            INTRODUCTION TO RANDOMIZED ALGORITHMS

Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models of Randomized Algorithms – Classification of Randomized Algorithms – Paradigms of the Design of Randomized Algorithms - Game Theoretic Techniques – Game Tree Evaluation – Minimax Principle – Randomness and Non Uniformity.   

UNIT II           PROBABILISTIC METHODS

Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities – Randomized Selection – Two Point Sampling – The Stable Marriage Problem – The Probabilistic Method – Maximum Satisfiability – Expanding Graphs – Method of Conditional Probabilities – Markov Chains and Random Walks – 2-SAT Example – Random Walks on Graphs – Random Connectivity.  

UNIT III        ALGEBRAIC TECHNIQUES AND APPLICATIONS

Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs – Pattern Matching – Verification of Matrix Multiplication - Data Structuring Problems – Random Treaps – Skip Lists – Hash Tables.  

UNIT IV       GEOMETRIC AND GRAPH ALGORITHMS

Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal Decompositions – Linear Programming – Graph Algorithms – Min-cut – Minimum Spanning Trees.  

UNIT V       HASHING AND ONLINE ALGORITHMS

Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms - Online Paging – Adversary Models – Relating the Adversaries – The k-server Problem

REFERENCES: 

1. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge University Press, 1995. 

2. Juraj Hromkovic,”Design and Analysis of Randomized Algorithms”, Springer, 2010. 

3. Michael Mitzenmacher and Eli Upfal, “Probabilty and Computing – Randomized Algorithms and Probabilistic Analysis”, Cambridge University Press, 2005. 

NE7001 SENSING TECHNIQUES AND SENSORS

NE7001       SENSING TECHNIQUES AND SENSORS

UNIT I          PRINCIPLES OF SENSING

Data Acquisition – sensor characteristics – electric charges, fields, potentials – capacitance – magnetism – inductance – resistance – piezoelectric – pyroelectric – Hall effect – thermoelectric effects – sound waves – heat transfer – light – dynamic models of sensors

  

UNIT II        OPTICAL COMPONENTS AND INTERFACE ELECTRONICS

Radiometry – Photometry – mirrors – lenses – fibre optics – concentrators – Interface circuits – amplifiers – light-to-voltage – excitation circuits – ADC – Digitization – Capacitance-to-voltage – bridge circuits – data transmission – noise in sensors and circuits – calibration – low power sensors  

UNIT III         MOTION RELATED SENSORS

Occupancy and motion detectors: ultrasonic – microwave – capacitive detectors – triboelectric – optoelectronic motion sensors – optical presence sensor – Pressure Gradient sensorsVelocity and acceleration sensors: Accelerometer characteristics – capacitative accelerometers – piezoelectric accelerometers – piezoresistive accelerometers – thermal accelerometers – Gyroscopes – piezoelectric cables – gravitational sensors  

UNIT IV       LIGHT AND RADIATION DETECTORS

Light Detectors: Photo diodes – photo transistor – photo resistor – cooled detectors – CCD and CMOS image sensors – thermal detectors – optical design – gas flame detectors Radiation Detectors: scintillating detectors – ionization detectors – cloud and bubble chambers  

UNIT V       TEMPERATURE AND CHEMICAL SENSORS

Temperature Sensors: coupling with objects – temperature reference points – thermo resistive sensors – thermo electric contact sensors – semiconductor sensors – acoustic sensors – piezoelectric sensors Chemical sensors: characteristics – classes of chemical sensors – biochemical sensors – multi-sensor arrays – electronic noses and tongues   

REFERENCE: 

1. Jacob Fraden, “Handbook of Modern Sensors: Physics, Designs, and Applications”, Fourth Edition, Springer, 2010.

CP7004 IMAGE PROCESSING AND ANALYSIS

CP7004    IMAGE PROCESSING AND ANALYSIS

UNIT I         SPATIAL DOMAIN PROCESSING

Introduction to image processing – imaging modalities – image file formats – image sensing and acquisition – image sampling and quantization – noise models – spatial filtering operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial filtering – spatial filters for noise removal  

UNIT II       FREQUENCY DOMAIN PROCESSING

Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT), and Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing – image sharpening – selective filtering – frequency domain noise filters – wavelets – Haar Transform – multiresolution expansions – wavelet transforms – wavelets based image processing  

  

UNIT III           SEGMENTATION AND EDGE DETECTION 

Thresholding techniques – region growing methods – region splitting and merging – adaptive thresholding – threshold selection – global valley – histogram concavity – edge detection – template matching – gradient operators – circular operators – differential edge operators – hysteresis thresholding – Canny operator – Laplacian operator – active contours – object segmentation  

UNIT IV           INTEREST POINTS, MORPHOLOGY, AND TEXTURE

Corner and interest point detection – template matching – second order derivatives – median filter based detection – Harris interest point operator – corner orientation – local invariant feature detectors and descriptors – morphology – dilation and erosion – morphological operators – grayscale morphology – noise and morphology – texture – texture analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter approach  

UNIT V           COLOR IMAGES AND IMAGE COMPRESSION

Color models – pseudo colors – full-color image processing – color transformations – smoothing and sharpening of color images – image segmentation based on color – noise in color images. Image Compression – redundancy in images – coding redundancy – irrelevant information in images – image compression models – basic compression methods – digital image watermarking. 

REFERENCES: 

1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012. 

2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction using Java”, Springer, 2008. 

3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011. 

4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson, 2008. 

5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for Computer Vision”, Third Edition, Academic Press, 2012. 

6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”, Packt Publishing, 2012. 

7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms for analyzing images”, O'Reilly Media, 2012.

CP7003 DATA ANALYSIS AND BUSINESS INTELLIGENCE

CP7003     DATA ANALYSIS AND BUSINESS INTELLIGENCE

UNIT I            LINEAR REGRESSION

Introduction to data analysis – Statistical processes – statistical models – statistical inference – review of random variables and probability distributions – linear regression – one predictor – multiple predictors – prediction and validation – linear transformations – centering and standardizing – correlation – logarithmic transformations – other transformations – building regression models – fitting a series of regressions    

UNIT II        LOGISTIC AND GENERALIZED LINEAR MODELS 

Logistic regression – logistic regression coefficients – latent-data formulation – building a logistic regression model – logistic regression with interactions – evaluating, checking, and comparing fitted logistic regressions – identifiability and separation – Poisson regression – logistic-binomial model – Probit regression – multinomial regression – robust regression using t model – building complex generalized linear models – constructive choice models    

UNIT III      SIMULATION AND CAUSAL INFERENCE                                                                   9 Simulation of probability models – summarizing linear regressions – simulation of non-linear predictions – predictive simulation for generalized linear models – fake-data simulation – simulating and comparing to actual data – predictive simulation to check the fit of a time-series model – causal inference – randomized experiments – observational studies – causal inference using advanced models – matching – instrumental variables    

UNIT IV       MULTILEVEL REGRESSION

Multilevel structures – clustered data – multilevel linear models – partial pooling – group-level predictors – model building and statistical significance – varying intercepts and slopes – scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression – multi-level generalized linear models    

UNIT V       DATA COLLECTION AND MODEL UNDERSTANDING

Design of data collection – classical power calculations – multilevel power calculations – power calculation using fake-data simulation – understanding and summarizing fitted models – uncertainty and variability – variances – R2 and explained variance – multiple comparisons and statistical significance – analysis of variance – ANOVA and multilevel linear and general linear models – missing data imputation

REFERENCES: 

1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and multilevel/Hierarchical Models", Cambridge University Press, 2006. 

2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.  

3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012. 

4. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second Edition, Oxford University Press, 2006.  

5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and data mining applications", Academic Press, 2009.  

6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013. 

7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An Examplebased Approach", Third Edition, Cambridge University Press, 2010.  

8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer, 2011. 

CP7002 PROBABILISTIC REASONING SYSTEMS

CP7002    PROBABILISTIC REASONING SYSTEMS

UNIT I           REPRESENTATION

Probability Theory, Graphs, Bayesian network representation: Bayes networks, Independence in graphs – Undirected graphical models: Parameterization, Markov Network independencies –   Conditional Bayesian networks.

   

UNIT II        TEMPLATE BASED REPRESENTATION

Temporal models (Dynamic Bayesian networks , Hidden Markov Models) – Directed probabilistic models for object-relational domains – Inference in temporal models:  Kalman filters.  

UNIT III        INFERENCE

Exact inference: Variable elimination – Exact inference: Clique trees (Junction trees) – Approximate inference: Forward sampling, Importance sampling, MCMC –  MAP inference: Variable elimination for MAP, Max-product in clique trees. 

UNIT IV       LEARNING

Learning graphical models – Parameter estimation: maximum-likelihood estimation, MLE for Bayesian networks, Bayesian parameter estimation – Structure learning in Bayesian networks: Constraint based, structure scores, structure search – Partially observed data: Parameter estimation, Learning models with hidden variables – Learning undirected models: Maximum likelihood 

UNIT V      ACTIONS AND DECISIONS

Causality – Utilities and decisions – Structured decision problems  

REFERENCES: 

1. Daphne Koller and Nir Friedman, “Probabilistic Graphical Models: Principles and Techniques”, MIT Press, 2009. 

2. David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press, 2012. 

3. Adnan Darwiche, “Modeling and Reasoning with Bayesian networks”, Cambridge University Press, 2009. 

4. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012. 

5. Stuart Russel and Peter Norvig, “Artificial Intelligence:  A Modern Approach”, Third Edition, Prentice Hall, 2009.