Introduction: Introduction to soft computing, application areas of soft computing, classification of soft computing techniques, structure & functioning of biological brain & Neuron, and concept of learning/training. Model of an Artificial Neuron, transfer/activation functions, perceptron, perceptron learning model, binary & continuous inputs, linear separability.
Multilayer Neural Networks: Feed Forward network - significance, training, loss function, Back-Propagation algorithm, convergence & generalization, momentum, applications. Feedback network -Hopfield Nets: architecture, energy functions, training algorithms & examples, competitive learning, self-organizing maps. Introduction to CNN and RNN network.
Fuzzy Systems: fuzzy set theory, fuzzy sets and operations, membership functions, concept of fuzzy relations and their composition, concept of fuzzy Measures. Fuzzy logic: fuzzy rules, inferencing. Fuzzy Control system: selection of membership functions, Fuzzyfication, rule based design & inferencing, defuzzyfication, applications of fuzzy system.
Genetic algorithm: concepts, creation of offspring, working principle, encoding, fitness functions, reproduction, genetic modeling. Generation cycle & convergence of GA, application areas of GA.
Advanced soft computing techniques: Rough Set Theory - Introduction, Set approximation, Rough membership, Attributes, optimization. SVM - Introduction, obtaining the optimal hyper plane, linear and nonlinear SVM classifiers. Introduction to Swarm Intelligence, Swarm Intelligence Techniques: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization etc.
S.N. Sivanandam & S.N. Deepa, Principles of Soft Computing, Wiley Publications
S, Rajasekaran & G.A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic & Genetic Algorithms, Synthesis & applications, PHI Publication
Bose, Neural Network fundamental with Graph , Algo.& Appl, TMH Kosko: Neural Network & Fuzzy System, PHI Publication
Klir & Yuan ,Fuzzy sets & Fuzzy Logic: Theory & Appli.,PHI Pub. Hagen, Neural Network Design, Cengage Learning
Installation and configuration of Hadoop/Euceliptus etc.
Service deployment & Usage over cloud.
Management of cloud resources.
Using existing cloud characteristics & Service models .
Cloud Security Management. 6. Performance evaluation of services over cloud .
Buyya, Selvi ,” Mastering Cloud Computing “,TMH Pub
Kumar Saurabh, “Cloud Computing” , Wiley Pub
Krutz , Vines, “Cloud Security “ , Wiley Pub
Velte, “Cloud Computing- A Practical Approach” ,TMH Pub
Sosinsky, “ Cloud Computing” , Wiley Pub
INTRODUCTION
Machine learning basics: What is Machine Learning, Types and Applications of ML, , Tools used, AI vs ML .Introduction to Neural Networks.
Introduction to linear regression: SSE; gradient descent; closed form; normal equations; features, Introduction to classification: Classification problems; decision boundaries; nearest neighbor methods.
Linear regression; SSE; gradient descent; closed form; normal equations; features Overfitting and complexity; training, validation, test data, and introduction to Matlab (II)
UNIT-II
SUPERVISED LEARNING:
Introduction to Supervised Learning, Supervised learning setup, LMS, Linear Methods for Classification, Linear Methods for Regression, Support Vector Machines. Basis Expansions, Model Selection Procedures
Perceptron, Exponential family, Generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines, Model selection and feature selection, Decision Tree, Ensemble methods: Bagging, boosting, Evaluating and debugging learning algorithms. Classification problems; decision boundaries; nearest neighbor methods, Probability and classification, Bayes optimal decisions Naive Bayes and Gaussian class- conditional distribution,
Linear classifiers Bayes' Rule and Naive Bayes Model, Logistic regression, online gradient descent, Neural Networks Decision tree and Review for Mid-term, Ensemble methods: Bagging, random forests, boosting A more detailed discussion on Decision Tree and Boosting
UNIT-III
REINFORCEMENT LEARNING: Markov decision process (MDP), HMM, Bellman equations, Value iteration and policy iteration, Linear quadratic regulation, Linear Quadratic Gaussian, Q-learning, Value function approximation, Policy search, Reinforce, POMDPs.
UNIT-IV
UNSUPERVISED LEARNING:
Introduction to Unsupervised Learning : Association Rules, Cluster Analysis, Reinforcement Learning,Clustering K-means, EM. Mixture of Gaussians, Factor analysis, PCA (Principal components analysis), ICA (Independent components analysis);, hierarchical agglomeration Advanced discussion on clustering and EM, Latent space methods; PCA, Text representations; naive Bayes and multinomial models; clustering and latent space models, VC-dimension, structural risk minimization; margin methods and support vector machines (SVM), Support vector machines and large-margin classifiers Time series; Markov models; autoregressive models
UNIT-V
DIMENSIONALITY REDUCTION: Feature Extraction , Singular value decomposition. Feature selection – feature ranking and subset selection, filter, wrapper and embedded methods. Machine Learning for Big data: Big Data and MapReduce, Introduction to Real World ML, Choosing an Algorithm, Design and Analysis of ML Experiments, Common Software for ML
Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and
Machine Learning), The MIT Press 2004.
Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
Berson: Data Warehousing & Data Mining &OLAP , TMH
Jiawei Han and Micheline Kamber, Data Mining Concepts & Techniques, Elsevier Pub.
Arun.K.Pujari, Data Mining Techniques, University Press.
N.P Gopalan: Data Mining Technique & Trend, PHI
Hand, Mannila & Smith: Principle of Data Mining, PHI
Tan, Introduction to Data Mining, Pearson Pub.
P. Pal Chandhari , “Computer Organization and design” Prentice Hall of India Pvt. Ltd, 1994
Del Corso, H.Kirrman, JD Nicond “Microcomputer buses & links” Academic Press 1986.
Douglas V Hall “Microprocessor & Interfacing Programming & H/W” McGraw Hill International 2nd Edition 1992.
Scott Muller, “Upgrading and repairing PC”
Introduction Computers and its Impact in Society, Overview of Computer and Web Technology, Need for Cyber Law, Cyber Jurisprudence at International and Indian Level, Cyber Law - International Perspectives UN & International Telecommunication Union (ITU) Initiatives Council of Europe - Budapest Convention on Cybercrime, Asia-Pacific Economic Cooperation (APEC), Organization for Economic Co-operation and Development (OECD), World Bank, Commonwealth of Nations.
Constitutional & Human Rights Issues in Cyberspace Freedom of Speech and Expression in Cyberspace, Right to Access Cyberspace – Access to Internet, Right to Privacy, Right to Data Protection, Cyber Crimes & Legal Framework Cyber Crimes against Individuals, Institution and State, Hacking, Digital Forgery, Cyber Stalking/Harassment, Cyber Pornography, Identity Theft & Fraud Cyber terrorism, Cyber Defamation.
Cyber Torts Cyber Defamation, Different Types of Civil Wrongs under the IT Act 2000, Intellectual Property Issues in Cyber Space Interface with Copyright Law, Interface with Patent Law, Trademarks & Domain Names Related issues
E-Commerce Concept, E-commerce-Salient Features, Online approaches like B2B, B2C & C2C Online contracts, Click Wrap Contracts, Applicability of Indian Contract Act, 1872,
Dispute Resolution in Cyberspace, Concept of Jurisdiction, Indian Context of Jurisdiction and IT Act, 2000. International Law and Jurisdictional Issues in Cyberspace, Dispute Resolutions .
Chris Reed & John Angel, Computer Law, OUP, New York.
Justice Yatindra Singh, Cyber Laws, Universal Law Publishing Co, New Delhi.
Verma S, K, Mittal Raman, Legal Dimensions of Cyber Space, Indian Law Institute.
Jonthan Rosenoer, Cyber Law, Springer, New York.
Sudhir Naib, The Information Technology Act, 2005: A Handbook, OUP, New York.
S. R. Bhansali, Information Technology Act, 2000, University Book House Pvt. Ltd.
Introduction of Virtual Reality: Fundamental Concept and Components of Virtual Reality. Primary Features and Present Development on Virtual Reality. Multiple Modals of Input and Output Interface in Virtual Reality: Input -Tracker, Sensor, Digital Glove, Movement Capture, Video-based Input, 3D Menus & 3DScanner etc. Output -- Visual / Auditory / Haptic Devices.
Environment Modeling in Virtual Reality: Geometric Modeling, Behavior Simulation, Physically Based Simulation, Interactive Techniques in Virtual Reality: Body Track, Hand Gesture, 3D Manus, Object Grasp
Burdea, G. C. and P. Coffet. Virtual Reality Technology, Second Edition. Wiley- IEEE Press, 2003/2006.
Sherman, William R. and Alan B. Craig. Understanding Virtual Reality – Interface, Application, and Design, Morgan Kaufmann, 2002.
Fei GAO. Design and Development of Virtual Reality Application System, Tsinghua Press, March 2012
Review of Networking and O.S. fundamentals, ISO-OSI Model, different layers and their functions, LAN, MAN, WAN, Communication media & principles IEEE standards etc.
Internetworking with TCP/IP, Basic concepts, Principles, Protocols and Architecture, Address handling Internet protocols and protocol layering. DNS, Applications: TELNET, RLOGN , FTP, TFTP, NFS, SMTP, POPL, IMAP, MIME, HTTP,STTP,DHCP, VOIP, SNMP.
Introduction to Router, Configuring a Router, Interior & Exterior Routing, RIP, Distance Vector Routing, OSPF, BGP, Uni-cast, Multicast and Broadcast. Multicast routing protocols: DVMRP, MOSPF, CBT, PIM, MBONE, EIGRP, CIDR, Multicast Trees, Comparative study of IPv6 and IPv4.
VPN addressing and routing, VPN Host management, ATM Concepts, Services Architecture, Equipments and Implementation
Introduction to wireless transmission and medium access control, wireless LAN: IEEE 802.11, Hipher LAN , Bluetooth Mobile Network and Transport layer, WAP GSM and CDMA: Network architecture and management
Computer Networks: Tanenbaum.
Internetworking with TCP/IP: Comer.
Data Communications, Computer Networks and Open Systems: Hallsall.
Data Communications, Stalling.
Mobile Communication: Schiller, Pearson Education.
Introduction: Introduction to soft computing, application areas of soft computing, classification of soft computing techniques, structure & functioning of biological brain & Neuron, and concept of learning/training. Model of an Artificial Neuron, transfer/activation functions, perceptron, perceptron learning model, binary & continuous inputs, linear separability.
Multilayer Neural Networks: Feed Forward network - significance, training, loss function, Back-Propagation algorithm, convergence & generalization, momentum, applications. Feedback network -Hopfield Nets: architecture, energy functions, training algorithms & examples, competitive learning, self-organizing maps. Introduction to CNN and RNN network.
Fuzzy Systems: fuzzy set theory, fuzzy sets and operations, membership functions, concept of fuzzy relations and their composition, concept of fuzzy Measures. Fuzzy logic: fuzzy rules, inferencing. Fuzzy Control system: selection of membership functions, Fuzzyfication, rule based design & inferencing, defuzzyfication, applications of fuzzy system.
Genetic algorithm: concepts, creation of offspring, working principle, encoding, fitness functions, reproduction, genetic modeling. Generation cycle & convergence of GA, application areas of GA.
Advanced soft computing techniques: Rough Set Theory - Introduction, Set approximation, Rough membership, Attributes, optimization. SVM - Introduction, obtaining the optimal hyper plane, linear and nonlinear SVM classifiers. Introduction to Swarm Intelligence, Swarm Intelligence Techniques: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization etc.
S.N. Sivanandam & S.N. Deepa, Principles of Soft Computing, Wiley Publications
S, Rajasekaran & G.A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic & Genetic Algorithms, Synthesis & applications, PHI Publication
Bose, Neural Network fundamental with Graph , Algo.& Appl, TMH Kosko: Neural Network & Fuzzy System, PHI Publication
Klir & Yuan ,Fuzzy sets & Fuzzy Logic: Theory & Appli.,PHI Pub. Hagen, Neural Network Design, Cengage Learning
Installation and configuration of Hadoop/Euceliptus etc.
Service deployment & Usage over cloud.
Management of cloud resources.
Using existing cloud characteristics & Service models .
Cloud Security Management. 6. Performance evaluation of services over cloud .
Buyya, Selvi ,” Mastering Cloud Computing “,TMH Pub
Kumar Saurabh, “Cloud Computing” , Wiley Pub
Krutz , Vines, “Cloud Security “ , Wiley Pub
Velte, “Cloud Computing- A Practical Approach” ,TMH Pub
Sosinsky, “ Cloud Computing” , Wiley Pub
INTRODUCTION
Machine learning basics: What is Machine Learning, Types and Applications of ML, , Tools used, AI vs ML .Introduction to Neural Networks.
Introduction to linear regression: SSE; gradient descent; closed form; normal equations; features, Introduction to classification: Classification problems; decision boundaries; nearest neighbor methods.
Linear regression; SSE; gradient descent; closed form; normal equations; features Overfitting and complexity; training, validation, test data, and introduction to Matlab (II)
UNIT-II
SUPERVISED LEARNING:
Introduction to Supervised Learning, Supervised learning setup, LMS, Linear Methods for Classification, Linear Methods for Regression, Support Vector Machines. Basis Expansions, Model Selection Procedures
Perceptron, Exponential family, Generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines, Model selection and feature selection, Decision Tree, Ensemble methods: Bagging, boosting, Evaluating and debugging learning algorithms. Classification problems; decision boundaries; nearest neighbor methods, Probability and classification, Bayes optimal decisions Naive Bayes and Gaussian class- conditional distribution,
Linear classifiers Bayes' Rule and Naive Bayes Model, Logistic regression, online gradient descent, Neural Networks Decision tree and Review for Mid-term, Ensemble methods: Bagging, random forests, boosting A more detailed discussion on Decision Tree and Boosting
UNIT-III
REINFORCEMENT LEARNING: Markov decision process (MDP), HMM, Bellman equations, Value iteration and policy iteration, Linear quadratic regulation, Linear Quadratic Gaussian, Q-learning, Value function approximation, Policy search, Reinforce, POMDPs.
UNIT-IV
UNSUPERVISED LEARNING:
Introduction to Unsupervised Learning : Association Rules, Cluster Analysis, Reinforcement Learning,Clustering K-means, EM. Mixture of Gaussians, Factor analysis, PCA (Principal components analysis), ICA (Independent components analysis);, hierarchical agglomeration Advanced discussion on clustering and EM, Latent space methods; PCA, Text representations; naive Bayes and multinomial models; clustering and latent space models, VC-dimension, structural risk minimization; margin methods and support vector machines (SVM), Support vector machines and large-margin classifiers Time series; Markov models; autoregressive models
UNIT-V
DIMENSIONALITY REDUCTION: Feature Extraction , Singular value decomposition. Feature selection – feature ranking and subset selection, filter, wrapper and embedded methods. Machine Learning for Big data: Big Data and MapReduce, Introduction to Real World ML, Choosing an Algorithm, Design and Analysis of ML Experiments, Common Software for ML
Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and
Machine Learning), The MIT Press 2004.
Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
Berson: Data Warehousing & Data Mining &OLAP , TMH
Jiawei Han and Micheline Kamber, Data Mining Concepts & Techniques, Elsevier Pub.
Arun.K.Pujari, Data Mining Techniques, University Press.
N.P Gopalan: Data Mining Technique & Trend, PHI
Hand, Mannila & Smith: Principle of Data Mining, PHI
Tan, Introduction to Data Mining, Pearson Pub.
P. Pal Chandhari , “Computer Organization and design” Prentice Hall of India Pvt. Ltd, 1994
Del Corso, H.Kirrman, JD Nicond “Microcomputer buses & links” Academic Press 1986.
Douglas V Hall “Microprocessor & Interfacing Programming & H/W” McGraw Hill International 2nd Edition 1992.
Scott Muller, “Upgrading and repairing PC”
Introduction Computers and its Impact in Society, Overview of Computer and Web Technology, Need for Cyber Law, Cyber Jurisprudence at International and Indian Level, Cyber Law - International Perspectives UN & International Telecommunication Union (ITU) Initiatives Council of Europe - Budapest Convention on Cybercrime, Asia-Pacific Economic Cooperation (APEC), Organization for Economic Co-operation and Development (OECD), World Bank, Commonwealth of Nations.
Constitutional & Human Rights Issues in Cyberspace Freedom of Speech and Expression in Cyberspace, Right to Access Cyberspace – Access to Internet, Right to Privacy, Right to Data Protection, Cyber Crimes & Legal Framework Cyber Crimes against Individuals, Institution and State, Hacking, Digital Forgery, Cyber Stalking/Harassment, Cyber Pornography, Identity Theft & Fraud Cyber terrorism, Cyber Defamation.
Cyber Torts Cyber Defamation, Different Types of Civil Wrongs under the IT Act 2000, Intellectual Property Issues in Cyber Space Interface with Copyright Law, Interface with Patent Law, Trademarks & Domain Names Related issues
E-Commerce Concept, E-commerce-Salient Features, Online approaches like B2B, B2C & C2C Online contracts, Click Wrap Contracts, Applicability of Indian Contract Act, 1872,
Dispute Resolution in Cyberspace, Concept of Jurisdiction, Indian Context of Jurisdiction and IT Act, 2000. International Law and Jurisdictional Issues in Cyberspace, Dispute Resolutions .
Chris Reed & John Angel, Computer Law, OUP, New York.
Justice Yatindra Singh, Cyber Laws, Universal Law Publishing Co, New Delhi.
Verma S, K, Mittal Raman, Legal Dimensions of Cyber Space, Indian Law Institute.
Jonthan Rosenoer, Cyber Law, Springer, New York.
Sudhir Naib, The Information Technology Act, 2005: A Handbook, OUP, New York.
S. R. Bhansali, Information Technology Act, 2000, University Book House Pvt. Ltd.
Introduction of Virtual Reality: Fundamental Concept and Components of Virtual Reality. Primary Features and Present Development on Virtual Reality. Multiple Modals of Input and Output Interface in Virtual Reality: Input -Tracker, Sensor, Digital Glove, Movement Capture, Video-based Input, 3D Menus & 3DScanner etc. Output -- Visual / Auditory / Haptic Devices.
Environment Modeling in Virtual Reality: Geometric Modeling, Behavior Simulation, Physically Based Simulation, Interactive Techniques in Virtual Reality: Body Track, Hand Gesture, 3D Manus, Object Grasp
Burdea, G. C. and P. Coffet. Virtual Reality Technology, Second Edition. Wiley- IEEE Press, 2003/2006.
Sherman, William R. and Alan B. Craig. Understanding Virtual Reality – Interface, Application, and Design, Morgan Kaufmann, 2002.
Fei GAO. Design and Development of Virtual Reality Application System, Tsinghua Press, March 2012
Review of Networking and O.S. fundamentals, ISO-OSI Model, different layers and their functions, LAN, MAN, WAN, Communication media & principles IEEE standards etc.
Internetworking with TCP/IP, Basic concepts, Principles, Protocols and Architecture, Address handling Internet protocols and protocol layering. DNS, Applications: TELNET, RLOGN , FTP, TFTP, NFS, SMTP, POPL, IMAP, MIME, HTTP,STTP,DHCP, VOIP, SNMP.
Introduction to Router, Configuring a Router, Interior & Exterior Routing, RIP, Distance Vector Routing, OSPF, BGP, Uni-cast, Multicast and Broadcast. Multicast routing protocols: DVMRP, MOSPF, CBT, PIM, MBONE, EIGRP, CIDR, Multicast Trees, Comparative study of IPv6 and IPv4.
VPN addressing and routing, VPN Host management, ATM Concepts, Services Architecture, Equipments and Implementation
Introduction to wireless transmission and medium access control, wireless LAN: IEEE 802.11, Hipher LAN , Bluetooth Mobile Network and Transport layer, WAP GSM and CDMA: Network architecture and management
Computer Networks: Tanenbaum.
Internetworking with TCP/IP: Comer.
Data Communications, Computer Networks and Open Systems: Hallsall.
Data Communications, Stalling.
Mobile Communication: Schiller, Pearson Education.