Microsoft Word - R20 M.Tech.CS_CSE Syllabusfinal.docx · Web viewR20 M.TECH. CSE10CMR Engineering CollegeM. Tech – CSE – I Year – I Semester ... analysis of web traffic, computer

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CMR ENGINEERING COLLEGE(Approved by AICTE, Affiliated to JNTU, Hyderabad)

UGC AUTONOMOUS

M. Tech. COMPUTER SCIENCE AND ENGINEERING EFFECTIVE FROM ACADEMIC YEAR 2020 - 21 (ADMITTED BATCH R20)

COURSE STRUCTURE AND SYLLABUS

I YEAR I – SEMESTERCourse Code Course Title L T P Credits

CS8101PC Mathematical Foundations of Computer Science 3 0 0 3CS8102PC Advanced Data Structures Using Python 3 0 0 3

Professional Elective - I 3 0 0 3Professional Elective - II 3 0 0 3

CS8103PC Advanced Data Structures Using Python Lab 0 0 4 2Professional Elective - I Lab 0 0 4 2

CS8104PC Research Methodology & IPR 2 0 0 2*MC8105 Audit Course – I 2 0 0 0

Total 16 0 8 18

I YEAR II –SEMESTERCourse Code Course Title L T P Credits

CS8201PC Advanced Algorithms 3 0 0 3CS8202PC Advanced Computer Architecture 3 0 0 3

Professional Elective - III 3 0 0 3Professional Elective - IV 3 0 0 3

CS8203PC Advanced Algorithms Lab 0 0 4 2Professional Elective - III Lab 0 0 4 2

CS8204PROJ Mini Project with Seminar 0 0 4 2*MC8205 Audit Course - II 2 0 0 0

Total 14 0 12 18

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II YEAR I –SEMESTERCourse Code Course Title L T P Credits

Professional Elective - V 3 0 0 3Open Elective 3 0 0 3

CS8301PROJ Dissertation Work Review - I 0 0 12 6Total 6 0 12 12

II YEAR II - SEMESTERCourse Code Course Title L T P Credits

CS8401PROJ Dissertation Work Review - II 0 0 12 6CS8402PROJ Dissertation Viva-Voce 0 0 28 14

Total 0 0 40 20

*For Dissertation Work Review - I, please refer to 7.8 in R20 Academic Regulations.

Professional Elective- I

Professional Elective- IICourse Code Course Title L T P Credits

CS8121PE Reinforcement Learning 3 0 0 3CS8122PE Web and Database Security 3 0 0 3CS8123PE High Performance Computing 3 0 0 3

Professional Elective- IIICourse Code Course Title L T P Credits

CS8231PE Big Data Analytics 3 0 0 3CS8232PE Digital Forensics 3 0 0 3CS8233PE Parallel Computing 3 0 0 3

Professional Elective– IVCourse Code Course Title L T P Credits

CS8241PE Ubiquitous and Pervasive Computing 3 0 0 3CS8242PE Wireless and Sensor Networks 3 0 0 3CS8243PE Optimization Techniques 3 0 0 3

Course Code Course Title L T P Credits

CS8111PE Machine Learning 3 0 0 3

CS8112PE Cryptography & Network Security 3 0 0 3CS8113PE Internet of Things 3 0 0 3

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Professional Elective – VCourse Code Course Title L T P Credits

CS8351PE Deep Learning 3 0 0 3CS8352PE Natural Language Processing 3 0 0 3CS8353PE Blockchain Technology 3 0 0 3

Open ElectiveCourse Code Course Title L T P Credits

CS8311OE Machine Learning 3 0 0 3CS8312OE Big Data Analytics 3 0 0 3CS8313OE Digital Forensics 3 0 0 3CS8314OE Cyber Security 3 0 0 3

Audit Course I & II:1. English for Research Paper Writing2. Disaster Management3. Sanskrit for Technical Knowledge4. Value Education5. Constitution of India6. Pedagogy Studies7. Stress Management by yoga8. Personality Development Through Life Enlightenment Skills

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CMR Engineering CollegeM. Tech – CSE – I Year – I Semester

CS8101PC: MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE

Pre-Requisites: UG level course in Discrete Mathematics/ Mathematical Foundations of Computer Science

Course Objectives: To understand the mathematical fundamentals that is prerequisites for a variety

of courses like data mining, network protocols, analysis of web traffic, computer security, software engineering, computer architecture, operating systems, distributed systems, bioinformatics, machine learning.

To develop the understanding of the mathematical and logical basis to many modern techniques in information technology like machine learning, programming language design, and concurrency.

To study various sampling and classification problems.

Course Outcomes: After completion of course, students would be able to: To understand the basic notions of discrete and continuous probability. To understand the methods of statistical inference, and the role that sampling

distributions play in those methods. To be able to perform correct and meaningful statistical analyses of simple

to moderate complexity.

UNIT – IProbability mass, density, and cumulative distribution functions, parametric families of distributions, expected value, variance, conditional expectation, applications of the univariate and multivariate distributions, central limit theorem, probabilistic inequalities, markov chains

UNIT - IIDescriptive statistics, notion of probability distributions, mean, variance, covariance, covariance matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence interval for estimates, methods of moments and maximum likelihood

UNIT - IIIFundamental notions of linear algebra namely, vector spaces, linear independence, and basis, matrix and vector norms, eigenvalues and vectors in dimensionality reduction, singular value decomposition, principal component analysis, linear discriminant analysis, least squares problem and linear regression, numerical solution of linear least-squares problems

UNIT – IVGraph Theory: isomorphism, planar graphs, graph coloring, Hamilton circuits and Euler cycles. permutations and combinations with and without repetition. specialized techniques to solve combinatorial enumeration problems

UNIT - VComputer science and engineering applications data mining, network protocols, analysis of web traffic, computer security, software engineering, computer architecture, operating systems, distributed systems, bioinformatics, machine learning.Recent trends in various distribution functions in mathematical field of computer science for varying fields like bio-informatics, soft computing, and computer vision.

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Text Books:1. Foundation Mathematics for Computer Science, John Vince, Springer.2. Probability and Statistics with Reliability, Queuing, and Computer Science

Applications, K. Trivedi, Wiley.

References:1. Probability and Computing: Randomized Algorithms and Probabilistic

Analysis, M. Mitzenmacher and E. Upfal.2. Applied Combinatorics, Alan Tucker, Wiley3. Introduction to Linear Algebra, Gilbert Strang, Fifth Edition, 2016

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CS8102PC: ADVANCED DATA STRUCTURES USING PYTHON

Pre-Requisites: UG level course in Data Structures

Course Objectives: The student should be able to choose appropriate data structures,

understand the ADT/libraries, and use it to design algorithms for a specific problem.

Students should be able to understand the necessary mathematical abstraction to solve problems.

To familiarize students with advanced paradigms and data structure used to solve algorithmic problems.

Student should be able to come up with analysis of efficiency and proofs of correctness.

Course Outcomes: After completion of course, students would be able to: Understand the implementation of symbol table using hashing techniques. Develop algorithms for text processing applications. Identify suitable data structures and develop algorithms for computational geometry

problems.

UNIT – IComplexity Analysis: Time and space complexity analysisDictionaries:Definition, dictionary, Abstract Data Type, implementation of dictionaries.Hashing:Review of hashing, hash function, collision resolution techniques in hashing, separate chaining, open addressing, linear probing, quadratic probing, double hashing, rehashing, extendible hashing.

UNIT - IISkip Lists:Need for randomizing data structures and algorithms, search and update operations on skip lists, probabilistic analysis of skip lists, deterministic skip lists.

UNIT - IIITrees:Binary Search Trees, AVL Trees, Red Black Trees, 2-3 Trees, B-Trees, Splay Trees

UNIT - IVText Processing:Sting Operations, Brute-Force Pattern Matching, The Boyer- Moore Algorithm, The Knuth-Morris-Pratt Algorithm, Standard Tries, Compressed Tries, Suffix Tries, The Huffman Coding Algorithm, The Longest Common Subsequence Problem (LCS), Applying Dynamic Programming to the LCS Problem

UNIT - VComputational Geometry:One dimensional range searching, two-dimensional range searching, constructing a priority search tree, searching a priority search tree, priority range trees, quadtrees, k-D Trees.Recent trends in hashing, trees and various computational geometry methods for efficiently solving the new evolving problem

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Textbooks1. Data Structures and Algorithms in Python, Roberto Tamassia, Michael H.

Goldwasser, Michael T. Goodrich2. Data Structures and Algorithms Using Python, Rance D. Necaise, Wiley3. Data Structures and Algorithms with Python. Kent D. Lee. Steve Hubbard, Springer4. Introduction to Algorithms, TH Cormen, PHI

References:1. Algorithm Design, M T Goodrich, Roberto Tamassia, John Wiley, 2002.

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CS8111PE: MACHINE LEARNING (Professional Elective - I)

Course Pre-Requisites: Probability and Statistics, Database management system, Algorithms

Course Objectives: To learn the concept of how to learn patterns and concepts from data without

being explicitly programmed. To design and analyse various machine learning algorithms and techniques

with a modern outlook focusing on recent advances. Explore supervised and unsupervised learning paradigms of machine learning. To explore Deep learning technique and various feature extraction strategies.

Course Outcomes: After completion of course, students would be able to: Extract features that can be used for a particular machine learning approach

in various applications. To compare and contrast pros and cons of various machine learning techniques

and to get an insight of when to apply a particular machine learning approach. To mathematically analyse various machine learning approaches and paradigms.

UNIT - ISupervised Learning (Regression/Classification): Introduction to Statistical Learning Theory, Distance- based methods, Nearest-Neighbours, Decision Trees, Naive Bayes algorithmLinear models: Linear Regression, Logistic Regression, Generalized Linear Models. Support Vector Machines, Nonlinearity and Kernel Methods.Beyond Binary Classification: Multi-class/Structured Outputs, Ranking.

UNIT – IIUnsupervised Learning: Clustering, K-means/Kernel K-means, Dimensionality Reduction: PCA and kernel PCA., Matrix Factorization and Matrix Completion, Generative Models (mixture models and latent factor models).

UNIT - IIIEvaluating Machine Learning algorithms and Model Selection, Ensemble Methods (Boosting, Bagging, Random Forests)

UNIT - IVSparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and Feature Representation Learning

UNIT - VScalable Machine Learning (Online and Distributed Learning), A selection from some other advanced topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in Graphical Models, Introduction to Bayesian Learning and Inference.Recent trends in various learning techniques of machine learning and classification methods for IOT applications. Various models for IOT applications.

Text Books1. Machine Learning, M. Mitchell,-MGH2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis

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References:1. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 20122. The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome

Friedman, Springer 2009 (freely available online)3. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007

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CMR Engineering College

CS8112PE: CRYPTOGRAPHY & NETWORK SECURITY (Professional Elective - I)

Pre-Requisites: Computer Networks, Mathematics

Course Objectives:1. To impart knowledge on network security issues, services, goals and mechanisms.2. To analyze the security of communication systems, networks and protocols.3. To apply algorithms used for secure transactions in real world applications

Course Outcomes:1. Demonstrate the knowledge of cryptography and network security concepts and

applications.2. Ability to apply security principles in system design.3. Ability to identify and investigate vulnerabilities and security threats and

mechanisms to counter them.

UNIT – ISecurity Attacks (Interruption, Interception, Modification and Fabrication), Security Services (Confidentiality, Authentication, Integrity, Non-repudiation, access Control and Availability) and Mechanisms, A model for Internetwork security, Internet Standards and RFCs, Buffer overflow & format string vulnerabilities, TCP session hijacking, ARP attacks, route table modification, UDP hijacking, and man-in-the-middle attacks.

UNIT – IINumber Theory: Modular Arithmetic, Euclid’s Algorithm, Fermat’s and Euler’s Theorem, Chinese Remainder Theorem, Public key cryptography principles, public key cryptography algorithms, digital signatures, digital Certificates, Certificate Authority and key management Kerberos, X.509 Directory Authentication Service.

UNIT – IIIConventional Encryption: Principles, Conventional encryption algorithms (DES, AES, RC4, Blowfish), cipher block modes of operation, location of encryption devices, key distribution Approaches of Message Authentication, Secure Hash Functions and HMAC.

UNIT - IVEmail privacy: Pretty Good Privacy (PGP) and S/MIME.IP Security: Overview, IP Security Architecture, Authentication Header, Encapsulating Security Payload, Combining Security Associations and Key Management.

UNIT - VWeb Security: Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS), Secure Electronic Transaction (SET).Intruders, Viruses and related threats, Firewall Design principles, Trusted Systems, Intrusion Detection Systems.

Text Book:1. Cryptography and Network Security, William Stallings, 3rd Edition, Pearson Education.

References:1. Cryptography and Network Security, Behrouz A. Forouzan, 2nd edition, Tata

McGraw-Hill Education2. Applied Cryptography. B. Schneier Wiley, 1999.

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CS8113PE: INTERNET OF THINGS (Professional Elective - I)

Course Objectives:1. To introduce the terminology, technology and its applications2. To introduce the concept of M2M (machine to machine) with necessary protocols3. To introduce the Python Scripting Language which is used in many IoT devices4. To introduce the Raspberry PI platform, that is widely used in IoT applications5. To introduce the implementation of web-based services on IoT devices

Course Outcomes:1. Understand the new computing technologies2. Able to apply the latest computing technologies like cloud computing technology and

Big Data3. Ability to introduce the concept of M2M (machine to machine) with necessary

protocols4. Get the skill to program using python scripting language which is used in many IoT

devices

UNIT - IIntroduction to Internet of Things –Definition and Characteristics of IoT,Physical Design of IoT – IoT Protocols, IoT communication models, Iot Communication APIsIoT enabled Technologies – Wireless Sensor Networks, Cloud Computing, Big data analytics, Communication protocols, Embedded Systems, IoT Levels and TemplatesDomain Specific IoTs – Home, City, Environment, Energy, Retail, Logistics, Agriculture, Industry, health and Lifestyle

UNIT - IIIoT and M2M – Software defined networks, network function virtualization, difference between SDN and NFV for IoT, Basics of IoT System Management with NETCOZF, YANG- NETCONF, YANG, SNMP NETOPEER

UNIT - IIIIntroduction to Python - Language features of Python, Data types, data structures, Control of flow, functions, modules, packaging, file handling, data/time operations, classes, Exception handling Python packages - JSON, XML, HTTPLib, URLLib, SMTPLib

UNIT - IVIoT Physical Devices and Endpoints - Introduction to Raspberry PI-Interfaces (serial, SPI, I2C) Programming – Python program with Raspberry PI with focus of interfacing external gadgets, controlling output, reading input from pins.

UNIT - VIoT Physical Servers and Cloud Offerings – Introduction to Cloud Storage models and communication APIs Webserver – Web server for IoT, Cloud for IoT, Python web application framework Designinga RESTful web API

Text Books:1. Internet of Things - A Hands-on Approach, Arshdeep Bahga and Vijay Madisetti,

Universities Press, 20152. Getting Started with Raspberry Pi, Matt Richardson & Shawn Wallace, O'Reilly (SPD),

2014

References:

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1. From machine to machine to the internet of things: Introduction to a new age of intelligence, Jan Holler.

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CS8121PE: REINFORCEMENT LEARNING (Professional Elective - II)

Pre-Requisites: Mathematics for Machine Learning

Course Objectives:1. Learn the theoretical foundations of reinforcement learning (Markov decision

processes & dynamic programming).2. Learn the algorithmic foundations of reinforcement learning (temporal

difference and Monte- Carlo learning).3. Gain experience in framing low-dimensional problems and implementing solutions

using tabular reinforcement learning.4. Learn about the motivation behind deep reinforcement learning and its

relevance to high- dimensional applications, such as playing video games, and robotics.

5. Discover the state-of-the-art deep reinforcement learning algorithms such as Deep Q Networks (DQN), Proximal Policy Optimisation (PPO), and Soft Actor Critic (SAC)

Course Outcomes:1. Describe the core principles of autonomous systems learning.2. Calculate mathematical solutions to problems using reinforcement learning theory.3. Compare and contrast a range of reinforcement learning approaches.4. Propose solutions to decision making problems using knowledge of the state-of-the-

art.5. Evaluate the performance of a range of methods and propose appropriate

improvements.

UNIT – IIntroduction to Reinforcement Learning and its Mathematical Foundations, The Markov Decision Process Framework, Markov Reward Processes, The Policy, Markov Decision Processes

UNIT – IIDynamic Programming, Model-Free Learning & Control, Monte-Carlo Learning, Temporal Difference Learning

UNIT – IIIMotivation for function approximation, High-dimensional state and action spaces, Continuous state and action spaces

UNIT – IVDeep Q-learning, Q update through back propagation, Experience replay buffer, Target and Q networks

UNIT – VPolicy gradients: The REINFORCE algorithm, Policy update through back propagation, Proximal Policy Optimization Advanced topics: Soft Actor Critic, Learning from demonstration, Model-based reinforcement learning

Text Books1. Optimal Control, Linear Quadratic Methods, Anderson and Moore. (1989).2. Optimal control and reinforcement learning, Bertsekas. (2019)3. Predictive control for linear and hybrid systems, Borrelli, Bemporad, and Morari.

(2017).References:

1. Reinforcement Learning, Sutton and Barto (2018).2. Adaptive Filtering Prediction and Control, Goodwin and Kwai. (1984).

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CS8122PE: WEB AND DATABASE SECURITY (Professional Elective - II)

Pre-Requisites: Database Management Systems

Course Objectives:1. Give an Overview of information security2. Give an overview of Access control of relational databases

Course Outcomes: Students should be able to1. Understand the Web architecture and applications2. Understand client side and server side programming3. Understand how common mistakes can be bypassed and exploit the application4. Identify common application vulnerabilities

UNIT - IThe Web SecurityThe Web Security Problem, Risk Analysis and Best PracticesCryptography and the Web: Cryptography and Web Security, Working Cryptographic Systems and Protocols, Legal Restrictions on Cryptography, Digital Identification

UNIT - IIThe Web PrivacyThe Web’s War on Your Privacy, Privacy-Protecting Techniques, Backups and Antitheft, Web Server Security, Physical Security for Servers, Host Security for Servers, Securing Web Applications

UNIT - IIIDatabase SecurityRecent Advances in Access Control, Access Control Models for XML, Database Issues in Trust Management and Trust Negotiation, Security in Data Warehouses and OLAP Systems

UNIT - IVSecurity Re-engineering for DatabasesConcepts and Techniques, Database Watermarking for Copyright Protection, Trustworthy Records Retention, Damage Quarantine and Recovery in Data Processing Systems, Hippocratic Databases: Current Capabilities and

UNIT - VFuture Trends Privacy in Database PublishingA Bayesian Perspective, Privacy-enhanced Location-based Access Control, Efficiently Enforcing the Security and Privacy Policies in a Mobile Environment

Text Books:1. Web Security, Privacy and Commerce, Simson G. Arfinkel, Gene Spafford, O’ Reilly.2. Handbook on Database security applications and trends, Michael Gertz, Sushil Jajodia.

References:1. Web application security, Bryan Sullivan, Mc Graw Hi

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CS8123PE: HIGH PERFORMANCE COMPUTING (Professional Elective - II)

Pre-Requisites: Computer Organization

Course Objectives: To Improve the system performance To learn various distributed and parallel computing architecture To learn different computing technologies

Course Outcomes: Understanding the concepts in grid computing Ability to understand the cluster projects and cluster OS Understanding the concepts of pervasive computing & quantum computing.

UNIT - IGrid Computing: data & computational grids, grid architectures and its relations to various distributed technologies, autonomic computing, examples of the grid computing efforts (IBM).

UNIT - IICluster setup & its advantages, performance models & simulations, networking protocols & i/o, messaging systems, process scheduling, load sharing and balancing, distributed shared memory, parallel i/o.

UNIT - IIIExample Cluster System – Beowulf, cluster operating systems, compas and nanos pervasive computing concepts, scenarios, hardware & software, human machine interface.

UNIT - IVDevice connectivity, java for pervasive devices, application examples.

UNIT – VClassical vs quantum logic gates, one, two & three qubit quantum gates, Fredkin & Toffoli gates, quantum circuits, quantum algorithms.

Text Book:1. Grid Computing, J. Joseph & C. Fellenstien, Pearson Education2. Selected topics in advanced computing, Dr. P. Padmanabham, Dr. M.B. Srinivas,

2005 Pearson Education.References:

1. Pervasive computing, J. Burkhardt, Pearson Education2. Approaching quantum computing, Marivesar, Pearson Education.3. High performance cluster computing, Raj kumar Buyya, Pearson Education.4. Quantum computing and Quantum Information, Neilsen & Chung L, Cambridge

University Press.5. A networking approach to Grid Computing, Minoli, Wiley.

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CS8103PC: ADVANCED DATA STRUCTURES USING PYTHON LAB (Lab - I)

Pre-Requisites: A course on Computer Programming & Data Structures

Course Objectives:1. Introduces the basic concepts of Abstract Data Types.2. Reviews basic data structures such as stacks and queues.3. Introduces a variety of data structures such as hash tables, search trees, tries,

heaps, graphs, and B-trees.4. Introduces sorting and pattern matching algorithms.

Course Outcomes:1. Ability to select the data structures that efficiently model the information in a problem.2. Ability to assess efficiency trade-offs among different data structure

implementations or combinations.3. Implement and know the application of algorithms for sorting and pattern matching.4. Design programs using a variety of data structures, including hash tables, binary

and general tree structures, search trees, tries, heaps, graphs, and B-trees.

List of Programs1. Write a program to perform the following operations:

a) Insert an element into a binary search tree.b) Delete an element from a binary search tree.c) Search for a key element in a binary search tree.

2. Write a program for implementing the following sorting methods:a) Merge sort b) Heap sort c) Quick sort

3. Write a program to perform the following operations:a) Insert an element into a B- tree.b) Delete an element from a B- tree.c) Search for a key element in a B- tree.

4. Write a program to perform the following operations:a) Insert an element into a Min-Max heapb) Delete an element from a Min-Max heapc) Search for a key element in a Min-Max heap

5. Write a program to perform the following operations:a) Insert an element into a Lefiist treeb) Delete an element from a Leftist treec) Search for a key element in a Leftist tree

6. Write a program to perform the following operations:a) Insert an element into a binomial heapb) Delete an element from a binomial heap.c) Search for a key element in a binomial heap

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7. Write a program to perform the following operations:a) Insert an element into a AVL tree.b) Delete an element from a AVL search tree.c) Search for a key element in a AVL search tree.

8. Write a program to perform the following operations:a) Insert an element into a Red-Black tree.b) Delete an element from a Red-Black tree.c) Search for a key element in a Red-Black tree.

9. Write a program to implement all the functions of a dictionary using hashing.

10. Write a program for implementing Knuth-Morris-Pratt pattern matching algorithm.

11. Write a program for implementing Brute Force pattern matching algorithm.

12. Write a program for implementing Boyer pattern matching algorithm.

TEXT BOOKS:1. Data Structures and Algorithms in Python, Roberto Tamassia, Michael H.

Goldwasser, Michael T. Goodrich2. Data Structures and Algorithms Using Python, Rance D. Necaise, Wiley

REFERENCES:1. Data Structures and Algorithms with Python. Kent D. Lee. Steve Hubbard, Springer

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CS8111PEL: MACHINE LEARNING LAB (Lab – II)

Course Objective:1. The objective of this lab is to get an overview of the various machine learning

techniques and can able to demonstrate them using python.

Course Outcomes: After the completion of the “Machine Learning” lab, the student can able to1. Understand complexity of Machine Learning algorithms and their limitations;2. Understand modern notions in data analysis-oriented computing;3. Be capable of confidently applying common Machine Learning algorithms in

practice and implementing their own;4. Be capable of performing experiments in Machine Learning using real-world data.

List of Experiments1. The probability that it is Friday and that a student is absent is 3 %. Since there

are 5 school days in a week, the probability that it is Friday is 20 %. What is the probability that a student is absent given that today is Friday? Apply Baye’s rule in python to get the result. (Ans: 15%)

2. Extract the data from database using python3. Implement k-nearest neighbours classification using python4. Given the following data, which specify classifications for nine combinations

of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606, using the result of k-means clustering with 3 means (i.e., 3 centroids)

VAR1 VAR2 CLASS1.713 1.586 00.180 1.786 10.353 1.240 10.940 1.566 01.486 0.759 11.266 1.106 01.540 0.419 10.459 1.799 10.773 0.186 1

5. The following training examples map descriptions of individuals onto high, medium and low credit-worthiness.

medium skiing design single twenties no -> highRisk high golf trading

married forties yes -> lowRisklow speedway transport married thirties yes -> medRisk medium football banking single thirties yes -> lowRiskhigh flying media married fifties yes -> highRisk low football security single twenties no -> medRisk medium golf

media single thirties yes -> medRisk medium golf transport married forties yes -> lowRisk high skiing banking

single thirties yes -> highRisk low golfunemployed married forties yes -> highRisk

Input attributes are (from left to right) income, recreation, job, status, age-group, home-owner. Find the unconditional probability of `golf' and the conditional probability

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standard datasets

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7. Implement linear regression using python.8. Implement Naïve Bayes theorem to classify the English text9. Implement an algorithm to demonstrate the significance of genetic algorithm10. Implement the finite words classification system using Back-propagation algorithm

Text Books:1. Machine Learning, Tom M. Mitchell, MGH2. Introduction to machine learning using python, Andereas C. Muller & Sarah Guido

Reference Book:1. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis

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CS8112PEL: CRYPTOGRAPHY & NETWORK SECURITY LAB (Lab – II)

Course Objectives:1. To understand basics of cryptography & network security2. To be able to secure a message over insecure channel by various means3. To learn about how to maintain the confidentiality, integrity and availability of data4. To understand various protocols for network security to protect against the

threats in the network

Course Outcomes: After the completion of the cryptography and network security lab, the student can able to

1. Identify the security issues in the network and resolve it.2. Analyze the vulnerabilities in any computing system and hence be able to

design a security solution3. Evaluate security mechanism using rigorous approaches by key ciphers and hash

functions4. Learn different network security tools

List of Experiments1. Write a client-server program where client sends a text message to server and

server sends the text message to client by changing the case(uppercase and lowercase) of each character in the message.

2. Write a client-server program to implement following classical encryption techniques: ceaser cipher transposition cipher row substitution cipher hill cipher

3. Install JCrypt tool (or any other equivalent) and demonstrate Asymmetric, Symmetric crypto algorithm, Hash and Digital/PKI signatures studied in theory Network Security and Management

4. Perform an experiment to demonstrate how to sniff for router traffic by using the tool wireshark,

5. Using nmapa) Find open ports on a systemb) Find the machines which are activec) Find the version of remote os on other systemsd) Find the version of s/w installed on other system

6. Ethical Hacking:1. Setup a honey pot and monitor the honey pot on network2. Write a script or code to demonstrate SQL injection attacks3. Create a social networking website login page using phishing techniques4. Write a code to demonstrate DoS attacks5. Install rootkits and study variety of options

Text Books:1. Cryptography and network security, Behrouj A forouzan, Debdeep Mukhopadhyay Mc

Graw Hill2. The network security test lab, A step by step guide, Michael Gregg, Wiley

References:1. Hacking S3crets, Sai Satish, K. Srujan Raju,2. Cryptography and network security principles and practices, William stallings, 4 Ed,

Prentice Hall

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CS8113PEL: IOT LAB (Lab – II)

Course Objectives1. To introduce the raspberry PI platform, that is widely used in IoT applications2. To introduce the implementation of distance sensor on IoT devices

Course Outcomes1. Ability to introduce the concept of M2M (machine to machine) with necessary

protocols and get awareness in implementation of distance sensor2. Get the skill to program using python scripting language which is used in many IoT

devices

List of Experiments1. Using raspberry pi

a. Calculate the distance using distance sensor.b. Basic LED functionality.

2. Using Arduinoa. Calculate the distance using distance sensor.b. Basic LED functionality.c. Calculate temperature using temperature sensor.

3. Using Node MCUa. Calculate the distance using distance sensor.b. Basic LED functionality.c. Calculate temperature using temperature sensor.

4. Write a program on Arduino/Raspberry Pi to publish temperature data to MQTT broker.5. Write a program on Arduino/Raspberry Pi to subscribe to MQTT broker for

temperature data and print it.6. Write a program to create UDP server on Arduino/Raspberry Pi and respond with

humidity data to UDP client when requested

Text Books:1. The Internet of Things: A Hands-on Approach, Arshdeep Bahga and Vijay K. Madisetti2. Internet of things programming Projects, Colin Dow, Packt

References:1. Internet of things with python, Gaton C. Hiliar, Packt

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CS8104PC: RESEARCH METHODOLOGY & IPR

Course Objectives: To understand the research problem To know the literature studies, plagiarism and ethics To get the knowledge about technical writing To analyze the nature of intellectual property rights and new developments To know the patent rights

Course Outcomes: At the end of this course, students will be able to Understand research problem formulation. Analyze research related information Follow research ethics Understand that today’s world is controlled by Computer, Information

Technology, but tomorrow world will be ruled by ideas, concept, and creativity.

Understanding that when IPR would take such important place in growth of individuals & nation, it is needless to emphasis the need of information about Intellectual Property Right to be promoted among students in general & engineering in particular.

Understand that IPR protection provides an incentive to inventors for further research work and investment in R & D, which leads to creation of new and better products, and in turn brings about, economic growth and social benefits.

UNIT-IMeaning of research problem, Sources of research problem, Criteria Characteristics of a good research problem, Errors in selecting a research problem, Scope and objectives of research problem.Approaches of investigation of solutions for research problem, data collection, analysis, interpretation, Necessary instrumentations

UNIT-IIEffective literature studies approaches, analysis, Plagiarism, Research ethics

UNIT-IIIEffective technical writing, how to write report, Paper Developing a Research Proposal, Format of research proposal, a presentation and assessment by a review committee

UNIT-IVNature of Intellectual Property: Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research, innovation, patenting, development, International Scenario, International cooperation on Intellectual Property, Procedure for grants of patents, Patenting under PCT.

UNIT-VPatent Rights: Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases, Geographical Indications, New Developments in IPR, Administration of Patent System, New developments in IPR, IPR of Biological Systems, Computer Software etc. Traditional knowledge Case Studies, IPR and IITs.

TEXT BOOKS:1. Research methodology: an introduction for science & engineering students,

Stuart Melville and Wayne Goddard2. Research Methodology: An Introduction, Wayne Goddard and Stuart Melville

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REFERENCES:1. Research Methodology: A Step-by-Step Guide for beginners, Ranjit Kumar, 2nd Edition2. Resisting Intellectual Property, Halbert, Taylor & Francis Ltd ,2007.3. Industrial Design, Mayall, McGraw Hill,

1992 Product Design, Niebel, McGraw Hill, 1974

4. Introduction to Design, Asimov, Prentice Hall, 1962.5. Intellectual Property in New Technological Age, Robert P. Merges, Peter S.

Menell, Mark A. Lemley, 2016.6. Intellectual Property Rights Under WTO, T. Ramappa, S. Chand, 2008

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CMR Engineering CollegeM. Tech – CSE – I Year – II Semester

CS8201PC: ADVANCED ALGORITHMS

Course Objectives: Introduce students to the advanced methods of designing and analyzing algorithms. The student should be able to choose appropriate algorithms and use it for a specific

problem. To familiarize students with basic paradigms and data structures used to

solve advanced algorithmic problems. Students should be able to understand different classes of problems concerning

their computation difficulties. To introduce the students to recent developments in the area of algorithmic design.

Course Outcomes: After completion of course, students would be able to: Analyze the complexity/performance of different algorithms. Determine the appropriate data structure for solving a particular set of problems. Categorize the different problems in various classes according to their complexity. Students should have an insight of recent activities in the field of the advanced data

structure.

UNIT – ISorting:Review of various sorting algorithms, topological sortingGraph:Definitions and Elementary Algorithms: Shortest path by BFS, shortest path in edge-weighted case (Dijkasra's), depth-first search and computation of strongly connected components, emphasis on correctness proof of the algorithm and time/space analysis, example of amortized analysis.

UNIT – IIMatroids:Introduction to greedy paradigm, algorithm to compute a maximum weight maximal independent set. Application to MST.Graph Matching:Algorithm to compute maximum matching. Characterization of maximum matching by augmenting paths, Edmond's Blossom algorithm to compute augmenting path.

UNIT - IIIFlow-Networks:Maxflow-mincut theorem, Ford-Fulkerson Method to compute maximum flow, Edmond-Karp maximum- flow algorithm.Matrix Computations:Strassen's algorithm and introduction to divide and conquer paradigm, inverse of a triangular matrix, relation between the time complexities of basic matrix operations, LUP-decomposition.

UNIT - IVShortest Path in Graphs:Floyd-Warshall algorithm and introduction to dynamic programming paradigm, More examples of dynamic programming.Modulo Representation of integers/polynomials:Chinese Remainder Theorem, Conversion between base-representation and modulo-representation. Extension to polynomials. Application: Interpolation problem.Discrete Fourier Transform (DFT):

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In complex field, DFT in modulo ring. Fast Fourier Transform algorithm, Schonhage-Strassen Integer Multiplication algorithm.

UNIT - VLinear Programming:Geometry of the feasibility region and Simplex algorithmNP-completeness:Examples, proof of NP-hardness and NP-completeness.One or more of the following topics based on time and interest:Approximation algorithms, Randomized Algorithms, Interior Point Method, Advanced Number Theoretic Algorithm, Recent Trends in problem solving paradigms using recent searching and sorting techniques by applying recently proposed data structures.

Text Books:1. Introduction to Algorithms, Cormen, Leiserson, Rivest, Stein.2. The Design and Analysis of Computer Algorithms, Aho, Hopcroft, Ullman.

References:1. Algorithm Design, Kleinberg and Tardos.

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CS8202PC: ADVANCED COMPUTER ARCHITECTURE

Pre-Requisites: Computer Organization

Course Objectives:1. To impart the concepts and principles of parallel and advanced computer architectures.2. To develop the design techniques of Scalable and multithreaded Architectures.3. To Apply the concepts and techniques of parallel and advanced computer

architectures to design modern computer systems

Course Outcomes: Gain knowledge of1. Computational models and Computer Architectures.2. Concepts of parallel computer models.3. Scalable Architectures, Pipelining, Superscalar processors, multiprocessors

UNIT - ITheory of Parallelism, Parallel computer models, The State of Computing, Multiprocessors and Multicomputers, Multivector and SIMD Computers, PRAM and VLSI models, Architectural development tracks, Program and network properties, Conditions of parallelism, Program partitioning and Scheduling, Program flow Mechanisms, System interconnect Architectures

UNIT - IIPrincipals of Scalable performance, Performance metrics and measures, Parallel Processing applications, Speed up performance laws, Scalability Analysis and Approaches, Hardware Technologies, Processes and Memory Hierarchy, Advanced Processor Technology, Superscalar and Vector Processors, Memory Hierarchy Technology, Virtual Memory Technology

UNIT - IIIBus Cache and Shared memory, Backplane bus systems, Cache Memory organizations, Shared- Memory Organizations, Sequential and weak consistency models, Pipelining and superscalar techniques, Linear Pipeline Processors, Non-Linear Pipeline Processors, Instruction Pipeline design, Arithmetic pipeline design, superscalar pipeline design

UNIT - IVParallel and Scalable Architectures, Multiprocessors and Multicomputers, Multiprocessor system interconnects, cache coherence and synchronization mechanism, Three Generations of Multicomputers, Message-passing Mechanisms, Multivetor and SIMD computers, Vector Processing Principals, Multivector Multiprocessors, Compound Vector processing, SIMD computer Organizations, The connection machine CM-5

UNIT - VScalable, Multithreaded and Dataflow Architectures, Latency-hiding techniques, Principals of Multithreading, Fine-Grain Multicomputers, Scalable and multithreaded Architectures, Dataflow and hybrid Architectures

Text Book:1. Advanced Computer Architecture, Kai Hwang, 2nd Edition, Tata McGraw Hill Publishers.

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References:1. Computer Architecture, J.L. Hennessy and D.A. Patterson, 4th Edition, ELSEVIER.2. Advanced Computer Architectures, S.G.Shiva, Special Indian edition, CRC, Taylor

&Francis.3. Introduction to High Performance Computing for Scientists and Engineers, G.

Hager and G. Wellein, CRC Press, Taylor & Francis Group.4. Advanced Computer Architecture, D. Sima, T. Fountain, P. Kacsuk, Pearson education.5. Computer Architecture, B. Parhami, Oxford Univ. Press.

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CS8231PE: BIG DATA ANALYTICS (Professional Elective - III)

Course Objectives: The students will learn To understand about big data To learn the analytics of Big Data To Understand the MapReduce fundamentals

Course Outcomes: The students will be able to Understand the key issues in big data management and its associated applications Achieve adequate perspectives of big data analytics Understand HDFS and Map reduce fundamentals

UNIT - IBig Data Analytics: What is big data, History of Data Management, Structuring Big Data, Elements of Big Data, Big Data Analytics, Distributed and Parallel Computing for Big Data;Big Data Analytics: What is Big Data Analytics, What Big Data Analytics Isn’t, Why this sudden Hype Around Big Data Analytics, Classification of Analytics, Greatest Challenges that Prevent Business from Capitalizing Big Data, Top Challenges Facing Big Data, Why Big Data Analytics Important, Data Science, Data Scientist, Terminologies used in Big Data Environments, Basically Available Soft State Eventual Consistency (BASE), Open source Analytics Tools;

UNIT - IIUnderstanding Analytics and Big Data: Comparing Reporting and Analysis, Types of Analytics, Points to Consider during Analysis, Developing an Analytic Team, Understanding Text Analytics;Analytical Approach and Tools to Analyze Data: Analytical Approaches, History of Analytical Tools, Introducing Popular Analytical Tools, Comparing Various Analytical Tools.

UNIT - IIIUnderstanding MapReduce Fundamentals and HBase : The MapReduce Framework, Techniques to Optimize MapReduce Jobs, Uses of MapReduce, Role of HBase in Big Data Processing, Storing Data in Hadoop : Introduction of HDFS, Architecture, HDFC Files, File system types, commands, org.apache.hadoop.io package, HDF, HDFS High Availability, Introducing HBase, Architecture, Storing Big Data with HBase, Interacting with the Hadoop Ecosystem, HBase in Operations-Programming with HBase, Installation, Combining HBase and HDFS;

UNIT - IVBig Data Technology Landscape and Hadoop: NoSQL, Hadoop, RDBMS versus Hadoop, Distributed Computing Challenges, History of Hadoop, Hadoop Overview, Use Case of Hadoop, Hadoop Distributors, HDFC (Hadoop Distributed File System), HDFC Daemons, read, write, Replica Processing of Data with Hadoop, Managing Resources and Applications with Hadoop YARN.

UNIT - VSocial Media Analytics and Text Mining: Introducing Social Media, Key elements of Social Media, Text mining, Understanding Text Mining Process, Sentiment Analysis, Performing Social Media Analytics and Opinion Mining on Tweets;Mobile Analytics: Introducing Mobile Analytics, Define Mobile Analytics, Mobile Analytics

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Text Books:1. Big Data and Analytics, Seema Acharya, Subhasinin Chellappan, Wiley publications.2. Big Data, Black BookTM, DreamTech Press, 2015 Edition.3. Business Analytics 5e, BY Albright |Winston

Reference Books:1. Business Intelligence Roadmap, Lariss T. Moss, Shaku Atre, Addison-Wesley2. Oracle Business Intelligence: The Condensed Guide to Analysis and Reporting,

Yuli Vasiliev, SPD Shroff, 2012.

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CMR Engineering CollegeM. Tech – CSE – II Year – I Semester

CS8232PE: DIGITAL FORENSICS (Professional Elective - III)

Pre-Requisites: Cybercrime and Information Warfare, Computer Networks

Course Objectives: Provides an in-depth study of the rapidly changing and fascinating field of computer

forensics. Combines both the technical expertise and the knowledge required to

investigate, detect and prevent digital crimes. Knowledge on digital forensics legislations, digital crime, forensics processes and

procedures, data acquisition and validation, e-discovery tools E-evidence collection and preservation, investigating operating systems and

file systems, network forensics, art of steganography and mobile device forensics

Course Outcomes: On completion of the course the student should be able to Understand relevant legislation and codes of ethics. Computer forensics and digital detective and various processes, policies

andprocedures. E-discovery, guidelines and standards, E-evidence, tools and environment. Email and web forensics and network forensics.

UNIT - IDigital Forensics Science: Forensics science, computer forensics, and digital forensics.Computer Crime: Criminalistics as it relates to the investigative process, analysis of cyber-criminalistics area, holistic approach to cyber-forensics

UNIT - IICyber Crime Scene Analysis: Discuss the various court orders etc., methods to search and seizure electronic evidence, retrieved and un-retrieved communications, Discuss the importance of understanding what court documents would be required for a criminal investigation.

UNIT - IIIEvidence Management & Presentation: Create and manage shared folders using operating system, importance of the forensic mindset, define the workload of law enforcement, Explain what the normal case would look like, Define who should be notified of a crime, parts of gathering evidence, Define and apply probable cause.

UNIT - IVComputer Forensics: Prepare a case, Begin an investigation, Understand computer forensics workstations and software, Conduct an investigation, Complete a case, Critique a case,Network Forensics: open-source security tools for network forensic analysis, requirements for preservation of network data.

UNIT - VMobile Forensics: mobile forensics techniques, mobile forensics tools.Legal Aspects of Digital Forensics: IT Act 2000, amendment of IT Act 2008.Recent trends in mobile forensic technique and methods to search and seizure electronic evidence

Text books:1. The Basics of Digital Forensics, John Sammons, Elsevier2. Computer Forensics: Computer Crime Scene Investigation, John Vacca, Laxmi

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1. Digital Forensics, Andre Arnes, Wiley

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CS8233PE: PARALLEL COMPUTING (Professional Elective - III)

Pre-Requisites: Computer Organization & Architecture, Operating Systems, Programming for problem solving

Course Objectives: To introduce the foundations of parallel Computing To learn various parallel computing architectures and programming models To gain knowledge of writing efficient parallel programs

Course Outcomes: Ability to understand the concepts of parallel architectures Ability to select the data structures that efficiently model the information in a

problem. Ability to develop an efficient parallel algorithm to solve it. Ability to implement an efficient and correct code to solve it, analyse its

performanceUNIT - IParallel Computing: Introduction, Motivation and scope - Parallel Programming Platforms – Basic Communication OperationsUNIT - IIPrinciples of Parallel Algorithm Design - Analytical Modelling of Parallel Programs

UNIT - IIIProgramming using Message Passing Paradigm(MPI) – Programming Shared Address Space Platforms (PThreads)UNIT – IVDense Matric Algorithms ( Matrix-Vector Multiplication, Matrix-Matrix Multiplication) – Sorting Algorithms (Issues, Bubble Sort, Quick Sort, Bucket Sort, Enumeration Sort, Radix Sort)UNIT - VGraph Algorithms ( Minimum Spanning Tree: Prim's Algorithm - Single-Source Shortest Paths: Dijkstra's Algorithm ) Search Algorithms ( DFS, BFS)Text Books:

1. Introduction to Parallel Computing, Second Edition, Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta, Addison-Wesley, 2003,

2. Parallel scientific computing in C++ and MPI, George EmKarniadaiks, Robert M. Kirby II,

References:1. Parallel Computing – Theory and Practice, Second Edition, Michaek J. Quinn, Tata

McGraw-Hill Edition.2. Parallel Computers – Architectures and Programming, V. Rajaraman, C. Siva Ram

Murthy,PHI.

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CS8241PE: Ubiquitous and Pervasive Computing (Professional Elective - IV)

Course Objectives: The course will focus on how computing will be used in the future. It is about moving beyond the traditional desktop computing model, into

embedding computing into everyday objects and everyday activities. The vision is that the virtual (computing) space will be seamlessly integrated with

our physical environment, such that we as people cease to take notice of computing artifacts.

Course Outcomes: The student at the end of the course the student will be able to realize software infrastructure for pervasive computing that can support the integration

between our physical space and virtual computing space sensors and sensor network that can capture and disseminate context information context-aware applications that use context information to create intelligent

everyday objects and applications, embedding computing into everyday objects user interfaces for ubiquitous computing security and privacy to protect access to user context information spontaneous interaction where appliances and services can seamlessly interact and

interoperate with each other with little or no prior agreements, and social computing that apply ubiquitous computing techniques and everyday

computing artifacts to improve our social lives.

UNIT IIntroduction: Pervasive computing and its market, Context aware computing, m-business, Challenges, understanding pervasive computing by applications of pervasive computing (Retail, Sales force automation, Healthcare, tracking, car information.

UNIT IIDevice Technology: Hardware, Human-machine interface, Biometrics, OS, Java for pervasive devices.

UNIT IIIDevices and their Connectivity: Protocols, Security, Device management, WAP, voice technology (speech and pervasive computing), personal digital assistants: device categories, PDA OS, Device Characteristics, S/W components, standards.

UNIT IVPervasive Web application architecture: Scalability, availability, Development of pervasive web applications, pervasive application architecture, Middle aware support: (Mobile middleware, Service discovery middleware), Access from PC Access via WAP, Access from PDA, Access via voice

UNIT VSecurity: security and privacy concerns in ubiquitous and pervasive computing

Text Books:1. Pervasive Computing by Jochen BurKhardt, Henn, Hepper etl., Pearson Education2.Fundamentals of mobile and Pervasive Computing by Adelstein, Gupta, Richard,

Schwiebert, TMHReferences:

1. Pervasive computing and networking, Mohammad S. Obaidat, Mieso Denko and Isaac Woungang, Wiley Edition

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CS8242PE: WIRELESS AND SENSOR NETWORKS (Professional Elective - IV)

Pre-Requisites: Computer Networks, Distributed Systems, Mobile Computing

Course Objectives:1. To understand the concepts of sensor networks2. To understand the MAC and transport protocols for adhoc networks3. To understand the security of sensor networks4. To understand the applications of adhoc and sensor networks

Course Outcomes:1. Understanding the state-of-the-art research in emerging subject of ad hoc and

wireless sensor networks (ASN)2. Ability to solve the issues in real-time application development based on ASN3. Ability to conduct further research in the ASN domain

UNIT - IIntroduction to Ad Hoc Networks - Characteristics of MANETs, Applications of MANETs and Challenges of MANETs.Routing in MANETs - Criteria for classification, Taxonomy of MANET routing algorithms, Topology-based routing algorithms-Proactive: DSDV, WRP, Reactive: DSR, AODV, TORA, Hybrid: ZRP, Position-based routing algorithms-Location Services-DREAM, Quorum-based, GLS, Forwarding Strategies: Greedy Packet, Restricted Directional Flooding-DREAM, LAR, Other routing algorithms-QoS Routing, CEDAR.

UNIT - IIData Transmission - Broadcast Storm Problem, Rebroadcasting Schemes-Simple-flooding, Probability- based Methods, Area-based Methods, Neighbour Knowledge-based: SBA, Multipoint Relaying, AHBP. Multicasting: Tree-based: AMRIS, MAODV, Mesh-based: ODMRP, CAMP, Hybrid: AMRoute, MCEDAR and Geocasting: Data-transmission Oriented- LBM, Route Creation Oriented-GeoTORA, MGR.

UNIT - IIITCP over Ad Hoc TCP protocol overview, TCP and MANETs, Solutions for TCP over Ad hoc Basics of Wireless, Sensors and Applications, Classification of sensor networks, Architecture of sensor network, Physical layer, MAC layer, Link layer.

UNIT - IVData Retrieval in Sensor Networks, Routing layer, Transport layer, High-level application layer support, Adapting to the inherent dynamic nature of WSNs, Sensor Networks and mobile robots.

UNIT - VSecurity - Security in Ad Hoc networks, Key management, Secure routing, Cooperation in MANETs, Intrusion Detection systems.

Text Books:1. Ad Hoc and Sensor Networks – Theory and Applications, Carlos Corderio Dharma

P.Aggarwal, World Scientific Publications, March 20062. Wireless Sensor Networks: An Information Processing Approach, Feng Zhao,

Leonidas Guibas, Elsevier Science,( Morgan Kauffman)References:

1. Fundamentals of wireless sensor networks, Waltenegus Dargie and Christian Poelabaer, Wiley

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CS8243PE: OPTIMIZATION TECHNIQUES (Professional Elective - IV)

Pre-Requisites: Mathematics

Course Objectives:1. This course explains various optimization problems and the techniques

to address those problems.2. To study Linear Programming, dynamic programming and optimization

Techniques etc.3. To understand the theory of games.

Course Outcomes:1. Gain the knowledge of optimization techniques2. Get the skill to apply Optimization techniques to address the real time

problems .

UNIT – IMODELS : Definition, Characteristics and Phases, Types of models, Operations Research models applications.ALLOCATION: Linear Programming Problem Formulation, Graphical solution, Simplex method, Artificial variables techniques: Two–phase method, Big-M method, Duality Principle.

UNIT – IITRANSPORTATION PROBLEM: Formulation, Optimal solution, unbalanced transportation problem degeneracy,ASSIGNMENT PROBLEM: Formulation, Optimal solution, Variants of Assignment Problem, Traveling Salesman problem.

UNIT - IIISEQUENCING: Introduction, Flow Shop sequencing, n jobs through two machines, n jobs through three machines, Job shop sequencing, two jobs through ‘m’ machinesREPLACEMENT: Introduction Replacement of items that deteriorate with time when money value is not counted and counted, Replacement of items that fail completely, Group Replacement.

UNIT - IVTHEORY OF GAMES: Introduction, Terminology, Solution of games with saddle points and without saddle points, 2 x 2 games, m x 2 & 2 x n games, graphical method, m x n games, dominance principle.INVENTORY: Introduction, Single item, Deterministic models – Types, Purchase inventory models with one price break and multiple price breaks, Stochastic models – demand discrete variable or continuous variable, Single Period model with no setup cost.

UNIT - VWAITING LINES: Introduction, terminology, Single Channel , Poisson arrivals and Exponential Service times with infinite population and finite population models, Multichannel Poisson arrivals and exponential service times with infinite population.DYNAMIC PROGRAMMING: Introduction, terminology, Bellman’s Principle of Optimality – Applications of dynamic programming, shortest path problem, linear programming problem.

TEXT BOOKS :1. Operation Research J. K. Sharma, MacMilan.2. Introduction to O.R Taha, PHI

References:1. Operations Research: Methods and Problems Maurice Saseini, Arhur Yaspan and

Lawrence Friedman

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3. Operations Research, Wagner, PHI Publications.4. Introduction to O.R, Hillier & Libermann, (TMH).

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CS8203PC: ADVANCED ALGORITHMS LAB (Lab III)

Course Objective: The student can able to attain knowledge in advance algorithms.

Course Outcomes: The student can able to analyze the performance of algorithms

List of Experiments1. Implement assignment problem using Brute Force method2. Perform multiplication of long integers using divide and conquer method.3. Implement solution for knapsack problem using Greedy method.4. Implement Gaussian elimination method.5. Implement LU decomposition6. Implement Warshall algorithm7. Implement Rabin Karp algorithm.8. Implement KMP algorithm.9. Implement Harspool algorithm10. Implement max-flow problem.

Text Book:1. Design and Analysis of Algorithms, S.Sridhar, OXFORD University Press

References:1. Introduction to Algorithms, second edition, T.H. Cormen, C.E. Leiserson, R.L.

Rivest and C.Stein,PHI Pvt. Ltd./ Pearson Education.2. Fundamentals of Computer Algorithms, Ellis Horowitz, Satraj Sahni and

Rajasekharam, Universities Press.3. Design and Analysis of algorithms, Aho, Ullman and Hopcroft, Pearson education.

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CS8231PEL: BIG DATA ANALYTICS LAB

Prerequisites: Java Programming, Python Programming

Course Objectives: This course is introduced to Provide the knowledge to setup a Hadoop Cluster. Impart knowledge to develop programs using MapReduce. Discuss Pig, PigLatin and HiveQL to process big data. Familiarize with NoSQL databases. Present latest big data frameworks and applications using Spark andScala. Integrate Hadoop with R (RHadoop) to process and visualize.

Course Outcomes: Upon successful completion of this course, student will beable to Understand Hadoop working environment. Work with big data applications in multi node clusters. Apply big data and echo system techniques for real world problems. Write scripts using Pig to solve real world problems. Write queries using Hive to analyze the datasets Understand spark working environment.

List of Programs:1. Understanding and using basic HDFS commands2. Word count application using Mapper Reducer on single node cluster3. Analysis of Weather Dataset on Multi node Cluster using Hadoop4. Real world case studies on Map Reduce applications5. Working with files in Hadoop file system: Reading, Writing and Copying6. Writing User Defined Functions/Eval functions for filtering unwanted data in Pig7. Working with Hive QL8. Writing User Defined Functions in Hive9. Understanding the processing of large dataset on Spark framework. 10.Integrating Hadoop with other data analytic framework like RText

Text Books:1. Hadoop: The Definitive Guide, Tom White, 4th Edition, O’Reilly Inc,2015.2. Hadoop Real-World Solutions Cookbook, Tanmay Deshpande, 2ndEdition, Packt

Publishing, 2016.

References:1. Programming Hive, Edward Capriolo, Dean Wampler, and Jason Rutherglen, O’Reilly

Inc, 2012.2. Big data Analytics with R and Hadoop, Vignesh Prajapati, Packt Publishing, 2013.3. https://parthgoelblog.wordpress.com/tag/hadoop-installation4. http://www.iitr.ac.in/media/facspace/patelfec/16Bit/index.html5. https://class.coursera.org/datasci-001/lecture6. http://bigdatauniversity.com

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CS8232PEL: DIGITAL FORENSICS LAB

Course Objectives: To provide students with a comprehensive overview of collecting, investigating,

preserving, and presenting evidence of cybercrime left in digital storage devices, emails, browsers, mobile devices using different Forensics tools

To Understand file system basics and where hidden files may lie on the disk, as well as how to extract the data and preserve it for analysis.

Understand some of the tools of e-discovery. To understand the network analysis, Registry analysis and analyse attacks

using different forensics tools

Course Outcomes: Learn the importance of a systematic procedure for investigation of data found on

digital storage media that might provide evidence of wrong-doing To Learn the file system storage mechanisms and retrieve files in hidden format Learn the use of computer forensics tools used in data analysis. Learn how to find data that may be clear or hidden on a computer disk, find out the

open ports for the attackers through network analysis, Registry analysis.

List of Experiments1. Perform email analysis using the tools like Exchange EDB viewer, MBOX viewer and

View user mailboxes and public folders, Filter the mailbox data based on various criteria, Search for particular items in user mailboxes and public folders

2. Perform Browser history analysis and get the downloaded content, history, saved logins, searches, websites visited etc using Foxton Forensics tool, Dumpzilla .

3. Perform mobile analysis in the form of retrieving call logs, SMS log, all contacts list using the forensics tool like SAFT

4. Perform Registry analysis and get boot time logging using process monitor tool5. Perform Disk imaging and cloning the using the X-way Forensics tools6. Perform Data Analysis i.e History about open file and folder, and view folder

actions using Last view activity tool7. Perform Network analysis using the Network Miner tool .8. Perform information for incident response using the crowd Response tool9. Perform File type detection using Autospy tool10. Perform Memory capture and analysis using the Live RAM capture or any forensic tool

Text books:1. Digital Forensics with Open Source Tools, Cory Altheide , Harlan Carvey, Syngress

Reference Books:1. Digital Forensics basics, A practical guide using windows os, Nihad A Hassan, Apress

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CS8233PEL: PARALLEL COMPUTING LAB

Pre-Requisites: Computer Organization & Architecture, Operating Systems, Programming for problem solving

Course Objectives: To introduce the foundations of parallel Computing To learn various parallel computing architectures and programming models To gain knowledge of writing efficient parallel programs

Course Outcomes Ability to understand the concepts of parallel architectures Ability to select the data structures that efficiently model the information in a

problem. Ability to develop an efficient parallel algorithm to solve it. Ability to implement an efficient and correct code to solve it, analyze its performance

List of Programs1. Design a parallel program to implement Matrix-Vector and Matrix-Matrix

Multiplication using MPI library.2. Design a parallel program to implement Bubble Sort using OpenMP and Pthread

Programming Constructs.3. Design a parallel program to implement Quick Sort using OpenMP and Pthread

Programming Constructs.4. Design a parallel program to implement Bucket Sort using OpenMP and Pthread

Programming Constructs.5. Design a parallel program to implement Prim's Algorithm using OpenMP and

Pthread Programming Constructs.6. Design a parallel program to implement DFS Algorithm using OpenMP and Pthread

Programming Constructs.7. Design a parallel program to implement BFS Algorithm using OpenMP and Pthread

Programming Constructs.8. Design a parallel program to implement Dijkstra's Algorithm using MPI library.

Text books:1. Parallel Programming in C with MPI and OpenMP Michael J, Quinn, Mc Graw Hill

Reference Books:1. Parallel Scientific Computing in C++ and MPI: A Seamless Approach to Parallel

Algorithms and their Implementation, George Em Karniadakis, Robert M. Kirby II, Cambridge University Press

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CS8251PE: DEEP LEARNING (Professional Elective -

V)

Pre-Requisites: Machine Learning

Course Objectives: To introduce the foundations of Artificial Neural Networks To acquire the knowledge on Deep Learning Concepts To learn various types of Artificial Neural Networks To gain knowledge to apply optimization strategies

Course Outcomes: Ability to understand the concepts of Neural Networks Ability to select the Learning Networks in modeling real world systems Ability to use an efficient algorithm for Deep Models Ability to apply optimization strategies for large scale applications

UNIT , IDeep Feedforward Networks: Example: Learning XOR, Gradient-Based Learning, Hidden Units, Architecture Design, Back-Propagation and Other Differentiation Algorithms, Historical Notes

UNIT , IIRegularization for Deep Learning:Parameter Norm Penalties, Norm Penalties as Constrained Optimization, Regularization and Under, Constrained Problems, Dataset Augmentation, Noise Robustness, Semi-Supervised Learning, Multi, Task Learning, Early Stopping, Parameter Tying and Parameter Sharing, Sparse Representations, Bagging and Other Ensemble Methods, Dropout, Adversarial Training, Tangent Distance, Tangent Prop, and Manifold Tangent Classifier.

UNIT , IIIOptimization for Training Deep Models, How Learning Differs from Pure Optimization, Challenges in Neural Network Optimization, Basic Algorithms, Parameter Initialization Strategies, Algorithms with Adaptive Learning Rates, Approximate Second-Order Methods, Optimization Strategies and Meta, Algorithms

UNIT , IVConvolutional Networks:The Convolution Operation, Motivation, Pooling, Convolution and Pooling as an Infinitely Strong Prior, Variants of the Basic Convolution Function, Structured Outputs, Data Types, Efficient Convolution Algorithms, Random or Unsupervised Features, The Neuro-scientific Basis for Convolutional Networks, Convolutional Networks and the History of Deep Learning

UNIT , VApplications:Large-Scale Deep Learning, Computer Vision, Speech Recognition, Natural Language Processing, Other Applications

Text Book:1. Deep Learning (Adaptive Computation and Machine Learning series), Goodfellow,

Yoshua Bengio, Aaron Courville, MIT Press.

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Reference Books:1. Deep Learning Methods and Applications, Foundations and Trends® in Signal

Processing, Li Deng and Dong Yu, Volume 7 Issues 3-4, ISSN: 1932-8346.2. Deep Learning Made Easy with R A Gentle Introduction for Data Science, Dr.

N.D. Lewis, Create Space Independent Publishing Platform (January 10, 2016).3. Deep Learning with R Version 1, François Chollet, JJ Allaire, MEAP Edition

Manning Early Access Program Copyright 2017, Manning Publications.

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CS8253PE: BLOCKCHAIN TECHNOLOGY (Professional Elective - V)

Pre-Requisites: Data structures

Course Objectives1. Gain knowledge on Blockchain Fundamentals and Working Principle2. Understand the basic concept of Cryptographic Hash Functions, Hash Pointers3. Learn Elliptic Curve Digital Signature Algorithm.4. Get an insight into the working of the Bitcoin network, wallet, Bitcoin mining

and distributed consensus for reliability.5. Gain knowledge about Bitcoin storage, Transaction and Usage6. Be familiar with Bitcoin Mining Hardware, Pools, strategies and basics of Anonymity.

Course Outcomes: After completion of this course, the student will be able to1. learn Blockchain Fundamentals and Working Principle.2. apply Cryptographic Hash Functions, Hash Pointers.3. implement Elliptic Curve Digital Signature Algorithm.4. work on Bitcoin network, wallet, Bitcoin mining and distributed consensus for reliability.5. use Bitcoin Transaction, Payment Services and Exchange Market Services.6. design Bitcoin Mining Hardware, Pools and strategies.

UNIT IBlock Chain Fundamentals: Tracing Blockchain’s Origin, Revolutionizing the Traditional Business Network, How Blockchain Works, What Makes a Blockchain Suitable for Business? Introduction to Cryptography: Cryptographic Hash Functions, SHA256, Hash Pointers and Data Structures, Merkle tree.

UNIT IIDigital Signatures: Elliptic Curve Digital Signature Algorithm (ECDSA), Public Keys as Identities, A Simple Crypto currency. Learning

UNIT IIICentralization vs. Decentralization, Distributed Consensus, Consensus without identity using a block chain, Incentives and proof of work. Mechanics of Bitcoin: Bitcoin transactions, Bitcoin Scripts, Applications of Bitcoin scripts, Bitcoin blocks, The Bitcoin network.

UNIT IVStorage of and Usage of Bitcoins: Simple Local Storage, Hot and Cold Storage, Splitting and Sharing Keys, Online Wallets and Exchanges, Payment Services, Transaction Fees, Currency Exchange Markets.

UNIT VBitcoin Mining: The Task of Bitcoin miners, Mining Hardware, Mining pools, Mining incentives and strategies. Bitcoin and Anonymity: Anonymity Basics, Mixing, Zerocoin and

Text Books:1. Blockchain for dummies, Manav Gupta, 2nd IBM Limited Edition, Published by John

Wiley & Sons, Inc, 2018.2. Bitcoin and Cryptocurrency Technologies, Arvind Narayanan, Joseph Bonneau,

Edward Felten, Andrew Miller and Steven Goldfeder, 2016.

References:

1. Blockchain: Blueprint for a New Economy, Melanie Swan, O'Reilly Media, 1/e, 2015.2. Mastering Bitcoin: Programming the Open Blockchain, Andreas M. Antonopoulos,

O'Reilly, 2/e, 2017.

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CMR Engineering CollegeM. Tech – CSE

ENGLISH FOR RESEARCH PAPER WRITING (Audit Course , I & II)

Prerequisite: NoneCourse objectives: Students will be able to:

Understand that how to improve your writing skills and level of readability Learn about what to write in each section Understand the skills needed when writing a Title Ensure the good quality of

paper at very first, time submission

UNIT-I:Planning and Preparation, Word Order, breaking up long sentences, Structuring Paragraphs and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness

UNIT-II:Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticizing, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction

UNIT-III:Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

UNIT-IV:key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are needed when writing an Introduction, skills needed when writing a Review of the Literature,

UNIT-V:skills are needed when writing the Methods, skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions. useful phrases, how to ensure paper is as good as it could possibly be the first, time submission

TEXT BOOKS/ REFERENCES:1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google

Books)2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.

Highman’s book.4. Adrian Wallwork, English for Writing Research Papers, Springer New York

Dordrecht Heidelberg London, 2011

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DISASTER MANAGEMENT (Audit Course , I & II)

Prerequisite: NoneCourse Objectives: Students will be able to learn to demonstrate a critical understanding of key concepts in disaster risk

reduction and humanitarian response. critically evaluate disaster risk reduction and humanitarian response policy and

practice from multiple perspectives. develop an understanding of standards of humanitarian response and practical

relevance in specific types of disasters and conflict situations. critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, particularly their home country or

the countries they work in

UNIT-I:Introduction:Disaster: Definition, Factors and Significance, Difference Between Hazard and Disaster, Natural and Manmade Disasters: Difference, Nature, Types and Magnitude.Disaster Prone Areas in India:Study of Seismic Zones, Areas Prone to Floods and Droughts, Landslides and Avalanches, Areas Prone to Cyclonic and Coastal Hazards with Special Reference to Tsunami, Post-Disaster Diseases and Epidemics

UNIT-II:Repercussions of Disasters and Hazards:Economic Damage, Loss of Human and Animal Life, Destruction of Ecosystem. Natural Disasters: Earthquakes, Volcanisms, Cyclones, Tsunamis, Floods, Droughts and Famines, Landslides and Avalanches, Man-made disaster: Nuclear Reactor Meltdown, Industrial Accidents, Oil Slicks and Spills, Outbreaks of Disease and Epidemics, War and Conflicts.

UNIT-III:Disaster Preparedness and Management:Preparedness: Monitoring of Phenomena Triggering A Disaster or Hazard, Evaluation of Risk: Application of Remote Sensing, Data from Meteorological and Other Agencies, Media Reports: Governmental and Community Preparedness.

UNIT-IV:Risk Assessment Disaster Risk:Concept and Elements, Disaster Risk Reduction, Global and National Disaster Risk Situation. Techniques of Risk Assessment, Global Co-Operation in Risk Assessment and Warning, People’s Participation in Risk Assessment. Strategies for Survival.

UNIT-V:Disaster Mitigation:Meaning, Concept and Strategies of Disaster Mitigation, Emerging Trends In Mitigation. Structural Mitigation and Non-Structural Mitigation, Programs of Disaster Mitigation in India.

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TEXT BOOKS/ REFERENCES:1. R. Nishith, Singh AK, “Disaster Management in India: Perspectives, issues and

strategies “’New Royal book Company.2. Sahni, Pardeep Et. Al. (Eds.),” Disaster Mitigation Experiences and Reflections”,

Prentice Hall of India, New Delhi.3. Goel S. L., Disaster Administration and Management Text and Case Studies”,

Deep &Deep Publication Pvt. Ltd., New Delhi.

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SANSKRIT FOR TECHNICAL KNOWLEDGE (Audit Course , I & II)

Prerequisite: NoneCourse Objectives:

To get a working knowledge in illustrious Sanskrit, the scientific language in the world Learning of Sanskrit to improve brain functioning Learning of Sanskrit to develop the logic in mathematics, science & other

subjects enhancing the memory power The engineering scholars equipped with Sanskrit will be able to explore the

huge knowledge from ancient literature

Course Outcomes: Students will be able to Understanding basic Sanskrit language Ancient Sanskrit literature about science & technology can be understood Being a logical language will help to develop logic in students

UNIT-I:Alphabets in Sanskrit,

UNIT-II:Past/Present/Future Tense, Simple Sentences

UNIT-III:Order, Introduction of roots,

UNIT-IV:Technical information about Sanskrit Literature

UNIT-V:Technical concepts of Engineering-Electrical, Mechanical, Architecture, Mathematics

TEXT BOOKS/ REFERENCES:1. “Abhyaspustakam” – Dr. Vishwas, Samskrita-Bharti Publication, New Delhi2. “Teach Yourself Sanskrit” Prathama Deeksha-Vempati Kutumbshastri,

Rashtriya Sanskrit Sansthanam, New Delhi Publication3. “India’s Glorious Scientific Tradition” Suresh Soni, Ocean books (P) Ltd., New Delhi.

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VALUE EDUCATION (Audit Course , I & II)

Prerequisite: NoneCourse Objectives: Students will be able to

Understand value of education and self, development Imbibe good values in students Let the should know about the importance of character

Course outcomes: Students will be able to Knowledge of self-development Learn the importance of Human values Developing the overall personality

UNIT-I:Values and self-development –Social values and individual attitudes. Work ethics, Indian vision of humanism. Moral and non, moral valuation. Standards and principles. Value judgements

UNIT-II:Importance of cultivation of values. Sense of duty. Devotion, Self-reliance. Confidence, Concentration. Truthfulness, Cleanliness. Honesty, Humanity. Power of faith, National Unity. Patriotism. Love for nature, Discipline

UNIT-III:Personality and Behavior Development , Soul and Scientific attitude. Positive Thinking. Integrity and discipline, Punctuality, Love and Kindness.

UNIT-IV:Avoid fault Thinking. Free from anger, Dignity of labour. Universal brotherhood and religious tolerance. True friendship. Happiness Vs suffering, love for truth. Aware of self-destructive habits. Association and Cooperation. Doing best for saving nature

UNIT-V:Character and Competence –Holy books vs Blind faith. Self-management and Good health. Science of reincarnation, Equality, Nonviolence, Humility, Role of Women. All religions and same message. Mind your Mind, Self-control. Honesty, Studying effectively

TEXT BOOKS/ REFERENCES:1. Chakroborty, S.K. “Values and Ethics for organizations Theory and practice”,

Oxford University Press, New Delhi

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Prerequisite: None

CONSTITUTION OF INDIA(Audit Course , I & II)

Course Objectives: Students will be able to: Understand the premises informing the twin themes of liberty and freedom from a

civil rights perspective. To address the growth of Indian opinion regarding modern Indian intellectuals’

constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism.

To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

Course Outcomes: Students will be able to: Discuss the growth of the demand for civil rights in India for the bulk of Indians

before the arrival of Gandhi in Indian politics. Discuss the intellectual origins of the framework of argument that informed the

conceptualization of social reforms leading to revolution in India. Discuss the circumstances surrounding the foundation of the Congress Socialist

Party [CSP] under the leadership of Jawaharlal Nehru and the eventual failure of the proposal of direct elections through adult suffrage in the Indian Constitution.

Discuss the passage of the Hindu Code Bill of 1956.

UNIT-I:History of Making of the Indian Constitution: History Drafting Committee, (Composition & Working),Philosophy of the Indian Constitution: Preamble, Salient Features.

UNIT-II:Contours of Constitutional Rights & Duties: Fundamental Rights Right to Equality, Right to Freedom, Right against Exploitation, Right to Freedom of Religion, Cultural and Educational Rights, Right to Constitutional Remedies, Directive Principles of State Policy, Fundamental Duties.

UNIT-III:Organs of Governance: Parliament, Composition, Qualifications and Disqualifications, Powers and Functions, Executive, President, Governor, Council of Ministers, Judiciary, Appointment and Transfer of Judges, Qualification, Powers and Functions.

UNIT-IV:Local Administration: District’s Administration head: Role and Importance, Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. Pachayati raj: Introduction, PRI: Zila Pachayat. Elected officials and their roles, CEO Zila Pachayat: Position and role. Block level: Organizational Hierarchy (Different departments), Village level: Role of Elected and Appointed officials, Importance of grass root democracy.

UNIT-V:Election Commission: Election Commission: Role and Functioning. Chief Election Commissioner and Election Commissioners. State Election Commission: Role and Functioning. Institute and Bodies for the welfare of SC/ST/OBC and women.

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TEXT BOOKS/ REFERENCES:1. The Constitution of India, 1950 (Bare Act), Government Publication.2. Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015.3. M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.4. D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.

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PEDAGOGY STUDIES (Audit Course , I & II)

Prerequisite: NoneCourse Objectives: Students will be able to:

Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers.

Identify critical evidence gaps to guide the development.

Course Outcomes: Students will be able to understand: What pedagogical practices are being used by teachers in formal and informal

classrooms in developing countries? What is the evidence on the effectiveness of these pedagogical practices, in

what conditions, and with what population of learners? How can teacher education (curriculum and practicum) and the school curriculum

and guidance materials best support effective pedagogy?

UNIT-I:Introduction and Methodology: Aims and rationale, Policy background, Conceptual framework and terminology Theories of learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of methodology and Searching.

UNIT-II:Thematic overview: Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries. Curriculum, Teacher education.

UNIT-III:Evidence on the effectiveness of pedagogical practices, Methodology for the indepth stage: quality assessment of included studies. How can teacher education (curriculum and practicum) and the scho curriculum and guidance materials best support effective pedagogy? Theory of change. Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers’ attitudes and beliefs and Pedagogic strategies.

UNIT-IV:Professional development: alignment with classroom practices and follow-up support, Peer support, Support from the head teacher and the community. Curriculum and assessment, Barriers to learning: limited resources and large class sizes

UNIT-V:Research gaps and future directions: Research design, Contexts, Pedagogy, Teacher education, Curriculum and assessment, Dissemination and research impact.

TEXT BOOKS/ REFERENCES:1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools,

Compare, 31 (2): 245-261.2. Agrawal M (2004) Curricular reform in schools: The importance of

evaluation, Journal of Curriculum Studies, 36 (3): 361-379.3. Akyeampong K (2003) Teacher training in Ghana , does it count? Multi-site

teacher education research project (MUSTER) country report 1. London: DFID.

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4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282.

5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell.

6. Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.7. www.pratham.org/images/resource%20working%20paper%202.pdf.

R20 M.TECH. 5


STRESS MANAGEMENT BY YOGA (Audit Course , I & II)


To achieve overall health of body and mind To overcome stress

Course Outcomes: Students will be able to: Develop healthy mind in a healthy body thus improving social health also Improve efficiency

UNIT-I:Definitions of Eight parts of yog. (Ashtanga)

UNIT-II:Yam and Niyam.

UNIT-III:Do`s and Don’t’s in life.i) Ahinsa, satya, astheya, bramhacharya and aparigrahaii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan

UNIT-IV:Asan and Pranayam

UNIT-V:i) Various yog poses and their benefits for mind & bodyii)Regularization of breathing techniques and its effects-Types of pranayam

TEXT BOOKS/ REFERENCES:1. ‘Yogic Asanas for Group Tarining-Part-I”: Janardan Swami Yogabhyasi Mandal, Nagpur 2.“Rajayoga or conquering the Internal Nature” by Swami Vivekananda, Advaita Ashrama

(Publication Department), Kolkata

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PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS(Audit Course , I & II)


To learn to achieve the highest goal happily To become a person with stable mind, pleasing personality and determination To awaken wisdom in students

Course Outcomes: Students will be able to Study of Shrimad-Bhagwad-Geeta will help the student in developing his

personality and achieve the highest goal in life The person who has studied Geeta will lead the nation and mankind to peace and

prosperity Study of Neetishatakam will help in developing versatile personality of students

UNIT-I:Neetisatakam-Holistic development of personality

Verses, 19,20,21,22 (wisdom) Verses, 29,31,32 (pride & heroism) Verses, 26,28,63,65 (virtue)

UNIT-II:Neetisatakam-Holistic development of personality

Verses, 52,53,59 (dont’s) Verses, 71,73,75,78 (do’s)

UNIT-III:Approach to day to day work and duties.

Shrimad Bhagwad Geeta: Chapter 2-Verses 41, 47,48, Chapter 3-Verses 13, 21, 27, 35, Chapter 6-Verses 5,13,17, 23, 35, Chapter 18-Verses 45, 46, 48.

UNIT-IV:Statements of basic knowledge.

Shrimad Bhagwad Geeta: Chapter2-Verses 56, 62, 68 Chapter 12 -Verses 13, 14, 15, 16,17, 18 Personality of Role model. Shrimad Bhagwad Geeta:

UNIT-V: Chapter 2-Verses 17, Chapter 3-Verses 36,37,42, Chapter 4-Verses 18, 38,39 Chapter18 – Verses 37,38,63

TEXT BOOKS/ REFERENCES:1. “Srimad Bhagavad Gita” by Swami Swarupananda Advaita Ashram (Publication

Department), Kolkata.2. Bhartrihari’s Three Satakam (Niti-sringar-vairagya) by P.Gopinath,

Rashtriya Sanskrit Sansthanam, New Delhi.

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