Syllabus for
Master of Technology (Information Technology)
Academic Year  (2019)

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

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Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness

Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction

Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

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

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

SUBJECT DESCRIPTION:

During the seminar session each student is expected to prepare and present a topic on engineering / technology, it is designed to:

     Review and increase their understanding of the specific topics tested.

     Improve their ability to communicate that understanding to the grader.

     Increase the effectiveness with which they use the limited examination time.

SUBJECT OBJECTIVE:

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn.

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines.

The course will broadly cover the following aspects:

    Teaching skills

    Laboratory skills and other professional activities

    Research methodology

For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal.

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline.

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Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.

In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. Each student will present 3- 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week.

Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography.

A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student.

The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student.

A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated.

Wherever  necessary  and  feasible,  the  panel  coordinator  in  consultation  with  the concerned  group may also seek participation of the faculty members from other groups in lectures and comprehensive viva.

Mid semester report and final evaluation report should be submitted in the 9^th week and 15^th week of the semester respectively. These should contain the following sections:

●        Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIA-I) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material.

●        Section (B): Laboratory experiments reports and professional work report.

●        Section (C): Research proposal with detailed references and bibliography in a standard format.

Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time.

The aim of the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research.

1. Explain the principles and concepts of research methodology.

2. understand the different methods of data collection

3. Apply appropriate method of data collection and analyze using statistical/software tools.

4. Present research output in a structured report as per the technical and ethical standards.

5. Create research design for a given engineering and management problem /situation.

Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan.

Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis.

Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external.

Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools.

Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables.

2.        Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

3.     .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide an empirical evidence and the scientific approach applying Artificial Intelligence techniques for problem solving using probabilistic, fuzzy, statistical and Deep Learning Models.

Intelligent Agents – Agents and environments - Good behaviour – The nature of environments – structure of agents - Problem Solving agents – Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited.

Representing knowledge in an uncertain domain; – The semantics of Bayesian networks; Efficient representation of conditional distributions; – Inference in Bayesian networks; Approximate inference in Bayesian Networks; – Extending probability to first-order representations; Other approaches to Uncertain Reasoning. – Time and uncertainty; Inference in temporal models; – Hidden Markov models; – Kalman filters; Dynamic Bayesian Networks.

Hopfield Networks; Learning in Neural Network –  Application –  Recurrent Network; – Distributed Representation – Fuzzy logic system Introduction. Crisp Sets–  Fuzzy Sets and Terminologies –  Fuzzy Logic Control –  Sugeno Style of Fuzzy Inference – Fuzzy Hedges. Alpha cut Threshold – Neuro Fuzzy Systems.

Learning from observations - forms of learning - Inductive learning - Learning decision trees - Ensemble learning - Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming - Statistical learning methods - Learning with complete data - Learning with hidden variable - EM algorithm - Instance based learning - Reinforcement learning – Passive reinforcement learning - Active reinforcement learning - Generalization in reinforcement learning.

Convolutional Neural Networks, Motivation, Convolution operations, Pooling,  Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures, Tuning/Debugging Neural Networks, Parameter search, Overfitting, Visualizations, Pretrained Models

 Rich and Kevin Knight, “Artificial Intelligence”, 3^rd Edition, Tata McGraw-Hill, 2012.

       Francois Chollet “Deep Learning with Python”, 1^st Edition Manning Publication, 2018

       George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, 6^th Edition, Pearson Education / PHI, 2009.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Data mining is one of the most advanced fields of Computer Science and Engineering. This field makes use of the applications of Mathematics, Statistics and Information Technology in discovering and prediction of new information and knowledge from largely available data. It is a new evolving interdisciplinary area of research and development which has created interest among scientists of various disciplines like Computer Science, Mathematics, Statistics, and Information Technology and so on. This course titled, “Advanced Data Mining,” involves learning a collection of techniques for extracting and discovering new patterns and trends in large amounts of data. This course will also provide a hands-on introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management.

A multidimensional Data Model, Data preprocessing, Data cleaning, Data integration and Transformation, Correlation analysis and Data Reduction

Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Data Matrix versus Dissimilarity Matrix, Proximity Measures for Nominal Attributes, Binary Attributes, Numeric Data, Ordinal Attributes,Dissimilarity for Attributes of Mixed Types.

Mining Frequent Patterns-basic concepts-apriori principle, Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Mining High-Dimensional Data and Colossal Patterns.

Bayesian Belief Networks, Classification by Backpropagation, Support Vector Machines, kNearest-Neighbour Classifiers, Genetic Algorithms, Rough Set Approach, Fuzzy Set, Model Evaluation and Selection, Approaches, Techniques to Improve Classification Accuracy.

k-Means: A Centroid-Based Technique, k-Medoids, Hierarchical Methods, Probabilistic Model-Based Clustering, Clustering High-Dimensional Data, Clustering Graph and Network Data, Evaluation of Clustering.

Proximity-Based Methods, and Clustering-Based Methods, Outlier Detection in HighDimensional Data.

Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.

2.      Mohammed J.Zaki, Wagneermeira, “Data Mining and Analysis: Fundamental concepts and algorithms”, First Edition, Cambridge University Press India, 2015.

Ian H. Witten, &Eibe Frank, “Data Mining –Practical Machine Learning Tools and Techniques”, 3rd Edition, Elesvier, 2011.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Proximity-Based Methods, and Clustering-Based Methods, Outlier Detection in HighDimensional Data.

Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.

The Engineering Method andStatistical Thinking, Probability, Sample space and events, Interpretation of probability, Conditional probability, multiplication and total probability, Bayes theorem, Random variables, Discrete Random Variables, Probability Distributions and probability mass functions, Cumulative DistributionFunctions, Mean and Variance of a Discrete, Random Variable, Binomial distribution

Continuous Random Variables, Probability Distributions and Probability Density Functions, Cumulative Distribution Functions, Mean and Variance of a Continuous Random Variable,  Continuous Uniform Distribution, Normal Distribution, Normal Approximation to the Binomial and Poisson Distributions, Exponential Distribution, Weibull Distribution

Two Discrete Random Variables, Multiple Discrete Random Variables, Two Continuous Random Variables, Multiple Continuous Random Variables, Covariance and Correlation, Bivariate Normal Distribution, Linear Combinations of Random Variables, 

Data Summary and Display, Random Sampling, Stem-and-Leaf Diagrams, Frequency Distributions and Histograms, Box Plots, Time Sequence Plots, Probability Plots, Introduction to Statistical Intervals for a Single Sample, Confidence Interval on the Mean of a Normal Distribution, Variance Known, Confidence Interval on the Mean of a Normal Distribution, Variance Unknown, Confidence Interval on the Variance and Standard Deviation of a Normal

Distribution, A Large-Sample Confidence Interval for a Population Proportion,  A Prediction Interval for a Future Observation

Hypothesis Testing, Tests on the Mean of a Normal Distribution, Variance Known, Tests on the Mean of a Normal Distribution, Variance Unknown, Tests on the Variance and Standard Deviation of a Normal Distribution, Tests on a Population Proportion, Inference For a Difference in Means of Two Normal Distributions, Variances Known, Inference For a Difference in Means of Two Normal Distributions, Variances Unknown, Inference on the Variances of Two Normal Distributions

NinaZumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014. 

  Ronald E Walpole, Raymond H Myers, Sharon L Myers, Keying E Ye, “Probability and Statistics for Engineers and Scientists”, Ninth Edition, Pearson Education, 2013

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

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Study of all SQL commands

2.     Implementation of PL/SQL Programs.

3.     Implementation of Cursor, Trigger.

4.     Implement the inventory control system with a reorder level

5.     Develop a package for a bank to maintain its customer details

6.     Develop a package for the payroll of a company

7.     Implementation of IEEE/ACM paper

8.     Implementation of Data Science Application Problems

9.      Learning SPSS tool to implement research based concepts

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

Data mining is one of the most advanced fields of Computer Science and Engineering. This field makes use of the applications of Mathematics, Statistics and Information Technology in discovering and prediction of new information and knowledge from largely available data. It is a new evolving interdisciplinary area of research and development which has created interest among scientists of various disciplines like Computer Science, Mathematics, Statistics, and Information Technology and so on. This course titled, “Advanced Data Mining,” involves learning a collection of techniques for extracting and discovering new patterns and trends in large amounts of data. This course will also provide a hands-on introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management.

1.        Introduction to data mining tools

2.        Analysis of the various datasets by using frequent pattern mining algorithms

3.        Analysis of the various datasets by using clustering algorithms

4.        Analysis of the various datasets by using classifier algorithms

5.    Analysis of the various datasets by using outlier detection algorithms 

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

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Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables, Normalization

Structure Query Language, Creating Tables, Modifying Structure of table , The SELECT statement, DELETE and UPDATE, Smart table design , ALTER 

Advance Select , Multi-table database design, Joins and Multi-table operations, sub queries, constraints, views and transactions , security

Distributed Databases Vs. Conventional Databases-Architecture-Fragmentation-Query Processing-Transaction Processing-Concurrency Control-Recovery.

Introduction to Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Implementation, Data Warehousing to Data Mining, KDD process.

Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Students will be able to:

 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective.

 2. 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.

 3. 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.

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Philosophy of the Indian Constitution: Preamble Salient Features, ∙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.

Organs of Governance: ∙ Parliament ∙ Composition ∙ Qualifications and Disqualifications ∙ Powers and Functions ∙ Executive ∙ President ∙ Governor ∙ Council of Ministers ∙ Judiciary, Appointment and Transfer of Judges, Qualifications ∙ Powers and Functions

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: ZilaPachayat. ∙ Elected officials and their roles, CEO ZilaPachayat: Position and role. ∙ Block level: Organizational Hierarchy (Different departments), ∙ Village level: Role of Elected and Appointed officials, ∙ Importance of grass root democracy

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.

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.

Students will be able to:

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

2. Identify critical evidence gaps to guide the development.

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.

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

Evidence on the effectiveness of pedagogical practices ∙ Methodology for the in depth stage: quality assessment of included studies. ∙ How can teacher education (curriculum and practicum) and the school 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.

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

Research gaps and future directions ∙ Research design ∙ Contexts ∙ Pedagogy ∙ Teacher education ∙ Curriculum and assessment ∙ Dissemination and research impact.

2.      Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education research project (MUSTER) country report 1. London: DFID.

3.      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.

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

5.      Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.

www.pratham.org/images/resource%20working%20paper%202.pdf.

To achieve overall health of body and mind

To overcome stress

Definitions of Eight parts of yog. ( Ashtanga )

Yam and Niyam. Do`s and Don’t’s in life. i) Ahinsa, satya, astheya, bramhacharya and aparigraha ii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan

Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effects-Types of pranayam

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

Neetisatakam-Holistic development of personality ∙ Verses- 19,20,21,22 (wisdom) ∙ Verses- 29,31,32 (pride & heroism) ∙ Verses- 26,28,63,65 (virtue) ∙ Verses- 52,53,59 (dont’s) ∙ Verses- 71,73,75,78 (do’s)

Approach to day to day work and duties. ∙ShrimadBhagwadGeeta : 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.

Statements of basic knowledge. ∙ShrimadBhagwadGeeta: Chapter2-Verses 56, 62, 68 ∙ Chapter 12 -Verses 13, 14, 15, 16,17, 18 ∙ Personality of Role model. ShrimadBhagwadGeeta: Chapter2-Verses 17, Chapter 3-Verses 36,37,42, ∙ Chapter 4-Verses 18, 38,39 ∙ Chapter18 – Verses 37,38,63

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

• Review and increasetheir understandingof thespecific topics tested.
• Improvetheir abilityto communicate that understandingto thegrader.
• Increasetheeffectiveness with which theyusethelimited examinationtime.

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

• Teachingskills
• Laboratoryskills andother professional activities
• Research methodology

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

To Understand big data for business intelligence

To Learn business case studies for big data analytics

To Understand Nosql big data management

To manage Big data without SQL

To understanding map-reduce analytics using Hadoop and related tools

What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations

Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats

Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study.

Eric Sammer, "Hadoop Operations",1^st Edition, O'Reilley, 2012.

2.    E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

3.    Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

Alan Gates, "Programming Pig", O'Reilley, 2011.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

·         To introduce the basic concepts and techniques of Machine Learning.

·         To develop the skills in using recent machine learning software for solving practical problems.

To be familiar with a set of well-known supervised, semi-supervised and unsupervised learning algorithms

Introduction- overview of machine learning- Different forms of learning- Generative learning- Gaussian parameter estimation- maximum likelihood estimation- MAP estimation- Bayesian estimation- bias and variance of estimators- missing and noisy features- nonparametric density estimation- applications- software tools. 

Classification Methods-Nearest neighbour- Decision trees- Linear Discriminant Analysis - Logistic regression-Perceptrons- large margin classification- Kernel methods- Support Vector Machines. Classification and Regression Trees. 

Graphical and sequential models- Bayesian networks- conditional independence-Markov random fields- inference in graphical models- Belief propagation- Markov models- Hidden Markov models- decoding states from observations- learning HMM parameters.

Clustering Methods-Partitioned based Clustering - K-means- K-medoids; Hierarchical Clustering - Agglomerative- Divisive- Distance measures; Density based Clustering - DBScan; Spectral clustering. 

Neural networks- the perceptron algorithm- multilayer perceptron’s- back propagation-nonlinear regression- multiclass discrimination- training procedures- localized network structure- dimensionality reduction interpretation.

John Mueller and Luca Massaron, “Machine Learning For Dummies“, John Wiley & Sons, 2016.

2. E. Alpaydin, “Machine Learning”, MIT Press, 2010.

3. C. Bishop, “Pattern Recognition and Machine Learning, Springer”, 2006.

4. ShaiShalev-Shwartz, Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press, 2014. 

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms

Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering

Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation – Morphology

Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors

Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

               Total                                                                                       : 50 marks

•    The students will understand the basic model of the information visualization process, information processing, visual representation, interaction in visual systems and its impacts.

•    The students will learn the conceptual framework for visualization design.

•    The students will learn how information can be transformed and visualized.

Helps the students to develop the skills necessary to solve visualization problems and critique and evaluate Information visualization systems.

Introduction – Issues – Data Representation – Data Presentation – Interaction.

Visualization stages – Experimental Semiotics based on Perception Gibson‘s Affordance theory – A Model of Perceptual Processing – Types of Data

Non-Computer Visualization – Computer Visualization: Exploring Complex Information Spaces – Fisheye Views – Applications – Comprehensible Fisheye views – Fisheye views for 3D data – Non Linear Magnification – Comparing Visualization of Information Spaces – Abstraction in computer Graphics – Abstraction in user interfaces.

One Dimension – Two Dimensions – Three Dimensions – Multiple Dimensions – Trees – Web Works – Data Mapping: Document Visualization – Workspaces

Case study-Small interactive calendars – Selecting one from many – Web browsing through a key hole – Communication analysis – Archival analysis

2.      Robert Spence “Information visualization – An Introduction”, 3rd Edition, Pearson Education, 2014. (Unit I &V)

3.      Stuart.K.Card, Jock.D.Mackinlay and Ben Shneiderman, “Readings in Information Visualization Using Vision to think”, Morgan Kaufmann Publishers

4.    Thomas Strothotte, Computer Visualization Graphics Abstraction and Interactivity, Springer Verlag Berlin Heiderberg, 

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

The course help in understanding the concepts in Soft Computing techniques VIZ Fuzzy systems, Genetic algorithms, Simulated annealing,  Ant Colony Optimization and Artificial Neural Networks, to apply these tools in solving problems, to analyze the strengths and weakness of these methods and to choose appropriate Soft Computing technique(s) for a given problem.  

Introduction to Soft Computing. Fuzzy sets and relations- operations – composition.  Membership functions – features – Fuzzification  - membership value assignments.  Defuzzification – Lambda cuts  (sets and relations) – Defuzzification to scalars.   Fuzzy Logic – approximate reasoning – different forms of implication. Natural language and Linguistic hedges. Fuzzy Rule-based systems – graphical techniques for inference. Extension principle and Fuzzy arithmetic.

Case Studies (minimum two) – application of Fuzzy Logic.

Genetic algorithm – Biological background – Search space – Basic terminologies in GA – a simple GA – General GA – Operators in GA (Encoding, Selection, Crossover – mutation) – stopping conditions – Constraints – Problem solving  - The schema theorem – advantages – applications.

Case study - Application of GA.

Simulated Annealing: Annealing Schedule, Parameter Selection, Applications.

Case study  - Application of SA.

Ant Colony Optimization: Ant Foraging Behavior,  artificial ants and minimum cost paths, ACO Metaheuristic, ACO algorithm for TSP problem, Theoretical considerations, convergence proof, ACO and Model based search. ACO optimization for subset problem,  

Supervised Learning Neural Networks – Perceptrons - Adaline – BackpropagationMutilayerPerceptrons – Radial Basis Function Networks – Unsupervised Learning Neural Networks – Competitive Learning Networks – Kohonen Self-Organizing Networks.  

Case study – Application of ANN.

Adaptive Resonance Theory – Introduction – ART 1 – ART2 – Applications.

Basic concepts in Associative memory – BAM.

Extreme Learning Machines - introduction – theory – applications- case study.

Hybrid soft computing systems – ANFIS – concepts and architecture - case study.

Introduction to Deep learning – Deep neural networks – concepts.

Recurrent neural network - concepts – applications.

Convolutional neural network – concepts – case study based on image classification.

3. Dorigo Marco, Stützle Thomas, “ANT COLONY OPTIMIZATION”, PHI, 2005

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

This course enables the students to Understand social media, web and social media analytics, and their potential impact. Determine how to Leverage social media for better services and Understand usability metrics, web and social media metrics. 

Overview of web & social media (Web sites, web apps, mobile apps and social media), Impact of social media on business, Social media environment, , How to leverage social media for better services, Usability, user experience, customer experience, customer sentiments, web marketing, conversion rates, ROI, brand reputation, competitive advantages Need of using analytics, Web analytics technical requirements., current analytics platforms, OpenSourcevs licensed platform, choosing right specifications & optimal solution, Web analytics and a Web analytics 2.0 framework (clickstream, multiple outcomes

Data (Structured data, unstructured data, metadata, Big Data and Linked Data), Participating with people centric approach, Data analysis basics (types of data, metrics and data, descriptive statistics, comparing, Basic overview of R R-Data Types, R-Decision Making, R-Loops, R-functions, R-Strings, Arrays, R-Lists, R-Data Frame, R-CSV Files, R-Pie Charts, R-Bar charts, R-Barplots. Basic Text Mining in R and word cloud.

Understand the discipline of social analytics, Aligning social objectives with business goals, Identify common social business objectives, developing KPIs; Standard vs Critical metrics. PULSE metrics (Page views, Uptime, Latency, Seven-day active users) on business and technical Issues, HEART metrics (Happiness, Engagement, Adoption, Retention, and Task success) on user behaviour issues; Bounce rate, exit rate, conversion rate, engagement, Syllabus of VII & VIII Semester B.E. / Computer Science &Engg. strategically aligned KPIs, Measuring Macro & micro conversions, On-site web analytics, off-site web analytics, the goal-signal-metric process. Case study on Ready-made tools for Web and social media analytics (Key Google Analytics metrics, dashboard, social reports, Tableau Public and KNIME

Why Is Twitter All the Rage?Exploring Twitter’s API, Fundamental Twitter Terminology, Creating a Twitter API Connection, Exploring Trending Topics, Searching for Tweets, Analyzing the 140 Character, Extracting Tweet Entities, Analyzing Tweets and Tweet Entities with Frequency Analysis, Computing the Lexical Diversity of Tweets, Examining Patterns in Retweets, Visualizing Frequency Data with Histograms. Analyzing Fan Pages, Examining Friendships, and More Overview, Exploring Facebook’s Social Graph API, Understanding the Social Graph API, Understanding the Open Graph Protocol, Analyzing Social Graph Connections, Analyzing Facebook Pages, Examining Friendships.

Introduction, Data Mining in a Nutshell, Social Media, Motivations for Data Mining in Social Media, Data Mining Methods for Social Media, Data Representation, Data Mining - A Process, Social Networking Sites: Illustrative Examples, The Blogosphere: Illustrative Examples, Related Efforts, Ethnography and Netnography, Event Maps.Introduction, Keyword Search, Query Semantics and Answer Ranking, Keyword search over XML and relational data, Keyword search over graph data, Classification Algorithms, Clustering Algorithms, Transfer Learning in Heterogeneous Networks

2.   Charu C Agarwal, Social Network Data Analytics, Springer; 2011 edition (1 October 2014), 978-1489988935

2.      AvinashKaushik, Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, John Wiley & Sons; Pap/Cdr edition (27 Oct 2009)

3.      Tom Tullis, Bill Albert, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Morgan Kaufmann; 1 edition (28 April 2008).

      4.    Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment, John Wiley & Sons (16 April 2010) Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley & Sons; 3rd Edition edition (30 Mar 2012

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

The course presents the basic concepts in Graph analysis in the big data context. The course is intended to give exposure to   basic concepts and practical aspects related to Massive Graph analysis.

Google’s Pregel –Computational model, algorithm design; Pregel-like systems –  Communication mechanism, Load balancing, out-of-core execution, fault recovery, On-demand querying.

Case study –BigGraph@CUHK

Programming interfaces and its expressiveness; GraphLab; PowerLab; Single-PC disk-based systems – GraphChi, X-Stream, VENUS, GridGraph.

Block centric vs Vertex centric; The Blogel Systems; Blogel Graph Practitioners;  Block Centric API.

Problem definition and existing methods; The G-thinker systems

Graph and Matrices; PEGUSES; GBASE; SystemML;  Comparison with vertex based system

R. Brath, D. Jonker, Graph Analysis and Visualization, Wiley, 2015.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

This course introduces the basic concepts of IoT, the functionalities of different types of sensors, actuators and micro controllers. It covers the protocols used in different layers and gives insight on programming IoT for different domains.

Definition and Characteristics of IoT, Physical Design of IoT: Things in IoT, Logical Design of IoT: IoT functional Blocks, IoT Communication Blocks, IoT communication APIs, IoT Enabling Technologies: WSN, Cloud Computing, Big Data Analysis, Communication Protocols, Embedded Systems.

Basics of Arduino: The Arduino Hardware, The Arduino IDE, Basic Arduino Programming, Basics of Raspberry pi: Introduction to Raspberry Pi, Programming with Raspberry Pi, CDAC IoT devices: Ubimote, Wi-Fi mote, BLE mote, WINGZ gateway, Introduction to IoT Platforms, IoT Sensors and actuators.

IoT Data Link Protocols, Network Layer Routing Protocols, Network Layer Encapsulation Protocols, Session Layer Protocols, IoT Security Protocols, Service Discovery Protocols, Infrastructure Protocols

Arduino Programming: Serial Communications, Getting input from sensors, Visual, Physical and Audio Outputs, Remotely Controlling External Devices, Wireless Communication. Programming with Raspberry Pi: Basics of Python Programming, Python packages of IoT, IoT Programming with CDAC IoT devices.

Home automation, Smart cities, Smart Environment, IoT in Energy, Logistics, Agriculture, Industry and Health & Life style secors. Case Studies: A Case study of Internet of Things Using Wireless Sensor Networks and Smartphones, Security Analysis of Internet-of-Things: A Case Study of August Smart Lock, OpenIoT platform.

2. Margolis, Michael. “Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects. " O'Reilly Media, Inc.", 2011.

3. Monk, Simon. Raspberry Pi cookbook: Software and hardware problems and solutions. " O'Reilly Media, Inc.", 2016.

2.      Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012.

3.      David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, 2010.

4.      Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376.

5.      Tsitsigkos, Alkiviadis, et al. "A case study of internet of things using wireless sensor networks and smartphones." Proceedings of the Wireless World Research Forum (WWRF) Meeting: Technologies and Visions for a Sustainable Wireless Internet, Athens, Greece. Vol. 2325. 2012.

        Ye, Mengmei, et al. "Security Analysis of Internet-of-Things: A Case Study of August Smart Lock."

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

                      Attendance                                                                             : 05 marks

                                  Total                                                                                       : 50 marks

·         To understand the fundamental principles and techniques in deep and reinforcement learning.

·         Helps to understand different algorithms in deep and reinforcement learning.

·         Helps to understand few applications of deep and reinforcement learning.

To analyze few active research topics in deep and reinforcement learning areas.

Introduction- Historical Trends in Deep Learning, Machine Learning Basics, History of Reinforcement Learning – Examples - Elements of Reinforcement Learning - Limitations and Scope

Deep Feedforward Networks-Example-Gradient-Based Learning-Hidden Units-Architecture Design-  Back-Propagation and Other Differentiation Algorithms, Regularization for Deep Learning, Optimization for Training Deep Models - Challenges - Basic Algorithms - Parameter Initialization - Algorithms with Adaptive Learning Rates - Approximate Second-Order Methods-Optimization Strategies and Meta-Algorithms

Convolutional Networks -Operation - Motivation - Pooling - Variants of the Basic Convolution Function -Efficient Convolution Algorithms -Random or Unsupervised Features, Sequence Modeling: Recurrent and Recursive Nets - Unfolding Computational Graphs - Recurrent Neural Networks - Bidirectional RNNs - Encoder-Decoder Sequence-to-SequenceArchitectures -Deep Recurrent Networks -Recursive Neural Networks,

Applications

Multi-armed Bandits-Dynamic Programming - Monte Carlo Methods -Temporal-Difference Learning -n-step Bootstrapping 

On-policy Prediction with Approximation -On-policy Control with Approximation –Off policy Methods with Approximation -Policy Gradient Methods

Richard S. Sutton and Andrew G. Barto,“Reinforcement Learning: An Introduction” second edition, MIT Press.

Deng & Yu, Deep Learning: Methods and Applications, Now Publishers, 2013.)

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Objective of this course is to enable to students to learn the fundamentals of Pattern Recognition techniques, Statistical Pattern recognition techniques, Syntactical Pattern recognition techniques and Neural Pattern recognition techniques useful for computer vision applications.

Pattern recognition,   Classification   and   Description—Patterns   and   feature Extraction   with   Examples—Training   and Learning   in   PR systems—Pattern recognition Approaches

Introduction   to statistical   Pattern Recognition—supervised   Learning   using Parametric and Non Parametric Approaches

Introduction-Discrete   and   binary   Classification   problems—Techniques   to directly   Obtain   linear   Classifiers   - Formulation   of   Unsupervised   Learning Problems—Clustering for unsupervised learning and classification

Overview of Syntactic Pattern Recognition—Syntactic recognition via parsing and other grammars–Graphical Approaches to syntactic pattern recognition—Learning via grammatical inference

Introduction to Neural networks—Feedforward Networks and training by Back Propagation—Case Study - Content   Addressable   Memory   Approaches   and Unsupervised Learning in Neural PR

2.         Bishop C.M., “Neural Networks for Pattern Recognition”, Oxford University Press, 2005.

3.         Duda R.O., P.E.Hart   & D.G Stork, “Pattern Classification”, 2nd Edition, J.WileyInc 2001.

Duda R.O. & Hart P.E., “Pattern Classification and Scene Analysis”,  J.WileyInc, 1973.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

Introduction to optimization techniques using both linear and non-linear programming. The focus of the course is on convex optimization though some techniques will be covered for non-convex function optimization too. After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering minima maxima problems in the framework of optimization problems.

Linear algebra and matrices  Vector space, eigen analysis,  Elements of probability theory,  Elementary multivariable calculus

Simplex method, Introduction to linear programming model,  Duality,  Karmarkar's method

Conjugate direction and quasi-Newton methods, Gradient-based methods, One-dimensional search methods

Lagrange theorem  FONC, SONC, and SOSC conditions

Projection methods, KKT conditions, Non-linear constrained optimization models

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

            End semester practical examination                                     : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

 End semester practical examination                                     : 25 marks

    Records                                                                                   : 05 marks

            Mid semester examination                                           : 10 marks

            Class work                                                                       : 10 marks

            Total                                                                                  : 50 marks

Cyber Security is defined as the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage or unauthorized access. Similar to other forms of security, Cyber Security requires coordinated effort throughout an information system.  This course will provide a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security for all parties who may be involved keeping view of Global and Indian Legal environment. 

After learning the course for a semester, the student will be aware of the important cyber laws in the Information Technology Act (ITA) 2000 and ITA 2008 with knowledge in the areas of Cyber-attacks and Cyber-crimes happening in and around the world. The student would also get a clear idea on some of the cases with their analytical studies in Hacking and its related fields.

Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities

Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities  - Server Hardening  

Risk Management and Assessment - Risk Management Process - Threat Determination Process - Risk Assessment - Risk Management Lifecycle – Vulnerabilities, Security Policy Management - Security Policies  - Coverage Matrix, Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning - Business Continuity Planning Process.

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. Business-to-Business, Exceptions to Policy, Special Services and Protocols, Configuration Management - Certification and Accreditation

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services Securing Communications: Securing Services - Transport – Wireless - Steganography and NTFS Data Streams, Intrusion Detection and Prevention Systems: Intrusion - Defense in Depth - IDS/IPS  -  IDS/IPS Weakness and Forensic Analysis, Cyber Evolution: Cyber Organization - Cyber Future

TEXT BOOKS:   

1. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,  ISBN-10: 1118027809 
2. Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690
3. Jason Andress, The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice, Syngress; 1 edition (June 24, 2011) ,  ISBN-10: 1597496537

4. Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002. 
5. Bruce, Schneier, “Applied Cryptography”, 2nd Edition, Toha Wiley & Sons, 2007. 
6. Man Young Rhee, “Internet Security”, Wiley, 2003. 
7. Pfleeger & Pfleeger, “Security in Computing”, Pearson Education, 3rd Edition, 2003.  

REFERENCES:

1. Information Technology Act 2008 Online 2. IT Act 2000.

This course is designed to introduce programming experience to techniques associated with the World Wide Web. The course will introduce web-based media-rich programming tools for creating interactive web pages. Basic animation programming is also introduced with an emphasis on media-rich content creation, distribution and tracking capabilities.

·         Analyze Build web applications using PHP, JSP and Servlets and client side script technologies like HTML, CSS and JavaScript with Apache web server.

·         Design and Integrate database environment to web applications being developed. Describe sessions conceptually and implement using cookies and URL. 

·         Analyze the XML applications with DTD and style sheets that span multiple domains and across various platforms. 

·     Analyze the reasons and effects of nonstandard client-side scripting language characteristics, such as limited data types, dynamic variable types and properties, and extensive use of automatic type conversion.

Introduction – Network concepts – Web concepts – Internet addresses - Retrieving Data with URL – HTML – DHTML: Cascading Style Sheets - Scripting Languages: JavaScript.

Common Gateway Interface: Programming CGI Scripts – HTML Forms – Custom Database Query Scripts – Server Side Includes – Server security issues 

XML- XSL, XSLT, DOM ,RSS, Client Technologies- Adobe Flash, Flex, Microsoft Silverlight.

Server side Programming – PHP- Passing variables between pages, Using tables, Form elements. Active server pages – Java server pages

Java Servlets: Servlet container – Exceptions – Sessions and Session Tracking – Using Servlet context – Dynamic Content Generation – Servlet Chaining and Communications.

Simple applications – Internet Commerce – Database connectivity.

2.      Beginning PHP, Apache, MySql Web Development , Timothy, Elizabath, Jason, Wrox ,2012

2.      Jeffy Dwight, Michael Erwin and Robert Nikes “USING CGI”, PHI Publications, 1999

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

    Attendance                                                                             : 05 marks

            Total                                                                                       : 50 marks

• To understand the concepts of machine learning
• To appreciate supervised and unsupervised learning and their applications
• To understand the theoretical and practical aspects of Probabilistic Graphical Models
• To appreciate the concepts and algorithms of reinforcement learning
• To learn aspects of computational learning theory

• Implement a neural network for an application of your choice using an available tool
• Implement probabilistic discriminative and generative algorithms for an application of your choice and analyze the results
• Implement typical clustering algorithms for different types of applications
• Design and implement an HMM for a sequence model type of application
• Identify applications suitable for different types of machine learning with suitable justification

Machine Learning - Machine Learning Foundations –Overview – applications - Types of machine learning - basic concepts in machine learning Examples of Machine Learning -Applications – Linear Models for Regression - Linear Basis Function Models - The Bias-Variance Decomposition - Bayesian Linear Regression - Bayesian Model Comparison

Linear Models for Classification - Discriminant Functions -Probabilistic Generative Models -

Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees – Classification Trees- Regression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Error Backpropagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting.                            

Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in General -Model selection for latent variable models - high-dimensional spaces -- The Curse of Dimensionality –Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis

Directed Graphical Models - Bayesian Networks - Exploiting Independence Properties – From Distributions to Graphs -Examples -Markov Random Fields - Inference in Graphical Models – Learning –Naive Bayes classifiers-Markov Models – Hidden Markov Models – Inference – Learning- Generalization – Undirected graphical models- Markov random fields- Conditional independence properties - Parameterization of MRFs - Examples - Learning - Conditional random fields (CRFs) - Structural SVMs

Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning - K-Armed Bandit - Elements - Model-Based Learning - Value Iteration - Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions- Eligibility Traces- Generalization- Partially Observable States- The Setting- Example. Semi - Supervised Learning. Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks