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1 Semester - 2018 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS131 | ADVANCED CLOUD COMPUTING | - | 4 | 3 | 100 |
MTCS132 | ADVANCED COMPUTER ARCHITECTURE | - | 4 | 3 | 100 |
MTCS133 | ADVANCED ALGORITHMS | - | 4 | 3 | 100 |
MTCS134 | SOFTWARE PROCESS MANAGEMENT | - | 4 | 3 | 100 |
MTCS135 | ADVANCED DIGITAL IMAGE PROCESSING | - | 4 | 3 | 100 |
MTCS151 | ADVANCED ALGORITHMS LABORATORY | - | 4 | 2 | 50 |
MTCS152 | DIGITAL IMAGE PROCESSING LABORATORY | - | 4 | 2 | 50 |
MTCS171 | PROFESSIONAL PRACTICE - I | - | 2 | 2 | 50 |
2 Semester - 2018 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS232 | COMPUTER COMMUNICATION NETWORKS | - | 4 | 3 | 100 |
MTCS233 | VERY LARGE DATABASE MANAGEMENT SYSTEMS | - | 4 | 3 | 100 |
MTCS234 | DATA SCIENCE | - | 4 | 3 | 100 |
MTCS235 | ADVANCED RESEARCH METHODOLOGY | - | 4 | 3 | 100 |
MTCS251 | NETWORKING LABORATORY | - | 4 | 2 | 50 |
MTCS252 | DATABASE TECHNOLOGY LABORATORY | - | 4 | 2 | 50 |
MTCS271 | PROFESSIONAL PRACTICE - II | - | 2 | 2 | 50 |
MTMA233 | MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE | - | 4 | 3 | 100 |
3 Semester - 2017 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
CY01 | CYBER SECURITY | - | 2 | 2 | 50 |
MTCS331E06 | BIO INFORMATICS | - | 4 | 3 | 100 |
MTCS332E01 | MACHINE LEARNING | - | 4 | 3 | 100 |
MTCS371 | PROJECT WORK (PHASE I) | - | 3 | 3 | 100 |
MTCS373 | INTERNSHIP | - | 4 | 2 | 50 |
MTIT333E04 | OPERATIONS RESEARCH | - | 4 | 3 | 100 |
4 Semester - 2017 - Batch | Course Code |
Course |
Type |
Hours Per Week |
Credits |
Marks |
MTCS471 | PROJECT WORK (PHASE-II) AND DISSERTATION | - | 6 | 9 | 300 |
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Introduction to Program: | |
The 2 year Post graduate program MTech in Computer Science and Engineering. started in 2009 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the course. | |
Assesment Pattern | |
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 For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks Mid semester practical examination will be conducted during regular practical hour with prior intimation to all candidates. End semester practical examination will have two examiners an internal and external examiner. v Assessment of Project Work(Phase-II) and Dissertation § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Assessed by Guide
§ End Semester Examination:100 Marks ¨ Viva Voce ¨ Demonstration ¨ Project Report § Dissertation (Exclusive assessment of Project Report): 100 Marks
vAssessment of Internship (M.Tech) All students should complete internship before 3rd semester. This component carries 2 credits. § Continuous Internal Assessment:2 credits Presentation assessed by Panel Members | |
Examination And Assesments | |
Assessment of each paper · 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) |
MTCS131 - ADVANCED CLOUD COMPUTING (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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This course focuses on learning emerging issues related to Cloud computing technology. The objectives are:
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Course Outcome |
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Upon successful completion of this course the students should be able to: |
Unit-1 |
Teaching Hours:12 |
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UNDERSTANDING CLOUD COMPUTING
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Cloud Computing – History of Cloud Computing – Cloud Architecture – Cloud Storage – Why Cloud Computing Matters – Advantages of Cloud Computing – Disadvantages of Cloud Computing – Companies in the Cloud Today – Cloud Services- Web-Based Application – Pros and Cons of Cloud Service Development. | |||||||||||||||||||||||||||||||
Unit-2 |
Teaching Hours:12 |
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CLOUD COMPUTING ARCHITECTURE
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Types of Cloud Service Development – Infrastructure / Hardware as a Service-Software as a Service – Platform as a Service | |||||||||||||||||||||||||||||||
Unit-3 |
Teaching Hours:12 |
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VIRTUALIZATION TECHNIQUES; VIRTUAL MACHINES PROVISIONING AND MIGRATION SERVICES
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Characteristics of Virtualized Environment – Taxonomy of Virtualization Techniques–Virtualization and Cloud Computing – Pros and Cons of Virtualization – Technology Examples: Xen, VMware, Hyper-V. Virtual Machines Provisioning and Manageability–Virtual Machine Migration Services – Provisioning in the Cloud Context. | |||||||||||||||||||||||||||||||
Unit-4 |
Teaching Hours:12 |
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WORKFLOW AND MAP-REDUCE PROGRAMMING MODELS
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Workflow Management Systems and Clouds- Architecture of Workflow Management Systems – Utilizing Clouds for Workflow Execution, Data-Intensive Computing– Technologies for Data-Intensive Computing – Storage Systems – Programming Platforms- Aneka MapReduce Programming, Major MapReduce Implementations for the Cloud. | |||||||||||||||||||||||||||||||
Unit-5 |
Teaching Hours:12 |
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CLOUD APPLICATIONS: SECURITY AND PERFORMANCE ISSUES
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Case Study: Business and Consumer Applications: CRM and ERP, Social Networking, Multiplayer Online Gaming, Technologies for Data Security in Cloud Computing. Cloud Computing and Data Security Risk- The Cloud, Digital Identity, and Data Security–Content Level Security, Data Privacy and Security Issues, HPC in the Cloud: Performance related Issues. | |||||||||||||||||||||||||||||||
Text Books And Reference Books:
Text BookS: 1. Mastering Cloud Computing - Rajkumar Buyya, Vecchiola, Selvi, McGraw Hill, 2013. 2. “Cloud Computing – A Practical Apprach”, Anthony Velte, Toby Velte and Robert Elsenpeter, McGraw Hill, 2010. 3. Cloud Computing – Principles and Paradigms, Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, Wiley 2011.
Reference Books: 1. “Grids, Clouds and Virtualization”, Massimo Cafaro and Giovanni Aloisio, Springer 2011. 2. “Cloud Computing – Web Based Applications That Change the Way You Work and Collaborate Online”, Michael Miller, Que Publishing, August 2008.
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Essential Reading / Recommended Reading
IEEE Transactions Referred (i) Rajkumar Buyya, “Introduction to Cloud Computing”, IEEE Transactions on Cloud Computing, Vol.1, No. 1, pp. 1-19, 2013. (ii) K Hwant, X Bai, Li, Chen and Wu, “Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies”, IEEE Transactions on Parallel and Distributed Systems, Vol.27, Issue.1, pp.130-143, 2016. (iii) Z Xiao, W Song and Qi Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment”, IEEE Transactions on Parallel and Distributed Systems, Vol.24, Issue.6, pp.1107-1117, 2013. (iv) X Fei and S Lu, “A Dataflow-Based Scientific Workflow Composition Framework”, IEEE Transactions On Services Computing, Vol.5, Issue.1, pp.45-58, 2012. (v) Q Wang, C Wang, K Ren and Li, “Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing”, IEEE Transactions on Parallel and Distributed Systems, Vol. 22, No.5, 2011. (vi) Sugam Sharma , “Evolution of as-a-Service Era in Cloud”, Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, USA. Cornell University Library: http://arxiv.org/ftp/arxiv/papers/1507/1507.00939.pdf
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Evaluation Pattern
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MTCS132 - ADVANCED COMPUTER ARCHITECTURE (2018 Batch) | |||||||||||||||||||||||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:12 |
FUNDAMENTALS OF COMPUTER DESIGN
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Measuring and Reporting performance - Quantitative principles of computer Design - Classifying | |
Unit-2 |
Teaching Hours:12 |
INSTRUCTION LEVEL PARALLELISM
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Pipelining and Hazards - Concepts of ILP - Dynamic scheduling - Dynamic Hardware prediction - | |
Unit-3 |
Teaching Hours:12 |
INSTRUCTION LEVEL PARALLELISM WITH SOFTWARE APPROACHES
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Compiler techniques for exposing ILP - Static branch prediction - Static multiple issues: VLIW - | |
Unit-4 |
Teaching Hours:12 |
MEMORY AND I/O
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Cache performance - Reducing cache miss penalty and miss rate - Reducing hit time - Main | |
Unit-5 |
Teaching Hours:12 |
MULTIPROCESSORS AND THREAD LEVEL PARALLELISM
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Symmetric and distributed shared memory architectures - Performance issues - Synchronization - | |
Text Books And Reference Books: TEXTBOOKS
REFERENCE BOOKS
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Essential Reading / Recommended Reading 1. D. Sima, T. Fountain and P. Kacsuk, " Advanced Computer Architectures: A Design Space Approach", Pearson, 2014. 2. Kai Hwang "Advanced computer architecture Parallelism Scalability Programmability" Tata Mcgraw Hill Edition 2015. 3.Stallings, William. Computer organization and architecture: designing for performance. Pearson Education India, 2015. 4. Vincent P.Heuring, Harry F.Jordan, “Computer System Design and Architecture”, Addison Wesley, 2nd Edition 2008. | |
Evaluation Pattern INTERNAL - 50 EXTERNAL - 50 | |
MTCS133 - ADVANCED ALGORITHMS (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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COURSE DESCRIPTION: To provide an in-depth knowledge in problem solving techniques, data structures and the various algorithms paradigms. Scope and Objective:
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Course Outcome |
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Upon completion of the subject, the students will be able to acquire the following Professional / Academic Knowledge, Skills and the Attributes for Efficient Thinking and Solving Critical Problems Independently. |
Unit-1 |
Teaching Hours:11 |
REVIEW OF ANALYSIS TECHNIQUES
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Growth of Functions: Asymptotic notations; Standard notations and common functions; Recurrences and Solution of Recurrence equationsThe substitution method, The recurrence – tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods. | |
Unit-2 |
Teaching Hours:14 |
GRAPH ALGORITHMS AND POLYNOMIALS
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Graph Algorithms: Bellman Ford Algorithm; Single source shortest paths in a DAG;Algorithm for sparse graphs; Flow networks and Ford Fulkerson method; Maximum bipartite matching. Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT. | |
Unit-3 |
Teaching Hours:13 |
NUMBER THEORETIC ALGORITHMS
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Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization. | |
Unit-4 |
Teaching Hours:10 |
STRING MATCHING ALGORITHMS
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Naïve string Matching; Rabin Karpalgorithm; String matching with finite automata; KnuthMorrisPratt algorithm; Boyer – Moore algorithms. | |
Unit-5 |
Teaching Hours:12 |
PROBABILISTIC ALGORITHMS
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Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms. Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages. | |
Text Books And Reference Books: TEXT BOOKS
REFERENCE BOOKS
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Essential Reading / Recommended Reading Topic models and advanced algorithms for profiling of knowledge in scientific papers:
Proceedings of the 35th International Convention MIPRO : IEEE Conference publications | |
Evaluation Pattern CIA 1 : 20 marks a. Library assignment b. Presentation CIA 2: 50 mark MSE CIA 3: 20 mark a. Test b. Review of IEEE papers on Optimization algorithms
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MTCS134 - SOFTWARE PROCESS MANAGEMENT (2018 Batch) | |
Total Teaching Hours for Semester:4 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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1. COURSE DESCRIPTION: The concept of processes is at the heart of software and systems engineering. Software process models integrate software engineering methods and techniques and are the basis for managing large-scale software and IT projects. High product quality routinely results from high process quality. Software process management deals with getting and maintaining control over processes and their evolution. Becoming acquainted with existing software process models is not enough, though. It is important to understand how to select, define, manage, deploy, evaluate, and systematically evolve software process models so that they suitably address the problems, applications, and environments to which they are applied. Providing basic knowledge for these important tasks is the main goal of this textbook. Courtesy: Software Process definition and Management by Authors: Münch, J., Armbrust, O., Kowalczyk, M., Soto, M. 2. Scope and Objective: The course aims to provide an in-depth knowledge in at understanding the software process management methods. They include.. • To study the needs of Software maturity framework • To understand Managing software organizations
• To make the students to get familiarized with Software standards, inspections with defined and optimized processes. |
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Course Outcome |
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Upon successful completion of this module, the student will be able to: · Understand and practice the Software maturity framework and its application in delivering successful IT projects. · Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities. · Understand the need of Software configuration management tools that identify the factors that put into baselines. · Identify the different software standards, software inspections, configuration management and its application in delivering successful IT projects. · Monitor the progress of a project and to assess the risk of slippage, revising targets or counteract drift. · Distinguish between the different types of project and follow the stages needed to negotiate an appropriate contract. |
Unit-1 |
Teaching Hours:12 |
A Software maturity framework
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Software Improvement, process maturity level, people in the optimizing level, need for the optimizing process. | |
Unit-2 |
Teaching Hours:12 |
The Repeatable Process
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Managing software organizations: commitment discipline, the management system, establishing a project management system. The Project plan: project planning principles, contents, size measures, estimating, productivity factors, scheduling, project tracking, the developing plan,planning models, final Considerations. Software configuration management: Need for configuration management, software product nomenclature, basic configuration management function, baselines, configuration management responsibilities, need for automated tools, software quality management. | |
Unit-3 |
Teaching Hours:12 |
Defined process
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Software standards: definitions, reasons, benefits, examples of major standards, establishing software standard, standards versus guidelines. Software inspections : Types of reviews, objectives, basic inspection principles, the conduct of inspections, inspection training, reports and tracking, other considerations, initiating and inspection program, future directions. Software configuration management : the Software configuration management plan, Software configuration management questioners, scm support functions, the requirement phase, design control, the implementation phase, operational data, the test phase, scm for tools, configuration accounting, the software configurations audit. | |
Unit-4 |
Teaching Hours:12 |
Managed Process
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Data gathering and analysis: The principles of data gathering, data gathering process, software measures, data analysis, other considerations. Managing software quality: The quality management paradigm, quality motivation, quality goals, quality plans, tracking and controlling software quality. | |
Unit-5 |
Teaching Hours:12 |
The Optimizing Process
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Defect Prevention: Defect prevention not a idea, the principles of SDP, process changes for defect prevention, defect prevention consideration, management role. Automating the software process: The need for software automation, What to automate? Development environments, organizational plans to automate, technology transitions,productivity. Case Study. | |
Text Books And Reference Books: TEXT BOOKS 1. Introduction to the Personal Software Process by Watts S. Humphrey, published by Pearson Education 2012. 2. Software Change Management: Case Studies and Practical Advice by Donald J. Reifer Pearson Education 2012. 3. Managing the software process by Watts S. Humphrey, published by Pearson Education 2013. REFERENCE BOOKS 1. Software Process Definition and Management Jürgen Münch Ove Armbrust Martin Kowalczyk Martín Soto - May 27, 2012 Springer Science & Business Media - Publisher 2. Software Process Modeling Silvia T. Acuna Natalia Juristo- January 27, 2006 Springer Science & Business Media - Publisher 3. A Discipline to Software Engineering by Watts S. Humphrey Pearson Education 2008. 4. Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010. 5. Software Project Management Readings and Cases by Chris Kemerer 2010. | |
Essential Reading / Recommended Reading Managing the software process by Watts S. Humphrey, published by Pearson Education 2013. ONLINE REFERENCE (MOOC and NPTEL) 1. https://www.futurelearn.com/courses/business-process-management 2. https://www.edx.org/course/enterprise-software-lifecycle-management-mephix-mephi001x 3. https://www.coursera.org/learn/process-improvement 4. https://www.coursera.org/learn/software-processes-and-agile-practices 5. http://nptel.ac.in/courses/106101061/36# 6. http://nptel.ac.in/courses/106105087/pdf/m13L35.pdf
JOURNALS AND RESEARCH PAPERS (IEEE) 1. Software Engineering Journal IEEE Xplore Digital Library 2. Process Management Tools IEEE Xplore Digital Library 3. IEEE Transactions on Engineering Management IEEE Xplore Digital Library 4. CMMI Guided Process Improvement for DevOps Projects: An Exploratory Case Study 5. Convergence analysis of ISO/IEC 12207 and CMMI-DEV: A systematic literature review 6. Mapping between the Guide of IT Solution Contract and CMMI Models: A Qualitative Analysis 7. Business goals monitoring and control measures in CMMI 8. Industrial Experience with Open Source Software Process Management 9. Identifying Software Process Management Challenges: Survey of Practitioners in a Large Global IT Company 10. The design of software process management and evaluation system in small and medium software enterprises
11. A Case Study on Usage of a Software Process Management Tool in China | |
Evaluation Pattern CIA I Component I Research Paper draft making (Moodle submissions): Good Grade (75%-90%): Submitting well in advance (before deadline) in the appropriate/ instructed format. Should have also reverted back for the rough draft before actual submission for feedback. Plagiarism should be less than 20% in the submitted draft. Pictures should also not to be copied from internet and to have good resolution. Average Grade (55-75): One day late submission. Feedback taken but not implemented in the work. Plagiarism more than 40% with few copied figures and also partially following the instructed format. Poor Grade (25-55): More than 2 days late submissions. Not reverting for any feedback. Plagiarism more than 50% without following the format.
CIA I Component II Quiz: MCQ/ True or False/ Matching (Moodle based) Good Grade (75%-90%): First attempt with all most all correct answers and finishing with in stipulated time. Average Grade (55-75): First attempt with more than 30% wrong answers. Poor Grade (25-55): Could not attend for the scheduled test and appeared in a rescheduled one. Failing to answer at least more than 50% of questions.
CIA III Component I Assignment: Every student is given a specific unique problem/case related to software process management Good Grade (75%-90%): Report submission within said deadline. Following the format instructed with less than 20% plagiarism. Showing the utilization of research papers/ library books as proof of concept. Flavor of management principles followed in report generation. Average Grade (55-75): Report submission after a day of deadline. Following the format partially less than 35% plagiarism. Not showing the utilization of research papers/ library books as proof of concept. Poor Grade (25-55): Submissions after 2 days of deadline. Not following the instructed format and plagiarized content more than 50%. Copied contents from the fellow mates.
CIA III Component II Written Test: On research topics related to subject syllabus. Good Grade (75%-90%): Relevant answers with appropriate figures and content answering all the questions. Average Grade (55-75): Answering only an 80% of the questions with the contents related to it.
Poor Grade (25-55): Not attempting on the scheduled date of test. Answering the paper with only 50% of questions.
Poor figures and lack of continuity in answers. | |
MTCS135 - ADVANCED DIGITAL IMAGE PROCESSING (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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· To study the image fundamentals and mathematical transforms necessary for image processing. · To study the image enhancement techniques · To study image restoration procedures. · To study the image compression procedures. · To study the image segmentation and representation techniques. · To study pattern recognition and interpretation. |
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Course Outcome |
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· Students will be able to do the following: · Explain and analyze the steps of image formation, sampling, quantization and representation digitally. · Outline and examine how images are processed by discrete, linear, time-invariant systems. · Summarize how images are perceived by humans and how color is represented . · Point out how image information can be modeled analytically and compare transform-domain representation of images (Fourier, DCT, Haar, WHT) · Outline how images are enhanced to improve subjective perception and ability to differentiate about restoration of images based on the knowledge acquisition system. · Ability to summarize about how images are analyzed to extract features of interest · Ability to summarize the principles of image compression |
Unit-1 |
Teaching Hours:12 |
DIGITAL IMAGE FUNDAMENTALS
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Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms. | |
Unit-2 |
Teaching Hours:12 |
IMAGE ENHANCEMENT & RESTORATION
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Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering. | |
Unit-3 |
Teaching Hours:12 |
IMAGE COMPRESSION & SEGMENTATION
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Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation - Morphology. | |
Unit-4 |
Teaching Hours:12 |
REPRESENTATION AND DESCRIPTION
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Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors | |
Unit-5 |
Teaching Hours:12 |
OBJECT RECOGNITION AND INTERPRETATION
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Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies | |
Text Books And Reference Books:
1. Gonzalez.R.C & Woods. R.E., “Digital Image Processing”, 3rd Edition, Pearson Education, Indian edition published by Dorling Kindersely India Pvt. Ltd. Copyright © 2009, Third impression 2011. 2. Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd 2011 (Asia) 3. Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014 4. David A Forsyth & Jean ponce “Computer Vision: A Modern Approach” 2nd Edition, Pearson Education India 2015 | |
Essential Reading / Recommended Reading
Anil K.Jain, “Fundamentals of Digital image Processing”, Prentice Hall of India, 2011. | |
Evaluation Pattern Internal 50 Marks External 50 Marks | |
MTCS151 - ADVANCED ALGORITHMS LABORATORY (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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● To increase the knowledge of advanced paradigms of algorithm design. |
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Course Outcome |
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· Effective use of mathematical techniques to construct robust algorithms. · Assess and to make critical judgment on the choices of algorithms for modern computer systems. |
Unit-1 |
Teaching Hours:6 |
Programs on Data Structures and Algorithms
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Unit-2 |
Teaching Hours:6 |
Programs on Data Structures and Algorithms
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Unit-3 |
Teaching Hours:6 |
Programs on Cloud Computing
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Unit-4 |
Teaching Hours:6 |
Programs on Cloud Computing
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Unit-5 |
Teaching Hours:6 |
Programs on Advanced Computer Architecture
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Essential Reading / Recommended Reading
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Evaluation Pattern Class work : 20mark Record : 10mark MSE : 20 mark ESE : 50 mark | |
MTCS152 - DIGITAL IMAGE PROCESSING LABORATORY (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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· To study the image fundamentals and mathematical transforms necessary for image processing. · To study the image enhancement techniques · To study image restoration procedures. · To study the image compression procedures. · To study the image segmentation and representation techniques. · To study pattern recognition and interpretation. |
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Course Outcome |
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Students will be able to do the following: · Explain and analyze the steps of image formation, sampling, quantization and representation digitally. · Outline and examine how images are processed by discrete, linear, time-invariant systems. · Summarize how images are perceived by humans and how color is represented . · Point out how image information can be modeled analytically and compare transform-domain representation of images (Fourier, DCT, Haar, WHT) · Outline how images are enhanced to improve subjective perception and ability to differentiate about restoration of images based on the knowledge acquisition system. · Ability to summarize about how images are analyzed to extract features of interest · Ability to summarize the principles of image compression |
Unit-1 |
Teaching Hours:12 |
unit 1
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Unit-2 |
Teaching Hours:12 |
unit 2
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Unit-3 |
Teaching Hours:12 |
unit 3
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Unit-4 |
Teaching Hours:12 |
unit 4
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Unit-5 |
Teaching Hours:12 |
unit 5
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Text Books And Reference Books:
1. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, “Digital Image Processing using MATLAB”, Pearson Education, Inc., 2004. 2. Gonzalez.R.C & Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd 2011 (Asia) 3. Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014
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Essential Reading / Recommended Reading David A Forsyth & Jean ponce “Computer Vision: A Modern Approach” 2nd Edition, Pearson Education India 2015. | |
Evaluation Pattern Internal 25 External 25 | |
MTCS171 - PROFESSIONAL PRACTICE - I (2018 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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The course will broadly cover the following aspects: • Teaching skills • Laboratory skills and other professional activities • Research methodology |
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Course Outcome |
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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. |
Unit-1 |
Teaching Hours:45 |
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OPERATIONAL DETAILS
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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. EVALUATION SCHEME Component InstructorsWeightage TeachingLecture materials 7.5 Lecture presentation 10
Laboratory and Professional activitiesReports 7.5 Viva/presentation 10
ResearchProposal 2.5 Viva/presentation 2.5
ComprehensiveTest/ viva 10
Total 50
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Unit-1 |
Teaching Hours:30 |
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OPERATIONAL DETAILS
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HeadoftheDepartmentwillassignasuitableinstructor/faculty membertoeachstudent.Studentsandfacultymemberscoveringabroadareawillbegroupedin apanel consistingof 4-5students and 4-5 facultymembers.
Withinone weekafter registration,thestudentshouldplanthedetailsofthetopicsof lectures,laboratory experiments,developmentalactivitiesandbroadtopicofresearchetcin consultationwiththeassignedinstructor/faculty.Thestudenthastosubmittwocopiesof the written outlineof thetotal work to theinstructor within one week.
Ina particulardiscipline,Instructorsbelongingtothe broadareaswillform the paneland willnominateoneof themasthepanelcoordinator.Thecoordinatortogether withthe instructors willdrawacompleteplanoflecturestobedelivered by allstudentsinasemester.Eachstudent willpresent3-4lectures,whichwillbeattendedby allotherstudentsandInstructors.These lectureswillbeevenly distributedovertheentiresemester.Thecoordinatorwillannouncethe schedule forthe entiresemester and fixsuitablemeetingtime in the week.
Eachstudentwillalsoprepare onepresentationabouthisfindingsonthebroadtopicof research.Thefinal reporthastobesubmitted inthe formof acompleteresearchproposal.The Referencesandthebibliography shouldbecitedinastandardformat.Theresearchproposal shouldcontaina)Topic ofresearchb)Backgroundandcurrentstatusoftheresearchworkinthe areaasevidentfromtheliteraturereviewc)Scopeoftheproposed workd)Methodology e) References and bibliography.
Areportcoveringlaboratory experiments,developmentalactivitiesandcodeof professional conduct andethics in discipline has to besubmitted byindividual student.
Thepanelwilljointly evaluateallthecomponentsofthecoursethroughoutthesemester and the mid semestergradewillbe announced bythe respectiveinstructor to his student.
Acomprehensiveviva/testwill be conductedatthe endofthesemesterjointly,wherever feasibleby allthepanelsinaparticularacademicdiscipline/department,inwhichintegrationof knowledge attained through various courseswillbetested and evaluated.
Wherever necessary and feasible, the panel coordinator in consultation with the concerned groupmayalsoseekparticipationofthefacultymembersfromothergroupsin lectures andcomprehensive viva.
Midsemesterreportandfinalevaluationreportshouldbesubmittedinthe9thweekand 15thweek of thesemesterrespectively. Theseshould contain the followingsections:
Wherever necessary,respective HeadoftheDepartmentscouldbeapproachedby Instructors/panelcoordinatorsforsmoothoperationofthecourse.Speciallecturesdealing with professional ethics in thediscipline mayalso be arranged bythegroup fromtime to time.
EVALUATION SCHEME
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Text Books And Reference Books: Reference meterials are based on their selected topic | |||||||||||||||||||
Essential Reading / Recommended Reading Research papers from their interested domain | |||||||||||||||||||
Evaluation Pattern Internal 50 Marks | |||||||||||||||||||
MTCS232 - COMPUTER COMMUNICATION NETWORKS (2018 Batch) | |||||||||||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:100 |
Credits:3 |
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Course Objectives/Course Description |
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· To understand the concepts of internetwork. · To study the functions of different layers. · To introduce IEEE standards employed in computer networking. · To make the students to get familiarized with different protocols and network components. |
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Course Outcome |
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· Explain the importance of data communications and the Internet in supporting business communications and daily activities. · Explain how communication works in data networks and the Internet. · Recognize the different internetworking devices and their functions. · Explain the role of protocols in networking. · Analyze the services and features of the various layers of data networks. · Design, calculate, and apply subnet masks and addresses to fulfill networking requirements. · Analyze the features and operations of various application layer protocols such as ARP, DHCP, ICMP, HTTP, DNS, SNMP and SMTP. · Analyze the security issues in the networks. |
Unit-1 |
Teaching Hours:12 |
INTRODUCTION
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Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-Effective Resource sharing, Support for Common Services, Manageability, Protocol layering, Performance, Bandwidth and Latency, Delay X Bandwidth Product, Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-Wait , Sliding Window, Concurrent Logical Channels.
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Unit-2 |
Teaching Hours:12 |
INTERNETWORKING- I
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Switching and Bridging, Datagram’s, Virtual Circuit Switching, Source Routing, Bridges and LAN Switches, Basic Internetworking (IP), Service Model, Global Addresses, Datagram Forwarding in IP, subnetting and classless addressing, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels. | |
Unit-3 |
Teaching Hours:12 |
INTERNETWORKING- II
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Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version 6(IPv6), Mobility and Mobile IP. | |
Unit-4 |
Teaching Hours:12 |
NETWORK SECURITY
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Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues, Segment Format, Connecting Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Queuing Disciplines, FIFO, Fair Queuing, TCP Congestion Control, Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery. | |
Unit-5 |
Teaching Hours:12 |
APPLICATIONS
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Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED), Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network Management(SNMP). | |
Text Books And Reference Books: TEXTBOOKS 1. Larry Peterson and Bruce S Davis “Computer Networks: A System Approach” 5th Edition, Elsevier -2014 2. Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and Architecture” 6th Edition, PHI – 2014
REFERENCE BOOKS 1. Uyless Black “Computer Networks, Protocols, Standards and Interfaces” 2nd Edition – PHI 2. Behrouz A Forouzan “TCP /IP Protocol Suite” 4th Edition – Tata McGraw-Hill 3. Andrew S. Tanenbaum, “Computer Networks”, Pearson Education 4th edition, 2012. 4. Larry L.Peterson and Brule S.Davie, “Computer Networks – A System Approach” MarGankangmann – Harcourt Asia, Fifth Edition, 2011. 5. William Stallings, “SNMP, SNMP V2, SNMPV3, RMON 1 and 2”, Pearson 2006 6.J.F Kurose and K.W. Ross, “Computer Networking –A top –down approach featuring the internet”, Pearson, 2012. 7.William Stallings, “Data & Computer Communication”, 6th Edition, Pearson Education, 2007. 8.Mani Subramanian, “Network Management: Principles and Practice”, Addison Wesley, 2000.
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Essential Reading / Recommended Reading | |
Evaluation Pattern internal 50 Marks External 50 Marks | |
MTCS233 - VERY LARGE DATABASE MANAGEMENT SYSTEMS (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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1. To learn the fundamentals of data models and to conceptualize and depict a database system using ER diagram. 2. To understand the internal storage structures using different file and indexing techniques which will help in physical DB design. 3. To know the fundamental concepts of transaction processing- concurrency control techniques and recovery procedure. 4. To have an introductory knowledge about the emerging trends in the area of distributed DBOO DB- Data mining and Data Warehousing and XML. |
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Course Outcome |
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1. Identify and define the information that is needed to design a database management system for a business information problem. 2. Create conceptual and logical database designs for a business information problem. 3. Build a database management system that satisfies relational theory and provides users with business queries, business forms, and business reports. 4. Understand the core terms, concepts, and tools of relational database management systems, distributed database, parallel database, spatial database, multimedia database and emerging technologies. 5. Work in teams and utilize effective group techniques to manage a complex project. 6. Understand research issues in this field. |
Unit-1 |
Teaching Hours:12 |
DISTRIBUTED DATABASE
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Distributed Databases Vs. Conventional Databases-Architecture-Fragmentation-Query Processing-Transaction Processing-Concurrency Control-Recovery. | |
Unit-2 |
Teaching Hours:12 |
OBJECT ORIENTED DATABASE
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Introduction to Object Oriented Databases-Approaches-Modeling and Design-Persistence-Query Languages-Transaction-Concurrency-Multiversion Locks-Recovery | |
Unit-3 |
Teaching Hours:12 |
EMERGING SYSTEMS
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Enhanced Data Models-Client/ Server Model-Data warehousing and Data mining-Web database-Mobile databases-Introduction to BigData. | |
Unit-4 |
Teaching Hours:12 |
DATABASE DESIGN ISSUES
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ER Model-Normalization-Security-Integrity-Consistency-Database Tuning-Optimization and Research Issues-Design of Temporal Databases-Spatial Databases | |
Unit-5 |
Teaching Hours:12 |
CURRENT ISSUES
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Rules-Knowledge bases-Active and Deductive Databases-Parallel Databases-Multimedia Databases-Image Databases-Text Databases-Case Studies-Research Issues-IEEE/ACM journal/Proceedings study | |
Text Books And Reference Books: TEXT BOOK 1. R. Elmasri and S.B. Navathe, “Fundamentals of Database Systems”, 6th Edition, Addison Wesley, 2010 2. Abraham Silberschatz, Henry. F. Korth, S.Sudharsan, “Database System Concepts”, 6th Edition. Tata McGraw Hill, 2010 3. Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014 4. Stefano Ceri & Giuesppe Pelagatti, Distributed Databases - Principles and Systems, McGraw Hill Book Company, 2008. 5. C.J.Date, “An Introduction to Database system”, Pearson Education, 7th Edition, 2006 REFERENCE BOOKS 1. Raghu Ramakrishnan & Johannes Gehrke, “Database Management Systems”, 3rd Edition, TMH, 2003 2. Philip M. Lewis, Arthur Bernstein, Michael Kifer, “Databases and Transaction Processing: An Application-Oriented Approach”, Addison-Wesley, 2002Jim Buyens, Step by Step Web Database Development, PHI, 2002. | |
Essential Reading / Recommended Reading Big Data for Dummies Thomas Connolly, Carolyn Begg, Database Systems: A Practical Apprach to Design, Implementation and Management | |
Evaluation Pattern Internal 50 Marks External 50 Marks | |
MTCS234 - DATA SCIENCE (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:12 |
INTRODUCTION AND THE DATA SCIENCE PROCESS
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Big Data and Data Science Hype, Getting Past the Hype, A Data Science Profile, Thought Experiment: Meta-Definition. Statistical Thinking in the Age of Big Data, Exploratory Data Analysis, The Data Science Process. | |
Unit-2 |
Teaching Hours:12 |
ALGORITHMS, SPAM FILTERS, NAIVE BAYES, AND WRANGLING
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Machine Learning Algorithms, Three Basic Algorithms, Exercise: Basic Machine Learning Algorithms, Thought Experiment: Automated Statistician, Naive Bayes, Comparing Naive Bayes to k-NN, Jake’s Exercise: Naive Bayes for Article Classification. | |
Unit-3 |
Teaching Hours:12 |
LOGISTIC REGRESSION, TIME STAMPS AND FINANCIAL MODELING
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Classifiers, M6D Logistic Regression Case Study, Kyle Teague and GetGlue Timestamps, Financial Modeling, The Kaggle Model, Feature Selection David Huffaker: Google’s Hybrid Approach to Social Research. | |
Unit-4 |
Teaching Hours:12 |
DATA VISUALIZATION, FRAUD DETECTION, SOCIAL NETWORKS AND DATA JOURNALISM
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Data Visualization History, Data Science and Risk, Data Visualization at Square, Social Network Analysis at Morning Analytics. Social Network Analysis, Terminology from Social Networks, Data Journalism. Epidemiology: Madigan’s Background, Modern Academic Statistics. Medical Literature and Observational Studies. | |
Unit-5 |
Teaching Hours:12 |
DATA ENGINEERING, NEXT-GENERATION DATA SCIENTISTS, HUBRIS, AND ETHICS
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MapReduce, Word Frequency Problem, Pregel, Economic Interlude, Case Studies - Hadoop: Starting with Hadoop, Data Science, Next-Gen Data Scientists, Ethical Data Scientist. | |
Text Books And Reference Books: Text Books: 1. Cathy O'Neil, Rachel Schutt, “Doing Data Science: Straight Talk from the Frontline”, O'Reilly Media, 2013. Reference Books: 1. John Hopcroft and R Kannan, “Foundations of Data Science”, Microsoft Research, 2014. 2. Jeffrey Stanton, “An Introduction to Data Science”, Syraccuse University, 2012. 3. Calvin.Andrus, Jon Cook, Suresh Sood, “Data Science: An Introduction”, Wikibooks, 2014. | |
Essential Reading / Recommended Reading NPTEL lectures on data science | |
Evaluation Pattern Internal 50 Marks External 50 Marks | |
MTCS235 - ADVANCED RESEARCH METHODOLOGY (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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● To orient the student to make an informed choice from the large number of alternative methods and experimental designs available. ● To enable the student to present a good research proposal. ● To familiarize the student with the nature of research and scientific writing
● To empower the student with the knowledge and skills they need to undertake a research project, to present a conference paper and to write a scientific article. |
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Course Outcome |
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● To define research and describe the research process and research methods. ● To know how to apply the basic aspects of the research process in order to plan and execute a research project. To plan a research project on a topic relevant to subject area of interest and write a draft research proposal for this research project ● To effectively use the library and its resources in gathering information related to the learners' research project. To find two international journal articles relevant to their topic of research; two national journal articles relevant to their topic of research; two newspaper articles relevant to their topic of research; two dissertations relevant to their topic of research ● To know how to perform basic operations with Excel spreadsheats. To draw graphs and diagrams using Excel and insert these graphs and diagrams into Word ● To use SPSS to conduct statistical analyses. Summarise and describe data by computing descriptive statistics, compiling frequency tables and drawing graphs; determine relationships between variables by computing correlations and regression equations; compare means of populations by using t-tests, ANOVA and post hoc comparisons; determine the effects of more than one factor and correction of co-variables by interpreting ANOVA and ANCOVA tables; determine assumptions of statistical analyses; compute non-parametric statistics and interpret test statistics; assess the construct validity and reliability of a questionnaire/test; design an experiment and do sampling ● To be able to use SPSS to perform multivariate statistical analyses, including canonical analysis, multiple regression analysis, logistic regression analysis, exploratory factor analysis, one-way analysis of variance, and multivariate analysis of variance. OR To be able to use EViews and STATA for simple regression and descriptive data analysis. Select the correct statistical techniques for a specific design, study population, apply SPSS to perform the analyses, apply guidelines to assess effects, report results correctly in tables, and interpret results correctly. OR Use a predetermined dataset and perform basic regression analysis and descriptive statistics and print and interpret these To be able to present, review and publish scientific articles. To evaluate the quality of scientific articles according to specific criteria and write a research proposal according to the guidelines ● To be able to prepare for and present a conference paper/poster at a national/international conference. |
Unit-1 |
Teaching Hours:12 |
Research Methodology:
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An Introduction Meaning of Research , Objectives of Research , Motivation in Research , Types of Research , Research approaches ,Research Method versus Methodology ,Research and Scientific Method, Importance of Knowing How Research is Done , Research Process, Criteria of Good Research, problem Encountered by Researchers in India .
Defining the Research Problem: Definition of Research Problem, Selecting the Problem, Necessity of Defining the Problem Technique Involved in Defining a Problem | |
Unit-2 |
Teaching Hours:12 |
Measurement and Scaling Technique
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Measurement in Research, Measurement Scales, Sources of Error in Measurement, Tests of Sound Measurement, Technique of Developing Measurement Tools, Scaling, Meaning of Scaling, Scale Classification Bases, Important Scaling Techniques, Scale Construction Techniques. | |
Unit-2 |
Teaching Hours:12 |
Processing and Analysis of Data
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Processing Operations, Some Problems in Processing, Elements /Types of Analysis, Statistics in Research, Measures of Central Tendency, Measures of Dispersion Measures of Asymmetry (Skewness), Measures of Relationship, Partial Correlation, Association in case of Attributes, Other Measures. | |
Unit-3 |
Teaching Hours:12 |
Analysis of algorithm
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The role of algorithm in computing –Insertion sort–Analyzing and designing algorithms –growth of functions–introduction to NP –completeness | |
Unit-4 |
Teaching Hours:12 |
Analysis of Variance and Covariance
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Analysis of variance(ANOVA), basic principles , technique, setting up analysis of variance table, short cut method for one- way ANOVA, coding method, two-way-ANOVA, ANOVA in Latin-Square-Design, Analysis of Co-variance(ANOCOVA), technique, assumption in ANOCOVA. | |
Unit-4 |
Teaching Hours:12 |
Sampling Fundamentals
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Need for Sampling, Some Fundamental Definitions, Central Limit Theorem, Sampling Theorem, Sandler’s A-test, Concept of Standard Error, Estimation, Estimating the Population Mean, Estimating the Population Proportion, Sample size and its Determination, Determination of Sample Size through the Approach, Based on Precision Rate and Confidence Level, Determination of Sample Size through the Approach, Based on Bayesian Statistics. | |
Unit-5 |
Teaching Hours:12 |
Interpretation and Report Writing
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Meaning of Interpretation, Technique of Interpretation: Precaution in Interpretation, Significance of Report Writing, Different Steps in Writing Report, Layout of the Research Report, Types of Reports, Oral Presentation, Mechanics of Writing a Research Report, Precautions for Writing a Research Report, Case study . | |
Text Books And Reference Books: Kothari C.R. , Research Methodology – Methods and Techniques, New Age International , New Delhi, (reprint 2011) Montgomery, Douglas C., Design and Analysis of Experiments, Willey India, 2007 Montgomery, Douglas C. & Runger, George C. ,Applied Statistics & Probability for Engineers, Wiley India , 2010 | |
Essential Reading / Recommended Reading Krishnaswamy, K.N. Sivkumar , Appa Iyer and Mathiranjan M., Management Research Methodology: Integration of Principles, Method and Techniques, Pearson Education, New Dehli, 2006 Charlie Catlett, Wolfgang Gentzsch, Lucio Grandinetti, Gerhard Joubert, and José Luis Vasquez-Poletti, Cloud computing and big data , Published/Distributed:Amsterdam : Washington, DC : IOS Press, [2013] | |
Evaluation Pattern Internal 50 Marks External 50 Marks | |
MTCS251 - NETWORKING LABORATORY (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:45 |
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Design, develop the project to implement following areas in networks
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Text Books And Reference Books:
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Essential Reading / Recommended Reading | |||||||||||
Evaluation Pattern internal 25 External 25 | |||||||||||
MTCS252 - DATABASE TECHNOLOGY LABORATORY (2018 Batch) | |||||||||||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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To study Data Defition, Control and Manipulation languages. To maintain database. To understand database design contraints. |
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Course Outcome |
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To experiment SQL for various application Domain. Use of PL/SQL , trigger and cursor in database design. |
Unit-1 |
Teaching Hours:60 |
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List of Programs
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1. 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 | ||
Text Books And Reference Books: 1.Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014 2. Philip M. Lewis, Arthur Bernstein, Michael Kifer, “Databases and Transaction Processing: An Application-Oriented Approach”, Addison-Wesley, 2002Jim Buyens, Step by Step Web Database Development, PHI, 2002. | ||
Essential Reading / Recommended Reading
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Evaluation Pattern Internal 25 External 25 | ||
MTCS271 - PROFESSIONAL PRACTICE - II (2018 Batch) | ||
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:
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Course Outcome |
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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: |
Unit-1 |
Teaching Hours:30 |
COURSE NOTICES
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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. | |
Text Books And Reference Books: Selected domain related text book will be sugessted. | |
Essential Reading / Recommended Reading Research papers for the selected domain | |
Evaluation Pattern Internal 50 | |
MTMA233 - MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE (2018 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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The course aims to develop the skills of the students in the areas of pure Mathematics problems and hence developing a strong logical concept. This paper will extend student’s mathematical maturity and ability to deal with abstraction and to introduce most of the basic terminologies used in computer science courses and application of ideas to solve practical problems. It paper will be very helpful for them to consider it as a prerequisite in order to pursue a career in research and development. |
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Course Outcome |
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At the end of the course the students would be capable of mathematically formulating certain practical problems in terms of combinatorics and physically interpret the results. Also students would be capable of developing and understanding algebraic structures, Algorithms skills.
The objective of this paper is to: · help the students in understanding the fundamental subject of Number Theory. · have knowledge of the concepts needed to test the logic of a program. be exposed to concepts and properties of algebraic structures |
Unit-1 |
Teaching Hours:12 |
Logic
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Propositional logic – Logical connectives – Truth tables – Normal forms ( conjunctive and disjunctive) - Predicate logic - Universal and existential quantifiers - Proof techniques –– Mathematical Induction. | |
Unit-2 |
Teaching Hours:14 |
Elementary Number Theory
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Divisibility, gcd, prime numbers, fundamental theorem of arithmetic, Congruences, Fermat's theorem, Euler function, primality testing, solution of congruences, Chinese remainder theorem, Wilson’s theorem. | |
Unit-3 |
Teaching Hours:10 |
Algebraic Structure
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Groups, Definition and examples, Cyclic groups, Permutation group (Sn and Dn), Subgroups, Cosets, Lagrange’s Theorem . | |
Unit-4 |
Teaching Hours:14 |
Advanced Counting Techniques
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Fundamental principles of counting, pigeonhole principle, principle of inclusion and exclusion, Solving Linear Recurrence Relations, Divide-and-Conquer Algorithms and Recurrence Relations, generating functions, Solve Recurrence Relations using Generating Functions | |
Unit-5 |
Teaching Hours:10 |
Boolean Algebra
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Relations, Representation of Relations, equivalence relations and partitions, partial order, lattices and Boolean algebra. | |
Text Books And Reference Books:
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Essential Reading / Recommended Reading 1. David M. Burton, “Elementary Number Theory”, Tata McGraw – Hill Publishing, 2007 2. R.P. Grimaldi, “Discrete and Combinatorial Mathematics”, 5th Edition, Pearson Publication, New Delhi 2009. | |
Evaluation Pattern
End Semester Examination (ESE) : The ESE is conducted for 100 marks of 3 hours duration. The syllabus for this paper is divided into FIVE units and each unit carries equal weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choice, will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is broadly based on the following criteria: 50 % - To test the objectiveness of the concept 30 % - To test the analytical skill of the concept 20 % - To test the application skill of the concept Mid Semester Examination (MSE) : The MSE is conducted for 50 marks of 2 hours duration. Question paper pattern; Five out of Six questions have to be answered. Each question carries 10 marks. | |
CY01 - CYBER SECURITY (2017 Batch) | |
Total Teaching Hours for Semester:30 |
No of Lecture Hours/Week:2 |
Max Marks:50 |
Credits:2 |
Course Objectives/Course Description |
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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. |
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Course Outcome |
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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. |
Unit-1 |
Teaching Hours:6 |
Unit-I
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Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities | |
Unit-2 |
Teaching Hours:6 |
Unit-II
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Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities - Server Hardening | |
Unit-3 |
Teaching Hours:6 |
Unit-III
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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. | |
Unit-4 |
Teaching Hours:6 |
Unit-IV
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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 | |
Unit-5 |
Teaching Hours:6 |
Unit-V
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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 And Reference Books:
TEXT BOOKS:
REFERENCES:
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Essential Reading / Recommended Reading Research papers from reputed journals. | |
Evaluation Pattern Internal 50 Marks. | |
MTCS331E06 - BIO INFORMATICS (2017 Batch) | |
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:9 |
Introductuon
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The Central Dogma, The Killer Application, Parallel Universes, Watson’s Definition, Top Down Versus Bottom up, Information Flow, Convergence Databases, Data Management, Data Life Cycle, Database Technology, Interfaces, Implementation. | |
Unit-2 |
Teaching Hours:9 |
Networking
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Networks, Geographical Scope, Communication Models, Transmissions Technology, Protocols, Bandwidth, Topology, Hardware, Contents, Security, Ownership, Implementation, Management. | |
Unit-3 |
Teaching Hours:9 |
SEARCH ENGINES AND DATA VISUALIZATION
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The search process, Search Engine Technology, Searching and Information Theory, Computational methods, Search Engines and Knowledge Management, Data Visualization, sequence visualization, structure visualization, user Interface, Animation Versus simulation, General Purpose Technologies. | |
Unit-4 |
Teaching Hours:9 |
STATISTICS, DATA MINING AND PATTERN MATCHING
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Statistical concepts: Microarrays, Imperfect Data, Randomness, Data Analysis. Clustering and Classification, Data Mining, Methods, Selection and Sampling, Preprocessing and Cleaning, Transformation and Reduction, Data Mining Methods, Pattern Recognition and Discovery, Machine Learning, Text Mining, Pattern Matching: Dot Matrix analysis, Substitution matrices, Dynamic Programming, Word methods, Bayesian methods. | |
Unit-5 |
Teaching Hours:9 |
MODELING AND SIMULATION
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Drug Discovery, components, process, Perspectives, Numeric considerations, Algorithms, Hardware, Issues, Protein structure, Ab Initio Methods, Heuristic methods, Systems Biology, Tools, Case Studies : Collaboration and Communications, Standards, Security, Intellectual property. | |
Text Books And Reference Books:
TEXT BOOKS
REFERENCE BOOKS
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Essential Reading / Recommended Reading
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Evaluation Pattern Assessment of each paper · 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 | |
MTCS332E01 - MACHINE LEARNING (2017 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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Course Outcome |
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Unit-1 |
Teaching Hours:9 |
INTRODUCTION
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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 | |
Unit-2 |
Teaching Hours:9 |
SUPERVISED LEARNING
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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. | |
Unit-3 |
Teaching Hours:9 |
UNSUPERVISED LEARNING
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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 | |
Unit-4 |
Teaching Hours:9 |
PROBABILISTIC GRAPHICAL MODELS
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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 | |
Unit-5 |
Teaching Hours:9 |
ADVANCED LEARNING
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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 | |
Text Books And Reference Books: TEXT BOOKS
REFERENCES 1. Tom Mitchell, "Machine Learning", McGraw-Hill, 1997. | |
Essential Reading / Recommended Reading NPTEL Videos Journal Paper | |
Evaluation Pattern Internal Marks : 50% ESE Marks : 50% | |
MTCS371 - PROJECT WORK (PHASE I) (2017 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:3 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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During these minar session each student is expected to prepare and presentatopicon engineering/ technology, itis designed to:
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Course Outcome |
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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: |
Unit-1 |
Teaching Hours:45 |
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UNIT-1
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Text Books And Reference Books: Selected domain related text book will be sugessted. | |||
Essential Reading / Recommended Reading Research papers for the selected domain | |||
Evaluation Pattern § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Guide ¨ Mid semester Project Report | |||
MTCS373 - INTERNSHIP (2017 Batch) | |||
Total Teaching Hours for Semester:60 |
No of Lecture Hours/Week:4 |
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Max Marks:50 |
Credits:2 |
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Course Objectives/Course Description |
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Internships are short-term work experiences that will allow a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices. More specifically, doing internships is beneficial because they provide the opportunity to:
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Course Outcome |
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Unit-1 |
Teaching Hours:60 |
Regulations
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1.The student shall undergo an Internship for30 days starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester. 2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of the students 3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise. 4. The Internship shall be completed by the end of 2nd semesters. 5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance. 6. Students can also undergo internships arranged by the department during vacation. 7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.
8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor and a presentation on the internship given to department constituted panel. | |
Text Books And Reference Books: Related to the Internship domain text books are sugessted. | |
Essential Reading / Recommended Reading Readings Related to the Internship domain | |
Evaluation Pattern Internal 50 Marks | |
MTIT333E04 - OPERATIONS RESEARCH (2017 Batch) | |
Total Teaching Hours for Semester:45 |
No of Lecture Hours/Week:4 |
Max Marks:100 |
Credits:3 |
Course Objectives/Course Description |
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The objectives of this course are to: introduce students to the techniques of operations research in mining operations provide students with basic skills and knowledge of operations research and its application in mineral industry introduce students to practical application of operations research in big mining projects |
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Course Outcome |
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Upon successful completion of this course, the student will be able to: (Knowledge based) · Explain the meaning of operations research · Know the various techniques of operations research; · Apply the techniques used in operations research to solve real life problem in mining industry · Select an optimum solution with profit maximization; · Have complete understand of the significant role operation research play in mining project completion at every stage of the mines (Skills) · Use operations research to solve transportation problems during the allocation of trucks to excavators formulate operation research models to solve real life problem o proficiently allocating scarce resources to optimize and maximize profit o eliminate customers / clients waiting period for service delivery · Turn real life problems into formulation of models to be solve by linear programming etc. · Determine critical path analysis to solve real life project scheduling time and timely delivery · Use critical path analysis and programming evaluation production and review techniques for timely project scheduling and completion and · Conduct literature search on the internet in the use of operation research techniques in mining projects execution and completion. |
Unit-1 |
Teaching Hours:9 |
QUEUEING MODELS
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Poisson Process – Markovian Queues – Single and Multi-server Models – Little’s formula – Machine Interference Model – Steady State analysis – Self Service Queue. | |
Unit-2 |
Teaching Hours:9 |
ADVANCED QUEUEING MODELS
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Non- Markovian Queues – Pollaczek Khintchine Formula – Queues in Series – Open Queueing Networks –Closed Queueing networks. | |
Unit-3 |
Teaching Hours:9 |
SIMULATION
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Discrete Even Simulation – Monte – Carlo Simulation – Stochastic Simulation – Applications to Queueing systems. | |
Unit-4 |
Teaching Hours:9 |
LINEAR PROGRAMMING
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Formulation – Graphical solution – Simplex method – Two phase method Transportation and Assignment Problems. | |
Unit-5 |
Teaching Hours:9 |
NON-LINEAR PROGRAMMING
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Lagrange multipliers – Equality constraints – Inequality constraints – Kuhn – Tucker conditions – Quadratic Programming. | |
Text Books And Reference Books: TEXT BOOKS 1. Winston.W.L. “Operations Research”, Fourth Edition, Thomson – Brooks/Cole, 2003. 2. Taha, H.A. “Operations Research: An Introduction”, Ninth Edition, Pearson Education Edition, Asia, New Delhi, 2002. REFERENCES 1.Robertazzi. T.G. “Computer Networks and Systems – Queuing Theory and Performance Evaluation”, Third Edition, Springer, 2002 Reprint. | |
Essential Reading / Recommended Reading NTPEL Videos Journal Papers | |
Evaluation Pattern Internal Marks : 50% ESE Marks : 50 % | |
MTCS471 - PROJECT WORK (PHASE-II) AND DISSERTATION (2017 Batch) | |
Total Teaching Hours for Semester:120 |
No of Lecture Hours/Week:6 |
Max Marks:300 |
Credits:9 |
Course Objectives/Course Description |
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Objective of this course is to encourage students to do research oriented project. |
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Course Outcome |
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The students are expected to comeout with the product implemetation with dissertation details. |
Unit-1 |
Teaching Hours:120 |
Assessment of Project Work(Phase II) and Dissertation
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v § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Assessment by Guide § Dissertation (Exclusive assessment of Project Report): 100 Marks § End Semester Examination:100 Marks ¨ Viva Voce ¨ Demonstration ¨ Project Report | |
Text Books And Reference Books: Research articles from the identified domain | |
Essential Reading / Recommended Reading Research papers from reputed journals | |
Evaluation Pattern Internal 200 External 100 |