Kurukshetra University, Kurukshetra

(Established by the State Legislature Act XII of 1956)

(‘A+’ Grade, NAAC Accredited)

 

||     योगस्थ:  कुरु कर्माणि     ||

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(Perform Actions while Stead fasting in the State of Yoga)

Kurukshetra University - Wikipedia

 

Scheme of Examination and Syllabus of 

Master of Technology (M.Tech.) in Computer Science & Engineering(CSE) (CBCS) in Phased Manner

 

DEPARTMENT OF COMPUTER SCIENCE & APPLICATIONS

 

CBCS CURRICULUM (2020-21)

Program Name: Master of Technology (M.Tech.) in Computer Science & Engineering (CSE) (CBCS)

(For the Batches Admitted From 2020-2021)

DEPARTMENT OF COMPUTER SCIENCE & APPLICATIONS

KURUKSHETRA UNIVERSITY, KURUKSHETRA

 

VISION

Pursue conducive advancement towards nurturing globally competent and ethically conscientious professionals and entrepreneurs in agile computing technologies and allied spheres for unceasing evolution of Nations IT affiliated commercial and research endeavours.

 

MISSION

Thrive to establish a strong foundation for technical competency in spheres concordant to software oriented design and development. Nurture skills and competency for administering expertise gained in computing discipline to a wide horizon of interdisciplinary application domains, thus supporting sustainable development of the society. Habituate the students to strive for technological innovations and successful endeavours ethically, supported by sustained learning continuance and problem solving proficiency that may promote nations welfare in terms of economic acceleration leading to the growth of society. 

 

NAME OF THE PROGRAMME: MASTER OF TECHNOLOGY

(COMPUTER SCIENCE AND ENGINEERING)

DURATION         : TWO YEARS

 

PROGRAMME OUTCOMES (POs)

PO1

Capable of demonstrating comprehensive disciplinary knowledge gained during course of study

PO2

Capability to ask relevant/appropriate questions for identifying, formulating and analyzing the research problems and to draw conclusion from the analysis

PO3

Ability to communicate effectively on general and Technical topics with the engineering community and with society at large  

PO4

Capability of applying knowledge to solve Engineering and other problems  

PO5

Capable to learn and work effectively as an individual, and as a member or leader in diverse teams, in multidisciplinary settings.  

PO6

Ability of critical thinking, analytical reasoning and research based knowledge including design of experiments, analysis and interpretation of data to provide conclusions

PO7

Ability to use and learn techniques, skills and modern tools for scientific and engineering practices

PO8

Ability to apply reasoning to assess the different issues related to society and the consequent responsibilities relevant to the professional Engineering practices

PO9

Aptitude to apply knowledge and skills that are necessary for participating in learning activities throughout life

PO10

Capability to identify and apply ethical issues related to one’s work, avoid unethical behaviour such as fabrication of data, committing plagiarism and unbiased truthful actions in all aspects of work

PO11

Ability to demonstrate knowledge and understanding of the engineering principles and apply these to manage projects 

 

PROGRAMME SPECIFIC OUTCOMES (PSOs)

PSO1

Supplement potential for pursuing advanced studies, engaging in research & technological development directed towards innovative activities, and nurturing entrepreneurial skills.

PSO2

Strengthen competency for innovating solutions to real-world problems by exercising data analysis skills and adopting contemporary technologies for demanding prospective applications.

PSO3

Inculcate the practice to administer Professional & Ethical virtues, along with Social and Environmental regulations.

PSO4

Stimulate the aptitude for problem analysis and programming skills for computer based system design and modeling in allied spheres related to Algorithmic, Computational, Architectural and Database environments, along with emerging technologies such as Machine Learning& Intelligent systems ,  Evolutionary Techniques and Optimization, Data Science & Analytics, Distributed and Wireless Communication in cognation with  IoT and Cloud Computing , Web and Mobile application designing, and   Real World Enhancement using Computer Vision & Augmented Reality.

 



KURUKSHETRA UNIVERSITY, KURUKSHETRA

 

SCHEME OF EXAMINATION FOR MASTER OF TECHNOLOGY 

(COMPUTER SCIENCE AND ENGINEERING)

CHOICE BASED CREDIT SYSTEM (CBCS)

W. E. F. ACADEMIC SESSION 2020-21 IN PHASED MANNER

 

Paper Code

Nomenclature of Paper

Credits

Workload Per Week (Hrs.)

Exam Time (Hrs.)

External Marks

Internal Marks

Total Marks

Pass Marks

Max.

Pass

First Semester

MT-CSE-20-11

Mathematical Foundations of Computer Science

4

4

3

75

30

25

100

40

MT-CSE-20-12

Advanced Data Structures

4

4

3

75

30

25

100

40

MT-CSE-20-13

Elective- I

4

4

3

75

30

25

100

40

MT-CSE-20-14

Elective- II

4

4

3

75

30

25

100

40

MT-CSE-20-15

Research Methodology and IPR

3

3

3

75

30

25

100

40

MT-CSE-20-16

S/W Lab–I Based on MT-CSE-20-12

2.5

5

3

100

40

100

40

MT-CSE-20-17

S/W Lab-II Based on MT-CSE-20-13

2.5

5

3

100

40

100

40

Total

24

29

 

575

230

125

700

280

Elective – I

MT-CSE-20-13(i)

Machine Learning using Python

4

4

3

75

30

25

100

40

MT-CSE-20-13(ii)

Data Science using Python

4

4

3

75

30

25

100

40

MT-CSE-20-13(iii)

Wireless Sensor Networks

4

4

3

75

30

25

100

40

MT-CSE-20-13(iv)

Advanced Database Systems

4

4

3

75

30

25

100

40

Elective – II

MT-CSE-20-14(i)

Intelligent Systems

4

4

3

75

30

25

100

40

MT-CSE-20-14(ii)

Distributed Systems

4

4

3

75

30

25

100

40

MT-CSE-20-14(iii)

Computer Vision and Augmented Reality

4

4

3

75

30

25

100

40

MT-CSE-20-14(iv)

Advanced Computer Architecture

4

4

3

75

30

25

100

40

Second Semester

MT-CSE-20-21

Advances in Algorithms

4

4

3

75

30

25

100

40

MT-CSE-20-22

Soft Computing

4

4

3

75

30

25

100

40

MT-CSE-20-23

Elective – III

4

4

3

75

30

25

100

40

MT-CSE-20-24

Elective – IV

4

4

3

75

30

25

100

40

MT-CSE-20-25

S/W Lab –III Based on MT-CSE-20-21

2.5

5

3

100

40

100

40

MT-CSE-20-26

S/W Lab –IV Based on MT-CSE-20-23

2.5

5

3

100

40

100

40

*OE-CSE-20-27

Open Elective Based on MOOCs ( The selected course should not be directly related with Computer Science )   Or As per University Guidelines

2

2

3

35

14

15

50

20

Total

23

28

 

535

214

115

650

260

Elective – III

MT-CSE-20-23(i)

Data Preparation and Analysis

4

4

3

75

30

25

100

40

MT-CSE-20-23(ii)

Optimization Techniques

4

4

3

75

30

25

100

40

MT-CSE-20-23(iii)

Advanced Wireless and Mobile Networks

4

4

3

75

30

25

100

40

MT-CSE-20-23(iv)

Networking and Administration in Linux / Unix

4

4

3

75

30

25

100

40

Elective – IV

MT-CSE-20-24(i)

Mobile Applications and Services

4

4

3

75

30

25

100

40

MT-CSE-20-24(ii)

Advanced Web Technologies

4

4

3

75

30

25

100

40

MT-CSE-20-24(iii)

Object-Oriented Software Engineering

4

4

3

75

30

25

100

40

MT-CSE-20-24(iv)

Big Data and Pattern Recognition

4

4

3

75

30

25

100

40

Third Semester

MT-CSE-20-31

Elective-V

4

4

3

75

30

25

100

40

MT-CSE-20-32

Dissertation-I / Industrial Project

10

20

200

80

50

250

100

*OE-CSE-20-33

Open Elective Based on MOOCs ( The selected course should not be directly related with Computer Science )   Or As Per University Guidelines

2

2

3

35

14

15

50

20

Total

16

26

 

310

124

90

400

160

Elective – V

MT-CSE-20-31(i)

Compiler for High Performance Computing

4

4

3

75

30

25

100

40

MT-CSE-20-31(ii)

Cloud Computing and IoT

4

4

3

75

30

25

100

40

MT-CSE-20-31(iii)

Information Retrieval System

4

4

3

75

30

25

100

40

MT-CSE-20-31(iv)

Digital Image Processing

4

4

3

75

30

25

100

40

Fourth Semester

MT-CSE-20-41

Dissertation-II

16

32

300

120

100

400

160

Total

16

32

300

120

100

400

160

Grand Total

79

115

1720

688

430

2150

860

















Note 1: Instructions for Examiners to award marks/grades for Dissertation – II :- 

 

The marks shall be awarded on the basis of three aspects, 

(i) Evaluation of Dissertation Report (ii) Viva-Voce and (iii) Publication/Presentation of Research Paper from the dissertation.

 

Part (i) and (ii) carries 75% of the total marks for both internal and external evaluation. Part (iii) carries 25% of the total marks for both internal and external evaluation.

Part (iii) marks shall be awarded using following criteria:

  1. Marks 91% or above: Publication from Dissertation in SCI indexed journal. 

  2. Marks 81% to 90%: Publication from Dissertation in Scopus indexed or Web of Science Indexed journal. 

  3. Marks 71% to 80%: Publication from Dissertation in Proceedings of Conference which is Scopus indexed/IEEE/ACM/Elsevier indexed or Publication from Dissertation in UGC Care List journal.

  4. Marks 61% to 70%: Presented paper in International Conference. 

  5. Marks 51% to 60%: Presented paper in National Conference.

 

*Note 2: In addition to the credits earned by compulsory and elective courses, every student has to earn 2 more 

  credits by selecting an open elective/MOOC course during second and third semester.

 

Note 3: The credits for the first year are 47(24+23) and for the second year are 32(16+16). Total credits of the 

  course shall be 47+32=79.

 

Note 4: For the purpose of computation of work-load the following mechanism shall be adopted:

  • 1 Credit = 1 Theory period of one hour duration.

  • 1 Credit = 1 Practical period of two hour duration.

 

Note 5: Evaluation procedure for internal assessment marks: 

Two Mid Term Examinations should be conducted by the concerned teacher each of 10 marks. Five 

marks may be given by the concerned teacher on the basis of performance during the course (puzzles/ assignments/ interactions/ attendance etc).

 

Note 6: Size of groups in all practical courses should not be more than thirty students.




MT-CSE-20-11: Mathematical Foundations of Computer Science

Type: Compulsory

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is make students understand the mathematical fundamentals that is prerequisites for a variety of courses like Data mining, Computer security, Software engineering, Computer architecture, distributed systems, Machine learning, etc.

Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-11.1

understand the basic notions of discrete and continuous probability;

MT-CSE-20-11.2

understand various sampling and classification problems;

MT-CSE-20-11.3

understand the methods of statistical inference, and the role that sampling distributions play in those methods;

MT-CSE-20-11.4

analyse graphs, permutation and combination and their use in various scenarios;

CO-PO Mapping Matrix for the Course Code : MT-CSE-20-11

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-11.1

3

2

1

2

1

1

1

1

2

1

1

MT-CSE-20-11.2

3

3

1

3

1

3

2

2

2

1

2

MT-CSE-20-11.3

3

3

1

3

1

3

2

2

2

1

2

MT-CSE-20-11.4

3

2

1

2

1

2

2

1

2

1

1

Average

3

2.5

1

2.5

1

2.25

1.75

1.5

2

1

1.5

CO-PSO Mapping Matrix for the Course Code : MT-CSE-20-11

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-11.1

1

2

1

3

MT-CSE-20-11.2

3

2

2

3

MT-CSE-20-11.3

3

3

2

3

MT-CSE-20-11.4

2

2

1

3

Average

2.25

2.25

1.5

3

Unit – I

Probability mass, density, and cumulative distribution functions, parametric families of distributions, Expected value, variance, conditional expectation, Applications of the univariate and multivariate, Central Limit Theorem, Probabilistic inequalities, Markov chains.

Unit – II 

Random samples, sampling distributions of estimators, Methods of Moments and Maximum Likelihood, Recent Trends in various distribution functions in mathematical field of computer science for varying fields.

Unit – III

Statistical inference, Introduction to multivariate statistical models: regression and classification problems, principal components analysis, the problem of over fitting model assessment.

Unit – IV 

Graph Theory: Isomorphism, Planar graphs, graph colouring, Hamilton circuits and Euler cycles.

Permutations and Combinations with and without repetition, Specialized techniques to solve combinatorial enumeration problems.

Text Books:

  1. John Vince, Foundation Mathematics for Computer Science, Springer.

  2. K. Trivedi, Probability and Statistics with Reliability, Queuing, and Computer Science Applications. Wiley.

Reference Books:

  1. M. Mitzenmacher and E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis, Cambridge University Press

  2.    Alan Tucker, Applied Combinatorics, Wiley.

 

 

 

MT-CSE-20-12: Advanced Data Structures

Type: Compulsory

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective is to make students able to understand the necessary mathematical abstraction to solve problems and to familiarize them with advanced paradigms and data structure used to solve algorithmic problems.

Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-12.1

understand the implementation of symbol table using hashing techniques;

MT-CSE-20-12.2

develop and analyse algorithms for red-black trees, B-trees and Splay trees;

MT-CSE-20-12.3

develop algorithms for text processing applications;

MT-CSE-20-12.4

identify suitable data structures and develop algorithms for computational geometry problems.

CO-PO Mapping Matrix for the Course Code : MT-CSE-20-12

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-12.1

3

3

1

3

1

3

3

1

3

1

2

MT-CSE-20-12.2

3

3

1

3

1

3

3

1

2

1

2

MT-CSE-20-12.3

3

3

2

3

1

3

2

2

3

1

3

MT-CSE-20-12.4

3

3

1

3

2

3

3

2

3

1

3

Average

3

3

1.25

3

1.25

3

2.75

1.5

2.75

1

2.5

CO-PSO Mapping Matrix for the Course Code : MT-CSE-20-12

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-12.1

3

2

2

3

MT-CSE-20-12.2

3

1

1

2

MT-CSE-20-12.3

3

2

2

3

MT-CSE-20-12.4

3

2

2

3

Average

3

1.75

1.75

2.75

Unit – I

Dictionaries: Definition, Dictionary Abstract Data Type, Implementation of Dictionaries. Hashing: Review of Hashing, Hash Function, Collision Resolution Techniques in Hashing, Separate Chaining, Open Addressing, Linear Probing, Quadratic Probing, Double Hashing, Rehashing, Extendible Hashing.

Unit – II 

Trees: Binary Search Trees, AVL Trees, Red Black Trees, 2-3 Trees, B-Trees, Splay Trees. Skip Lists: Need for Randomizing Data Structures and Algorithms, Search and Update Operations on Skip Lists, Probabilistic Analysis of Skip Lists, Deterministic Skip Lists.

Unit – III

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

Unit – IV 

Computational Geometry:  One Dimensional Range Searching, Two Dimensional Range Searching, Constructing a Priority Search Tree, Searching a Priority Search Tree, k-D Trees.

Text Books:

  1. Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, Pearson Education.

  2.    M T Goodrich, Roberto Tamassia, Algorithm Design, John Wiley.

Reference Books:

  1.    Cormen, Leiserson, Rivest, “Introduction to Algorithms”, PHI India.

 

 

MT-CSE-20-13(i): Machine Learning using Python

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to enable student to perform experiments in Machine Learning using real-world data using Python.

Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-13(i).1

construct and execute various programs using different data structures in Python;

MT-CSE-20-13(i).2

use the Python programming for machine learning;

MT-CSE-20-13(i).3

understand the machine learning along with concept learning and decision trees;

MT-CSE-20-13(i).4

understand Bayesian, Computational and Instance-based learning.

CO-PO Mapping Matrix for the Course Code : MT-CSE-20-13(i)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-13(i).1

3

3

2

3

2

3

3

1

3

1

3

MT-CSE-20-13(i).2

3

3

2

3

2

3

3

1

3

1

3

MT-CSE-20-13(i).3

3

3

2

2

1

2

2

3

3

1

2

MT-CSE-20-13(i).4

3

3

2

2

1

2

2

3

3

1

2

Average

3

3

2

2.5

1.5

2.5

2.5

2

3

1

2.5

CO-PSO Mapping Matrix for the Course Code : MT-CSE-20-13(i)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-13(i).1

3

3

3

3

MT-CSE-20-13(i).2

3

3

3

3

MT-CSE-20-13(i).3

2

3

3

3

MT-CSE-20-13(i).4

2

3

3

3

Average

2.5

3

3

3

Unit – I

Python Programming: Strings – String slices, immutability, string functions and methods, string module; Lists, Tuples, Dictionaries: Lists – Lists as arrays Traversing a List, list operations, list slices, list methods, Map, Filter and Reduce, list loop, mutability, aliasing, cloning lists, list parameters; Dictionaries – operations and methods; advanced list processing – list comprehension; Tuples – tuple assignment, tuple as return value.

Files and Modules: Files and exception – text files, reading and writing files, format operator; command line arguments, errors and exceptions, handling exceptions, modules.

Unit – II 

Packages in Python: PANDAS, NUMPY, SCIKIT-LEARN, MATPLOTLIB.

NumPy – Introduction, Ndarray Object ,Data types, Array Attributes, Array Creation Routines, Indexing & Slicing, Advanced Indexing, Broadcasting, Iterating Over Array, Array Manipulation, Binary Operators, String Functions, Mathematical Functions, Mathematical Functions, Arithmetic Operations, Statistical Functions, Linear Algebra, Matplotlib(Used for data visualization), Histogram Using Matplotlib.

Pandas: Performing data cleaning and analysis, Loading data with Pandas (data manipulation and analysis), Working with and Saving data with Pandas.

Using Scikit-Learn for Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, KNN, SVN, k Mean Clustering, Random Forest.

Unit – III

Introduction to Machine Learning – Well defined learning problems, Designing a Learning System, Issues in Machine Learning.

The Concept Learning Task – General-to-specific ordering of hypotheses, Find-S, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias

Decision Tree Learning – Decision tree learning algorithm-Inductive bias- Issues in Decision tree learning.

Unit – IV 

Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm.

Computational Learning Theory: Sample Complexity for Finite Hypothesis spaces, Sample Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning.

Instance-Based Learning – k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning.

Text Books:

  1. Tom M. Mitchell, Machine Learning, McGraw-Hill Education (India) Private Limited. 

  2. Ethem Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press.

  3. John V Guttag, Introduction to Computation and Programming Using Python, MIT Press.

  4. Robert Sedgewick, Kevin Wayne, Robert Dondero, Introduction to Programming in Python: An Inter-disciplinary Approach, Pearson India Education Services Pvt. Ltd.

Reference Books:

  1.    Stephen Marsland, Machine Learning: An Algorithmic Perspective, CRC Press.

  2.    Allen B. Downey, Think Python: How to Think Like a Computer Scientist, Updated for Python 3, Shroff/O„Reilly Publishers.                                                                                                                         

  3.    Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press.

  4.    Sebastian Raschka, Python Machine Learning. 

 

 

MT-CSE-20-13 (ii) : Data Science using Python

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester examination: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: Provide you with the knowledge and expertise to become a proficient data scientist. Demonstrate an understanding of statistics and machine learning concepts that are vital for data science. Student will learn Python code to statistically analyse a dataset.


Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-13 (ii).1

learn data collection, management and storage methods for data science;

MT-CSE-20-13 (ii).2

understand the implementation of machine learning algorithms;

MT-CSE-20-13 (ii).3

acquire knowledge of visualization techniques used by data scientists;

MT-CSE-20-13 (ii).4

implement data collection and management scripts using Python.


CO-PO Mapping Matrix for Course Code: MT-CSE-20-13 (ii)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-13 (ii).1

3

3

3

3

2

2

3

3

2

2

1

MT-CSE-20-13 (ii).2

3

3

3

3

3

1

2

3

3

3

2

MT-CSE-20-13 (ii).3

3

2

2

1

2

2

1

3

2

3

1

MT-CSE-20-13 (ii).4

3

3

3

2

3

3

1

2

3

2

1

Average

3

2.75

2.75

2.25

2.5

2

1.75

2.75

2.5

2.5

1.25

CO-PSO Mapping Matrix for Course Code: MT-CSE-20-13 (ii)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-13 (ii).1

3

2

1

3

MT-CSE-20-13 (ii).2

3

3

2

2

MT-CSE-20-13 (ii).3

3

2

1

3

MT-CSE-20-13 (ii).4

3

3

3

2

Average

3

2.5

1.75

2.5

Unit – I 

Introduction to core concepts and technologies: Introduction, Data Science Process, Data Science Toolkit, Types of Data, Example Applications. Data Collection and Management: Sources of data, Data collection and APIs, Exploring and Fixing Data, Data Storage and Management, Using Multiple Data Sources.

Unit – II 

Data Analysis: Introduction, Terminology and Concepts, Introduction to Statistics, Central Tendencies and Distributions, Variance, Distribution Properties and Arithmetic, Samples/CLT, Basic Machine Learning Algorithms, Linear Regression, SVM, Naive Bayes, Applications of Data Science.

Unit – III

Data   Visualisation: Introduction, Types of Data Visualisation, Data for Visualisation: Data Types, Data Encodings, Retinal Variables, Mapping Variables to Encodings, Visual Encodings, Technologies for Visualisation, Recent Trends in Various Data Collection and Analysis Techniques. Application Development Methods of Used in Data Science.

Unit – IV 

Python Programming: Python Strings, Operators, Functions, Control Structures, Mutable and Immutable Objects, Recursion, Files and Exception, Classes, List Manipulation, Applications of Python.

Text Books:

  1. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline. O’Reilly.

  2. Sheetal Taneja, Naveen Kumar, Python Programming, Pearson.

References Books:

  1. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press.

  2. G Dong and J Pei, Sequence Data Mining, Springer.

 

 




MT-CSE-20-13(iii): Wireless Sensor Networks

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester examination: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of the course is to provide a comprehensive understanding of the fundamental concepts and architecture of WSNs along with major issues, and effective solutions in wireless sensor networking.


Course Outcomes: At the end of this course, the student will be able to:

MT-CSE-20-13(iii).1

understand the fundamental concepts of wireless sensor networks and have a basic knowledge of its components, characterization and categorization, along with design objectives, challenges, applications and technological background of Wireless Sensor Networks;

MT-CSE-20-13(iii).2

get familiar with the Media Access Control and Transport Protocols for Wireless Sensor Networks;

MT-CSE-20-13(iii).3

get research directions for pertinent design issues of Wireless Sensor Networks such as Routing and energy efficiency;

MT-CSE-20-13(iii).4

have an insight into the directions for carrying out research activities to explore and solve issues related to data aggregation, node localization, synchronization and security in Wireless Sensor Networks.  

 


CO-PO Mapping Matrix for Course Code: MT-CSE-20-13(iii)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-13(iii).1

3

3

3

2

2

2

2

2

3

2

MT-CSE-20-13(iii).2

3

3

3

3

2

3

3

2

3

2

MT-CSE-20-13(iii).3

3

3

3

3

2

3

3

2

3

2

MT-CSE-20-13(iii).4

3

3

3

3

2

3

3

2

3

2

Average

3

3

3

2.75

2

2.75

2.75

2

3

2


CO-PSO Mapping Matrix for Course Code: MT-CSE-20-13(iii)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-13(iii).1

3

2

1

3

MT-CSE-20-13(iii).2

3

2

1

3

MT-CSE-20-13(iii).3

3

2

1

3

MT-CSE-20-13(iii).4

3

2

1

3

Average

3

2

1

3

Unit – I 

Introduction: Overview of Wireless Sensor Networks – Characteristics, Applications, Design objectives & challenges; Basic Components; Operating Systems for Wireless Sensor Networks; Quality of a Sensor Network: Coverage, Exposure.

Technological Background – MEMS Technology, Hardware and Software Platforms, Evolving Standards for Wireless Sensor Networks; Sensor Node Structure;  Network Architectures for Wireless Sensor Networks – Layered (UNPF) & Clustered (LEACH) ; Classification of Wireless Sensor Networks;  Protocol Stack for Wireless Sensor Networks.

Unit – II 

Media Access Control: Fundamental MAC protocols; MAC design issues for Wireless Sensor Networks; MAC Protocols for Wireless Sensor Networks; Contention–Free Protocols; Hybrid Protocols.

Transport Protocols: Transport Protocol Design Issues and Transport Protocols for Wireless Sensor Networks.

Unit – III

Routing and Data Dissemination: Fundamentals & Challenges; Taxonomy of Routing and Data Dissemination Protocols; Location-Aided Protocols; Layered and In-Network processing Based Protocols; Data-Centric Protocols; Multipath-Based Protocols; Mobility-based & Heterogeneity-Based Protocols ; QoS Based Protocols; Data gathering.

Broadcasting, Multicasting, and Geocasting:  Concepts, Major Challenges & Mechanisms.

Energy Efficiency: Need for Energy Efficiency; MAC layer and Higher Layers Power Conservation Mechanisms.

Unit – IV 

Data Aggregation in Wireless Sensor networks: Challenges & techniques; Node Clustering and its Algorithms in Wireless Sensor Networks.

Node Localization: Concepts, Challenges, & Algorithms; Ranging Techniques.

Time Synchronization: Need and Requirements of Synchronization in Wireless Sensor Networks; Synchronization Protocols for Wireless Sensor Networks.

Security Issues in Wireless Sensor networks:  Challenges of Security in Wireless Sensor Networks, Security Attacks in Sensor Networks, Protocols and Mechanisms for Security.

Future Trends in Wireless Sensor Networks.

Text Books:

  1. Jun Zheng, Abbas, Wireless Sensor Networks A Networking Perspective, Wiley. 

  2. Kazem Sohraby, Daniel Minoli, & Taieb Znati, Wireless Sensor Networks-Technology, Protocols, and Applications, Wiley 

Reference Books:

  1. W. Dargie and C. Poellabauer, Fundamentals of Wireless Sensor Networks –Theory and Practice, Wiley.

  2. Thomas Haenselmann, Wireless Sensor Networks: Design Principles for Scattered Systems, Oldenbourg Verlag 

  3. Waltenegus Dargie, Christian Poellabauer, Fundamentals of Wireless Sensor Networks: Theory and Practice, Wiley 

  4. Mohammad S. Obaidat, Sudip Misra, Principles of Wireless Sensor Networks, Cambridge, 

  5. C. Sivarm Murthy & B.S. Manoj, Adhoc Wireless Networks, PHI.




MT-CSE-20-13 (iv) : Advanced Database Systems

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week.

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: The objective of this course is to provide an in- depth knowledge of SQL and PL/SQL to design database for an organization. This course focuses on advance topics of the database including EER model, object oriented database, and emerging concepts of database.

Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-13 (iv).1

understand overall process of database modeling & design, Implementation;

MT-CSE-20-13 (iv).2

learn to write complex queries in SQL and to design PL/SQL blocks for database implementation; 

MT-CSE-20-13 (iv).3

acquire technical knowhow of the EER modelling, Query Optimization, Transaction management, database backup and recovery along with emerging databases management systems;

MT-CSE-20-13 (iv).4

undertake various projects and job profiles on database applications.

CO-PO Mapping Matrix for the Course Code : MT-CSE-20-13 (iv)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-13 (iv).1

3

3

1

3

1

3

2

3

3

1

2

MT-CSE-20-13 (iv).2

3

2

1

3

1

3

2

3

3

1

2

MT-CSE-20-13 (iv).3

3

3

1

3

3

3

2

3

3

1

3

MT-CSE-20-13 (iv).4

3

2

1

3

3

3

2

3

3

1

3

Average

3

2.5

1

3

2

3

2

3

3

1

2.5

CO-PSO Mapping Matrix for the Course Code : MT-CSE-20-13 (iv)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-13 (iv).1

2

3

2

3

MT-CSE-20-13 (iv).2

2

3

2

3

MT-CSE-20-13 (iv).3

2

3

3

3

MT-CSE-20-13 (iv).4

2

3

3

3

Average

2

3

2.5

3

Unit – I

Database System Concepts and Architecture: Three – Schema Architecture and Data Independence, Entity Relationship Model: Entity Types, Entity Sets, Attributes & keys, Relationships Types & Instances, ER Diagrams, Naming conventions and Design Issues. Relational Model Constraints, Enhanced Entity Relationship Model: Subclasses, Super classes, Inheritance, Specialization and Generalization, Constraints and characteristics of specialization and Generalization.

Unit – II 

SQL: Data Definition and Data Types, DDL, DML, and DCL, Views & Queries in SQL, Specifying Constraints & Indexes in SQL. PL/SQL: Architecture of PL/SQL, Basic Elements of PL/SQL, PL/SQL Transactions, Cursors and Triggers.

Relational Database Design: Functional Dependencies, Decomposition, Normal Forms Based on Primary Keys- (1NF, 2NF, 3NF, BCNF), Multi-valued Dependencies, 4 NF, Join dependencies, 5 NF, Domain Key Normal Form.

Unit – III

Query Processing and Optimization: Query Processing, Query Decomposition, Stages of Query Processing, Query Tree, Using Heuristics in Query Optimization, Semantic Query Optimization, Transaction Processing: Introduction to Transaction Processing, Transaction and System Concepts, Desirable Properties of Transactions, Concurrency Control Techniques: Two-Phase Locking Techniques, Timestamp Ordering, Serializability. Database Backup and Recovery: Recovery facilities, Recovery Techniques.

Unit – IV 

Object Model: Overview of Object-Oriented concepts, Object identity, Object structure, Type constructors, Databases for Advance Applications: Architecture for Parallel Database, I/O Parallelism, Interquery Parallelism, Intraquery Parallelism, Active Database Concept, Temporal Databases Concepts, Spatial and Multimedia Databases, XML Schema, XQuery and Approaches for XML query processing.

Text Books:

  1. Elmasri & Navathe, Fundamentals of Database systems, Pearson Education.

  2. Ivan Bayross, SQL, PL/SQL- The Program Language of ORACLE, BPB Publication.

  3. Alexis Leon & Mathews Leon, Database Management System, Leon Vikas Publication.

Reference Books:

  1. Korth & Silberschatz, Database System Concept, McGraw Hill International Edition.

  2. Raghu Ramakrishnan & Johannes Gehrke, Database Management Systems, McGraw Hill.

  3. Peter Rob, Carlos Colonel, Database system Design, Implementation, and Measurement, Cengage Learning.

  4. Abbey, Abramson & Corey: Oracle 8i-A Beginner’s Guide, Tata McGraw Hill.




MT-CSE-20-14 (i) : Intelligent Systems

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: To introduce to the field of Artificial Intelligence (AI) with emphasis on its use to solve real world problems for which solutions are difficult to express using the traditional algorithmic approach and to explore the essential theory behind methodologies for developing systems that demonstrate intelligent behaviour including dealing with uncertainty, learning from experience and following problem solving strategies found in nature.

Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-14 (i).1

understand the concepts of neural networks and fuzzy logic;

MT-CSE-20-14 (i).2

learn to use the concepts of artificial intelligence in state space serach; 

MT-CSE-20-14 (i).3

acquire technical knowhow about the knowledge representation;

MT-CSE-20-14 (i).4

Understand and use the concepts of reasoning in artificial intelligence.

CO-PO Mapping Matrix for the Course Code : MT-CSE-20-14 (i)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-14 (i).1

3

3

1

3

1

3

2

3

3

1

2

MT-CSE-20-14 (i).2

3

2

1

3

1

3

2

3

3

1

2

MT-CSE-20-14 (i).3

3

3

1

3

3

3

2

3

3

1

3

MT-CSE-20-14 (i).4

3

2

1

3

3

3

2

3

3

1

3

Average

3

2.5

1

3

2

3

2

3

3

1

2.5

CO-PSO Mapping Matrix for the Course Code : MT-CSE-20-14 (i)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-14 (i).1

2

3

2

3

MT-CSE-20-14 (i).2

2

3

2

3

MT-CSE-20-14 (i).3

2

3

3

3

MT-CSE-20-14 (i).4

2

3

3

3

Average

2

3

2.5

3


Unit – I

Biological foundations to intelligent systems: Artificial neural networks, Back-Propagation networks, Radial basis function networks, and recurrent networks.

Fuzzy logic, knowledge Representation and inference mechanism, genetic algorithm, and fuzzy neural networks.

Unit – II 

Search Methods Basic concepts of graph and tree search. Three simple search methods: breadth-first search, depth-first search, iterative deepening search. Heuristic search methods: best-first search, admissible evaluation functions, hill-climbing search. Optimization and search such as stochastic annealing and genetic algorithm.

Unit – III

Knowledge representation and logical inference Issues in knowledge representation. Structured representation, such as frames, and scripts, semantic networks and conceptual graphs.  Formal  logic  and  logical  inference. Knowledge-based systems structures, its basic components. Ideas of Blackboard architectures.

Unit – IV 

Reasoning under uncertainty and Learning Techniques on uncertainty reasoning such as Bayesian reasoning, Certainty factors and Dempster-Shafer Theory of Evidential reasoning, A study of different learning and evolutionary algorithms, such as statistical learning and induction learning. Recent trends in Fuzzy logic, Knowledge Representation

Text Books:

  1. Luger G.F. and Stubblefield W.A., Artificial Intelligence: Structures and strategies for Complex Problem Solving. Addison Wesley.

Reference Books:

  1. Russell S. and Norvig P., Artificial Intelligence: A Modern Approach. Prentice-Hall.

 

 

MT-CSE-20-14 (ii) : Distributed Systems

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester exam: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: To introduce the fundamental concepts and issues of managing large volume of shared data in a distributed environment.


Course Outcomes (COs)

At the end of this course, the student will be able to: 

MT-CSE-20-14 (ii).1

learn fundamental concept and architecture of distributed databases;

MT-CSE-20-14 (ii).2

learn different strategies of distributed database design and integration strategies;

MT-CSE-20-14 (ii).3

implement distributed query processing and optimization in distributed environment;

MT-CSE-20-14 (ii).4

understand concurrency control schemes and database reliability.


CO-PO Mapping Matrix for Course Code: MT-CSE-20-14 (ii)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-14 (ii).1

3

3

3

3

2

2

3

2

2

2

1

MT-CSE-20-14 (ii).2

3

2

3

1

3

1

2

3

3

1

3

MT-CSE-20-14 (ii).3

3

3

1

3

1

2

2

2

2

3

1

MT-CSE-20-14 (ii).4

3

3

3

2

3

3

1

2

3

2

3

Average

3

2.75

2.5

2.25

2.25

2

2

2.25

2.5

2

2

CO-PSO Mapping Matrix for Course Code: MT-CSE-20-14 (ii)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-14 (ii).1

3

3

3

3

MT-CSE-20-14 (ii).2

2

2

1

2

MT-CSE-20-14 (ii).3

3

2

2

1

MT-CSE-20-14 (ii).4

3

3

1

2

Average

2.75

2.5

1.75

2

UNIT – I

Introduction to Distributed Data Processing and Distributed Database System; Features of Distributed Databases, An Example of Distributed DBMS, Types of DDBS, Promises and Complications in a Distributed DBMS; Functions and Objectives of Distributed DBMS, Distributed DBMS Architecture: Client/Server System, Peer-to-Peer Distributed System, Multi Database System (MDBS).

UNIT – II

Distributed Database Design: Top-down Design Process, Designing Process and Issues, Data Fragmentation: Benefits, Correctness Rules and Types of Fragmentation, Allocation: Measures of Cost and Benefits for Fragment Allocation, Database Integration: Schema Matching, Schema Integration, Schema Mapping. Data and Access Control: View Management, Data Security, Semantic Integrity Control.

UNIT – III

Distributed Query Processing: Concept and Objectives of Query Processing; Phases/ Layers of Query Processing: Query Decomposition, Query Fragmentation; Global Query Optimization; Local Query Optimization, Join Strategies in Fragmented Relations, Global Query Optimization Algorithms. Distributed Database Security and Catalog Management.

UNIT –IV

Concurrency Control In Centralized Database Systems; Concurrency Control In DDBMS; Distributed Concurrency Control Algorithms; Deadlock Management, Reliability Issues In DDBMS; Types of Failures; Reliability Techniques; Commit Protocols; Recovery Protocols.

Text Books:

  1. M.T. Ozsu and P. Valduriez , Principles of Distributed Database Systems, Prentice-Hall.

  2. D. Bell and J. Grimson, Distributed Database Systems, Addison-Wesley.

  3. Chhanda Ray, Distributed Database Systems, Pearson.

Reference Books:

  1. Stefano Ceri, Giuseppe Pelagatti, Distributed Databases Principles and Systems, McGraw Hill Education.

  2. Sunita Mahajan, Seema Shah, Distributed Computing, Oxford Higher Education.

 

MT-CSE-20-14(iii): Computer Vision and Augmented Reality

Type: Elective

Course Credits: 04

Contact Hours: 4 hours/week

Examination Duration: 3 Hours

Mode: Lecture

External Maximum Marks: 75

External Pass Marks: 30 (i.e. 40%)

Internal Maximum Marks: 25

Total Maximum Marks: 100

Total Pass Marks: 40 (i.e. 40%)

Instructions to paper setter for End semester examination: 

Total number of questions shall be nine.  Question number one will be compulsory and will be consisting of short/objective type questions from complete syllabus. In addition to compulsory first question there shall be four units in the question paper each consisting of two questions. Student will attempt one question from each unit in addition to compulsory question. All questions will carry equal marks.

Course Objectives: Provide an overview of theory and practicable methods in Computer Vision and Augmented Reality, with applications. The aspiration is to develop ability to create image-based models related to real-world applications and blend real world imagery with computational visionary techniques.


Course Outcomes: At the end of this course, the student will be able to:

MT-CSE-20-14(iii).1

understand and use basic concepts in computer vision related to geometric image formation, image processing, and feature detection and matching;

MT-CSE-20-14(iii).2

gain exposure and perform image segmentation for changing the representation of images, feature based alignment for estimating the motion between two or more sets of points, and motion understanding and recognition of images;

MT-CSE-20-14(iii).3

get familiar with the concepts of Augmented Reality along with its related hardware and software and will learn how to create visual and audio content;

MT-CSE-20-14(iii).4

acquire skills necessary to actualize applications correlated to  Augmented Reality.

 


CO-PO Mapping Matrix for Course Code: MT-CSE-20-14(iii)

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

MT-CSE-20-14(iii).1

3

2

3

2

1

2

2

2

3

2

MT-CSE-20-14(iii).2

3

2

3

2

1

3

2

2

3

3

MT-CSE-20-14(iii).3

3

2

3

1

1

2

2

2

3

2

MT-CSE-20-14(iii).4

3

3

3

3

1

3

3

2

3

3

Average

3

2.25

3

2

1

2.5

2.25

2

3

2.5


CO-PSO Mapping Matrix for Course Code: MT-CSE-20-14(iii)

COs

PSO1

PSO2

PSO3

PSO4

MT-CSE-20-14(iii).1

3

3

1