M.Tech. Programme Mechanical Engineering Artificial...
Transcript of M.Tech. Programme Mechanical Engineering Artificial...
1
M.Tech. Programme
Mechanical Engineering – Artificial Intelligence
Curriculum and Scheme of Examinations (2020 Admission)
SEMESTER 1 Credits 23
Exam
Slot
Course No.
Course Name
L-T-P
Internal
Marks
End Semester
Exam Credits
Marks Duration
(hrs)
A 02ME6411 Artificial Intelligence –
Principles and Techniques
3-0-0 40 60 3 3
B 02ME6421 Data Structures and
Algorithms
3-1-0 40 60 3 4
C 02ME6431 Machine Learning 3-1-0 40 60 3 4
D 02ME6441 Mathematics for Machine
Learning
3-1-0 40 60 3 4
E 02ME6451 Elective 1 3-0-0 40 60 3 3
02CA6001 Research Methodology 1-1-0 100 0 0 2
02ME6461 Seminar 1 0-0-2 100 0 0 2
02ME6471 Programming Lab 0-0-2 100 0 0 1
List of Electives:
Elective I
02ME6451.1 Game Theory 02ME6451.2 Robotics & Automation 02ME6451.3 Adaptive Signal Processing 02ME6451.4 Biometric Technologies 02ME6451.5 Digital Image Processing 02ME6451.6 Pattern Recognition
02ME6451.7 Human Computer Interface
2
SEMESTER 2 Credits 18
Exam
Slot
Course No.
Course Name
L-T-P
Internal
Marks
End Semester
Exam
Credits Marks Duration
(hrs)
A 02ME6412 Big-Data Analytics 3-0-0 40 60 3 3
B 02ME6422 Topics in Optimisation 3-0-0 40 60 3 3
C 02ME6432 Deep Learning: Theory and
Practice
3-0-0 40 60 3 3
D 02ME6442 Elective 2 3-0-0 40 60 3 3
E
02ME6452
Elective 3
Industry run course or
MOOC course in the
domain of specialisation
(Subjected to the approval
by KTU)
3-0-0
40
60
3
3
02ME6462 Mini Project 0-0-4 100 0 0 2
02ME6472
Advanced Computing Lab (This lab can focus on
AI/ML frameworks)
0-0-2
100
0
0
1
List of Electives:
Elective 2
02ME6442.1 Computer Vision: Foundations and Applications
02ME6442.2 Modelling and Simulation
02ME6442.3 Internet of Things
02ME6442.4 Bioinformatics
02ME6442.5 Soft Computing
3
Semester 3 Credits 14
Exam
Slot
Course No.
Course Name
L-T-P
Internal
Marks
End Semester
Exam
Credits Marks Duration
(hrs)
A
02ME7411
Elective 4
Industry run course or
MOOC course in the
domain of specialisation
(Subjected to the approval
by KTU)
3-0-0
40
60
3
3
B 02ME7421 Elective 5 3-0-0 40 60 3 3
02ME7431 Seminar 0-0-2 100 0 0 2
02ME7441 Project (Phase 1) 0-0-8 50 0 0 6
List of Electives
Elective 4
02ME7411.1 Reinforcement Learning
02ME7411.2 Online Prediction and Learning
02ME7411.3 Medical image processing and Analysis
02ME7411.4 Time series Analysis
Elective 5
02ME7421.1 Scalable Systems for Data Science
02ME7421.2 Machine learning for big data
02ME7421.3 Cloud and big data analytics
02ME7421.4 Approximation Algorithms
02ME7421.5 Parallel and distributed data management
02ME7421.6 Social Network Analytics
Semester 4 Credits 12
Exam
Slot Course No. Course Name L-T-P
Internal
Marks
End Semester Exam
Credits Marks
Duration
(hrs)
02ME7412 Project (Phase 2) 0-0-21 100 0 0 12
Total Credits: 68
4
Credits Distribution
Semester Distribution of Credits Credits
Semester – 1
Core – 17
Elective – 3
Seminar – 2
Lab – 1
23
Semester – 2
Core – 9
Elective – 6
Mini Project – 2
Lab – 1
18
Semester – 3
Elective – 6
Seminar – 2
Project – 6
14
Semester – 4 Project – 12 12
Total
Core – 26
Elective – 15
Mini Project – 2
Lab – 2
Seminar – 4
Project – 18
67
5
Course No: 02ME6411
Course Title: ARTIFICIAL INTELLIGENCE – PRINCIPLES AND TECHNIQUES
Credits: 3-0-0: 3
Course Objectives
• To provide necessary basic concepts in Artificial Intelligence.
• Identify the problems where AI is required
• Compare and contrast different AI techniques available
• Understand learning algorithms
• Apply the basic concepts to various elementary and some advanced applications.
Syllabus
Introduction to artificial intelligence, Problems, Problem Spaces and search, Heuristic search
technique, Advanced search, Constraint satisfaction problems, Knowledge representation and
reasoning, Non-standard logics, Uncertain and probabilistic reasoning, semantic networks and
description logics, Rules systems: use and efficient implementation, Planning systems, Natural
Language Processing, Learning, Expert Systems
Course Outcomes
• Identify and choose the appropriate representation for an AI problem or domain model
• Apply the most appropriate algorithm for search and reasoning within an AI problem
domain
• Have a good knowledge of various learning techniques to solve AI problems
• Design, analyse and demonstrate AI applications or systems that apply to real life
problems.
References
1.Artificial Intelligence – A Modern Approach Stuart Russell, Peter Norvig Pearson
Education Third Edition,2015
2.“Artificial Intelligence”, Elaine Rich and Kevin Knight McGraw-Hil Third
Edition l, 2010
3.“Artificial Intelligence: Structures and Strategies for complex problem Solving”, G.
Luger Pearson Education Fourth Edition-2002
4.“Introduction to Artificial Intelligence” E Charniak and D McDermott Pearson
Education, 2008
5.“Artificial Intelligence and Expert Systems” Dan W. Patterson, Prentice Hall of
India 2010
6."Artificial Intelligence and Expert Systems Development" D W Rolston-. Mc Graw
hill 2010
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Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6411 ARTIFICIAL INTELLIGENCE –
PRINCIPLES AND TECHNIQUES 3-0-0 3 2020
Pre- requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Introduction to Artificial Intelligence (AI): What is AI, Foundations and
History of AI, Applications of AI
Intelligent Agents: Agents and Environments, the concept of Rationality,
Nature of environments, structure of agents
7 15
MODULE: 2
Problem Solving by Classical Searching: Problem solving agents,
uninformed search strategies, informed search strategies, heuristics in search
Beyond Classical Search: Local search algorithms and Optimization
problems, searching with non-deterministic actions, searching with partial
observations, adversarial search with alpha beta pruning, constraint
satisfaction problem
6 15
FIRST INTERNAL TEST
MODULE: 3
Knowledge Based Logical Agents: The Wumpus World, propositional logic,
propositional theorem proving, agents based on propositional logic
First Order Logic: Syntax and Semantics of First Order Logic, Using First
Order Logic, Knowledge Engineering in First Order Logic, Unification and
Lifting, Forward and Backward chaining, Resolution
6 15
MODULE: 4
Planning: Classical planning, Components of a planning system, algorithms
for planning as state-space search, Hierarchical planning, Multiagent
planning, Reactive Systems
Reasoning: Acting under uncertainty, representing knowledge in uncertain
domains, Semantics of Bayesian networks, Rule based methods for uncertain
reasoning, Time and uncertainty, Inference in temporal models, Weak Slot-
and-Filler Structures, Strong Slot-and-Filler Structures
7 15
SECOND INTERNAL TEST
MODULE: 5
Learning: What is learning? Rote learning, Learning by taking advice,
Learning by problem-solving, Learning by examples, Explanation based
learning, Discovery, Analogy, Formal learning theory, Neural Net learning,
Genetic learning, Connectionist AI and Symbolic AI
Expert Systems: Representing and using domain knowledge, Expert System
Shells, Explanation, Knowledge Acquisition.
7 20
MODULE: 6
Natural Language Processing: Introduction, Syntactic Processing,
Semantic Analysis, Discourse and Pragmatic Processing, Statistical NLP,
Spell Checking.
6 20
7
Course No: 02ME6421
Course Title: DATA STRUCTURES AND ALGORITHM
Credits: 3-1-0: 4
Course Objectives
• To be familiar with basic techniques of algorithm analysis
• To be familiar with writing recursive methods
• Master the implementation of linked data structures such as linked lists, binary trees
other tree representation.
• Be familiar with advanced data structures such as balanced search trees, hash tables,
priority queues and the disjoint set union/find data structure
• To be familiar with sorting algorithms such as quicksort, mergesort and heapsort
• To be familiar with graph algorithms such as shortest path and minimum spanning
tree
• To be familiar with algorithm design techniques.
Syllabus
Data organization and manipulation using data structures such as stacks, queues, linked lists,
binary trees, heaps, graphs, sets and algorithm design techniques.
Course Outcomes: After the completion of the course the student will be able to
CO1. Design algorithms for a task and calculate the time and space complexity of the
algorithm.
CO2. Assess the impact of choice of data structures and algorithm design methods in
the performance of programs.
CO3. Represent data using trees, graphs, heaps and manipulate them.
CO4. Select sorting algorithms appropriate to specific circumstances.
CO5. Arrange data using appropriate Hash Functions
CO6. Select data structure and algorithm design method for a given application.
References
1. S. Sahni, “Data structures, Algorithms and Applications in Java”, Universities Press.
[ISBN:0-07-109217-x]
2. Adam Drozdek, “Data structures and Algorithms in Java”, 3rd edition, Cengage
Learning. [ISBN:978-9814239233].
3. Ellis Horowitz, Sartaj Sahni, Susan Anderson Freed, Fundamentals of Data Structures
in C, Second Edition, University Press, 2008
4. Thomas Cormen, Charles E. Leiserson, Ronald Rivest, Introduction to algorithm,3rd
edition, PHI Learning.
5. Mark Allem Weiss, Data Structures and Algorithm Analysis in C, 2nd Edition, Pearson
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Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6421 DATA STRUCTURES AND
ALGORITHM 3-1-0 4 2020
Pre- requisites: Strong foundation in any one programming language
MODULES
Conta
ct
hours
Sem Exam
Marks %
MODULE: 1
Algorithms, Performance analysis- time complexity and space complexity,
Asymptotic Notation-Big Oh, Omega and Theta notations, Complexity
Analysis Examples. Data structures-Linear and nonlinear data structures, ADT
concept, Linear List ADT, Array representation, Linked representation, Vector
representation, singly and doubly linked, circular lists. Representation of arrays,
Sparse matrices and their representation.
10 15
MODULE: 2
Stack and Queue ADTs, array and linked list representations, infix to postfix
conversion using stack, implementation of recursion, Circular queue-insertion
and deletion, Dequeue ADT, array and linked list representations, Priority
queue ADT, implementation using Heaps, Insertion into a Max Heap, Deletion
from a Max Heap, Binomial Heaps (Definition and examples only), Symmetric
Min-Max Heaps (Definition and examples only), Interval Heaps (Definition and
examples only).
10 15
FIRST INTERNAL TEST
MODULE: 3
Trees- Ordinary and Binary trees terminology, Properties of Binary trees,
Binary tree ADT, representations, tree traversals.
Graphs- Graphs terminology, Graph ADT, representations, Data Structures for
Disjoint Sets, Disjoint Set operations and representation of disjoint sets.
9 15
MODULE: 4
Search trees- Binary search tree-Binary search tree ADT, insertion, deletion
and searching operations, Balanced search trees, AVL trees-Definition and
examples only, Red Black trees – Definition and examples only, B-Trees-
definition, insertion and searching operations( illustration with examples only)
Tries(Definition and examples only)- Binary Tries, k-d Trees, Point Quad trees.
8 15
SECOND INTERNAL TEST
MODULE: 5
Searching - Linear and binary search methods, Hashing-Hash functions,
Collision Resolution methods- Open Addressing, Chaining.
Sorting Methods- Quick sort, Merge sort, Heap sort, Radix sort, comparison of
sorting methods.
7 20
MODULE: 6
Algorithm design techniques- Divide and conquer strategy, dynamic
programming, backtracking and branch &bound.
Graph traversals/search methods-DFS and BFS, Applications of Graphs-
Minimum cost spanning tree using Kruskal’s algorithm, Dijkstra’s algorithm
for Single Source Shortest Path Problem
8 20
9
Course No: 02ME6431
Course Title: MACHINE LEARNING
Credits: 3-1-0: 4
Course Objectives
• To provide necessary basic understanding in machine learning
• To learn about the various concepts and models available in machine learning
• Choose and apply machine learning models based on application
Syllabus
Introduction to Machine Learning, Linear and Logistic Regression, Probability and
classification, Neural Networks, SVM, Dimensionality Reduction, Ensemble methods,
Unsupervised Learning, Reinforcement Learning.
Course Outcomes
• Understand the concept, purpose, scope, steps, applications, and effects of ML.
• Identify the concepts of supervised and unsupervised and reinforcement learning, and
forecasting models.
• Illustrate the working of classifier models like SVM, Neural Networks and
probabilistic classification methods and identify classifier model for typical machine
learning applications
• Use dimensionality reduction, cross validation and performance metrics of
classification.
• Acquire skills in unsupervised and reinforcement learning models and identify its
applicability in real life problems
References
• Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• Ethem Alpaydın, Introduction to Machine Learning (Adaptive Computation and
Machine Learning), MIT Press, 2004.
• Richard O Duda, Peter E Hart, David G Stork, Pattern Classification, Second Edition.
Wiley.
• Stephen Marsland, Machine Learning: An Algorithmic Perspective, Second Edition.
CRC press.
10
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6431 MACHINE LEARNING 3-1-0 4 2020
Pre-requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Introduction to Machine Learning (ML), Types of Machine Learning-
Supervised, Unsupervised, Reinforcement. Types of ML problems:
Association, Classification and Regression. General Steps or Process of
Machine Learning, Objective (Minimize Error or Cost Function), Cost
functions: Definition and Types
Linear regression: with single and multiple variables (features), Least
Squares Gradient descent, Bias-Variance trade off, Normal equations.
6 15
MODULE: 2
Logistic Regression: Hypothesis representation, Decision boundary,
Sigmoid Function and its differentiation, Gradient Descent, Regularization
of Logistic Regression.
Classification: Cross validation and re-sampling methods, Classifier
performance measures- Precision, recall, ROC curves.
Neural Networks – Concept of perceptron and Artificial neuron, Feed
Forward Neural Network, Introduction to back propagation, Weight
initialization.
7 15
FIRST INTERNAL TEST
MODULE:3
Probability and classification: Naive Bayes and Gaussian class-conditional
distribution, Bayes' Rule and Naive Bayes Model, Maximum Likelihood
estimation. Discrete Markov Processes, Hidden Markov models.
7 15
MODULE:4
Kernel Machines- Support Vector Machine- Optimal Separating hyper
plane, Soft-margin hyperplane, Kernel trick, Kernel functions.
Dimensionality Reduction: PCA, LDA, MDA
6 15
SECOND INTERNAL TEST
MODULE: 5
Ensemble methods: Boosting, Bagging, and Stacking
Unsupervised Learning - Clustering Methods - K-means, Expectation-
Maximization Algorithm, Hierarchical Clustering methods, Density based
clustering.
7 20
MODULE: 6
Reinforcement Learning- Markov Decision, Monte Carlo Prediction.
Forecasting models: Trend analysis, Cyclical and Seasonal analysis,
Smoothing, Moving averages, Auto-correlation, ARIMA.
8 20
11
Course No: 02ME6441
Course Title: MATHEMATICS FOR MACHINE LEARNING
Credits: 3-1-0: 4
Course Objectives
• To provide necessary basic concepts in computational techniques and algebraic skills.
• To study about fundamental probability concepts and statistics.
• To study about random process.
Syllabus
Linear Algebra, study of system of linear equation , matrix algebra, vector spaces, eigen
values and eigen vectors, orthogonality and diagonalization ,Probability and Statistics,
Probability Distribution Function, Random Variables, Function of random variables, Random
Process
Course Outcomes
• Have a good knowledge of computational techniques and algebraic skills.
• Have a fundamental knowledge of the basic probability concepts
• Have a good knowledge of standard distributions which can describe real life
phenomena.
• Acquire skills in handling situations involving several random variable and functions
of random variables
• Understand and characterize phenomena which evolve with respect to time in
probabilistic manner
References
• Mathematics for Machine learning by Marc Peter Deisenroth , A Aldo Faisal and
Cheng Soon Ong published by Cambridge University Press
• Gilbert Strang, Linear algebra and learning from data. Wellesley, Cambridge press ,
2019
• Probability and random process ORF 309/ MAT380 lecture notes Prinston University
Version Feb 22, 2016
• Probabilistic Graph models Principles and models ,Daphne Koller and Nir Friedman
• Ross, Sheldon M, Introduction to Probability Models.
• Ross, Sheldon M, Introduction to Probability and statistics for engineers and scientists,
Elsevier 2008
• Pradeep Kumar Ghosh, Theory of Probability and Stochastic process, University press
2010
• A Papoulis and S.O Pillai, Probability Random variable and Stochastic process. Mc
Graw Hill 2002
• V Krishnan , Probability and Random processes, Wiley and Sons 2006
12
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6441 MATHEMATICS FOR MACHINE
LEARNING 3-1-0 4 2020
Pre-requisites: Nil
MODULES
Contact
hours
Sem
Exam
Marks %
MODULE: 1
LINEAR ALGEBRA: Systems of Linear Equations – Matrices, Solving
Systems of Linear Equations. Vector Spaces - Linear Independence, Basis
and Rank, row space null space and column space - Linear Mappings,
Norms, - Inner Products - Lengths and Distances - Angles and Orthogonality
– Orthonormal Basis - Orthogonal Complement - Orthogonal Projections.
Matrix Decompositions.
7 15
MODULE: 2
Determinant and Trace, Eigenvalues and Eigenvectors, Cholesky
Decomposition, Eigen decomposition and Diagonalization, Singular Value
Decomposition, Matrix Approximation.
7 15
FIRST INTERNAL TEST
MODULE: 3
Introduction: Sets, Fields and Events, Definition of probability, Joint,
Conditional and Total Probability, Bayes’ Theorem and applications.
Random Variable:- Definition, Probability Distribution Function,
Probability Density function, Common density Functions-- Binomial
random variable, Uniform Distribution, Normal Distribution, Poisson,
Exponential, Rayleigh, Chi-square, Weibull Distribution, Lognormal,
Gamma and Beta Distribution
9 15
MODULE: 4
Conditional and Joint Distributions and densities, independence of random
variables. Functions of Random Variables: One function of one random
variable, one function of two random variables, two functions of two random
variables. Markov’s inequality, Chebyshev’s inequality, Independent
/uncorrelated random variables, Sum of random variables
9 15
SECOND INTERNAL TEST
MODULE: 5
Expectation: Fundamental Theorem of expectation, Moments, Joint
moments, Moment Generating functions, Characteristic functions,
Conditional Expectations, Correlation and Covariance, Jointly Gaussian
Random Variables.
10 20
MODULE: 6
Random Processes: -Basic Definitions, Poisson Process, Wiener Process,
Markov Process, Birth- Death Markov Chains, Chapman- Kolmogorov
Equations, Stationarity, Wide sense Stationarity, WSS Processes and LSI
Systems, Power spectral density, White Noise, Periodic and cyclo-stationary
processes. Chebyshev and Schwarz Inequalities, Chernoff Bound, Central
Limit Theorem. Laws of large numbers.
10 20
13
Elective -1
Course No: 02ME6451.1
Course Title: GAME THEORY
Credits: 3-0-0:
Course Objectives
• To provide necessary concept about game theory formulation
• Solution concepts in game theory
• Apply the basic concepts in game theory to various elementary and some advanced
applications.
Syllabus
Strategic and Extensive Form games – Zero sum and Non-zero sum games – Pure Strategy
and Mixed Strategy Nash Equilibrium : Existence and Computing – Mechanism Design.
Course Outcomes
Student will be able to
• acquire a fundamental knowledge of the basic game theory concepts
• apply game theory framework to various situations
• compute Nash Equilibria of a problem formulated as a game.
• formulate a problem using cooperative games framework.
References
• Y Narahari – Game Theory and Mechanism Design – IISc Press and World Scientific
• Anna R. Karlin and Yuval Peres – Game Theory, Alive (Available Online
https://homes.cs.washington.edu/~karlin/GameTheoryBook.pdf )
• Osborne, M.J. An Introduction to Game Theory, Oxford University Press, 2004
• Tamer Basar – Dynamic Non-cooperative Game Theory
• Gibbons, R. A Primer in Game Theory, Pearson Education, 1992
14
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.1 GAME THEORY 3-0-0 3 2020
Pre-requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Notions in Game Theory: Definition of a Game - Strategic Interactions –
Strategic Form Games – Preferences – Utilities – Rationality – Intelligence
– Classification of Games
Strategic Form and Extensive Form Games: Strategic Form Games:
Definition and Examples – Extensive Form Games: Definition and
Examples.
6 15
MODULE: 2
Zero Sum Games and Non-zero Sum Games: Definition and Examples of
Zero sum and Non-zero sum games in Strategic and Extensive Form
Dominant Strategy Equilibria: Strong Dominance and Weak Dominance
Equilibria : Definition and Examples.
6 15
FIRST INTERNAL TEST
MODULE: 3
Pure Strategy Nash Equilibrium: Definition and Illustrative Examples of
Pure Strategy Nash Equilibria in Zero Sum and Non-zero Sum Games – Best
Responses and Reaction Curves – Nash Equilibrium as a Fixed Point -
Saddle Point and Pure Strategy Nash Equilibria - Existence of Pure Strategy
Nash Equilibria – Interpretations of Nash Equilibria.
6 15
MODULE: 4
Mixed Strategy Nash Equilibrium: Mixed Strategies – Mixed Strategy Nash
Equilibrium in Zero sum and Non-zero sum games - Maxmin and Minmax
Values in Mixed Strategies - Existence of Mixed Strategy Nash Equilibrium
– Graphical Approach to compute Mixed Strategy Nash Equilibrium.
7
15
SECOND INTERNAL TEST
MODULE: 5
Computation of Nash Equilibrium: Example for Computing Nash Equilibria:
Pure and Mixed Strategy Nash Equilibria – General Algorithm For Finding
Nash Equilibria of Finite Strategic Games – Complexity of Computing Nash
Equilibria – Introduction of software tools for computing Nash Equilibria.
7 20
MODULE: 6
Cooperative Games: Nash Bargaining solution - Transferable Utility Games
– The Core – The Shapley Value.
Designing games and mechanisms: (Brief introduction) Fair Division –
Social Choice and Voting –– Auctions – Adaptive Decision Making.
7 20
15
Course No: 02ME6451.2
Course Title: ROBOTICS AND AUTOMATION
Credits: 3-0-0: 3
Course Objectives
• To introduce the basic concepts, types and parts of robots.
• To provide the knowledge in robot sensors and their applications in end effectors and
vision systems.
• To introduce robot kinematics, programming and the applications in Artificial
Intelligence.
• To outline various applications of robots in automated factory and other systems.
Syllabus
Automation and Robotics, Robot anatomy, robot configuration, Control System and
Components, Motion Analysis And Control, End Effectors , selection and design, Sensors,
Machine Vision, Robot Programming, Robot Languages, Robot Application, Recent Trends In
Robotics.
Course Outcomes
The students will be able to:
• Identify sensors used for various robotics application and the associated artificial
intelligence Classify robot used for automated factory
• Develop mathematical model for robot kinematics and path planning
• Analyse the principle behind robotic drive system, end effectors, sensor and vision
systems.
References
• K S Fu, Ralph Gonzalez and C S G Lee, “Robotics, Control, Sensing, Vision and
Intelligence”, Tata McGraw-Hill Education, 2008.
• Saeed B. Niku, “Introduction to Robotics: Analysis, Control, Applications”, John
Wiley & Sons, 2011.
• M.P. Groover, “Industrial Robotics – Technology, Programming and Applications”,
McGraw-Hill, 2001.
• Tadej Bajd, Matjaž Mihelj and Marko Munih, “Introduction to Robotics”, Springer,
2013.
• Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani and Giuseppe Oriolo, “Robotics-
Modelling, Planning and Control”, Springer-Verlag, 2010.
16
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.2 ROBOTICS AND AUTOMATION 3-0-0 3 2020
Pre-requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Introduction: Automation and Robotics, Robot anatomy, robot
configuration, motions joint notation work volume, robot drive systems-
Salient Features, Applications and Comparison of all Drives, control
system and dynamic performance, precision of movement.
7 15
MODULE: 2
Robot Sensors: Principles of Sensing, Sensors of Movement, Contact
Sensors, Tactile, Proximity and Ranging Sensors.
End Effectors: Grippers: Types, operation, mechanism, force analysis,
tools as end effectors consideration in gripper selection and design.
7 15
FIRST INTERNAL TEST
MODULE: 3
Kinematics of robot: Direct and inverse kinematics problems and
workspace, inverse kinematics solution for the general 6 DoF manipulator,
redundant and over-constrained manipulators. D-H Parameters.
7 15
MODULE: 4
Planning and control: Trajectory planning, position control, force control,
hybrid control.
Automates storage and retrieval system and Autonomous guided vehicles
in factory environment- Types, interfacing and various systems.
Robot Languages: Textual robot languages, Generation, Robot language
structures, Elements in function.
7 15
SECOND INTERNAL TEST
MODULE: 5
Machine Vision: Functions, image processing and analysis, training the
vision system, robotic applications.Industrial and medical robotics:
Application in manufacturing processes such as casting, welding, painting,
machining, heat treatment and nuclear power stations, etc; medical robots:
image guided surgical robots, radiotherapy, cancer treatment, etc.
7 20
MODULE: 6
Collaborative Robots: Collaborative Industrial Robot System,
Collaborative Robot, Collaborative Operation. Collaborative Robot
Grippers, Applications.
Mobile Robots: Mobile Robot Kinematics, Navigation.
Humanoid Robotics: Biped Locomotion, Imitation Learning.
7 20
17
Course No: 02ME6451.3
Course Title: ADAPTIVE SIGNAL PROCESSING
Credits: 3-0-0: 3
Course Objectives
The course is designed to provide students a strong background in the concept of signal processing
and apply it to the signals which can process adaptively.
Syllabus
Adaptive systems - definitions and characteristics - applications - properties- Correlation
matrix and its properties- z transform- Searching performance surface- gradient estimation -
performance penalty – LMS algorithm- sequential regression algorithm - adaptive recursive
filters - Kalman filters- Applications adaptive modelling and system identification-adaptive
modelling for multipath communication channel, geophysical exploration, inverse adaptive
modelling, equalization, and deconvolution-adaptive equalization of telephone channels
Course Outcomes
The students are expected to :
• Understand basic concepts of adaptive signal processing
• Top-level understanding of the convergence issues, computational complexities and
optimality of different filters
References
1. Bernard Widrow and Samuel D. Stearns, “Adaptive Signal Processing”, Person
Education, 2005.
2. Simon Haykin, “Adaptive Filter Theory”, Pearson Education, 2003.
3. John R. Treichler, C. Richard Johnson, Michael G. Larimore, “Theory and Design of
Adaptive Filters”, Prentice-Hall of India, 2002
4. S. Thomas Alexander, “Adaptive Signal Processing - Theory and Application”,
Springer-Verlag.
5. D. G. Manolokis, V. K. Ingle and S. M. Kogar, “Statistical and Adaptive Signal
Processing”, Mc Graw Hill International Edition, 2000.
18
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.3 ADAPTIVE SIGNAL PROCESSING 3-0-0 3 2020
Pre-requisites: (1) Basic knowledge of Signal processing at UG/PG Level.
(2) Basic knowledge of different transform domains like Fourier, Laplace, Z transform etc.
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Adaptive systems - definitions and characteristics – applications -
properties-examples - adaptive linear combiner-input signal and weight
vectors, performance function, Gradient and minimum mean square error,
Alternate expressions of gradient
6 15
MODULE: 2
Theory of adaptation with stationary signals: Correlation matrix and its
properties, its physical significance. Eigen analysis of matrix, structure of
matrix and relation with its eigen values and eigen vectors. Z Transforms in
Adaptive signal processing and its applications
8 15
FIRST INTERNAL TEST
MODULE: 3
Searching performance surface - stability and rate of convergence -
learning curve-gradient search - Newton's method - method of steepest
descent - comparison - gradient estimation - performance penalty - variance
-excess MSE and time constants – misadjustments
8 15
MODULE: 4
LMS algorithm - convergence of weight vector-LMS/Newton algorithm -
properties - sequential regression algorithm - adaptive recursive filters -
random-search algorithms
8 15
SECOND INTERNAL TEST
MODULE: 5 Kalman filters - recursive minimum mean square estimation for scalar
random variables- statement of Kalman filtering problem innovation
process-estimation of the state-filtering-initial conditions-Kalman filter as
the unifying basis for RLS filters
7 20
MODULE: 6
Applications - adaptive modeling and system identification adaptive
modeling for multipath communication channel, geophysical exploration,
inverse adaptive modeling, equalization, and deconvolution-adaptive
equalization of telephone channels, Adaptive interference cancelling:
applications in Bio-signal processing
8 20
19
Course No: 02ME6451.4
Course Title: BIOMETRIC TECHNOLOGIES
Credits: 3-0-0: 3
Course Objectives
• To familiarize the fundamental understanding of basic concepts in various
biometric traits
Syllabus
Biometric fundamentals and standards- Physiological Biometrics- Behavioural biometrics-
User interfaces- Biometric applications- Assessing the Privacy Risks of Biometrics- Fusion in
biometrics
Course Outcomes
On successful completion of the course, students will be able to:
1. Analyse the basic engineering principles underlying biometric systems
2. Understand and analyse biometric systems and be able to analyse and design basic
biometric system application
3. Identify the sociological issues associated with the design and implementation of
biometric systems
References
1. Anil K Jain, Patrick Flynn and Arun A Ross, “Handbook of Biometrics”, Springer,
USA, 2010.
2. John R Vacca, “Biometric Technologies and Verification Systems”, Elsevier, USA,
2009
3. Samir Nanavati, Michael Thieme and Raj Nanavati, “Biometrics – Identity Verification
in a Networked World”, John Wiley and Sons, New Delhi, 2003.
4. Paul Reid, “Biometrics for Network Security, Pearson Education, New Delhi, 2004.
5. Reid M. Bolle et al, “Guide to Biometrics, Springer”, USA, 2004
6. David D Zhang, “Automated Biometrics: Technologies and Systems”, Kluwer
Academic Publishers, New Delhi, 2000.
7. Arun A Ross, Karthik Nandakumar and Jain A K, “Handbook of Multi-biometrics”,
Springer, New Delhi 2011.
20
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.4 BIOMETRIC TECHNOLOGIES 3-0-0 3 2020
Pre-requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Biometric fundamentals and standards: Definition, Biometrics versus
traditional techniques, Characteristics, Key biometric processes:
Verification - Identification - Biometric matching, Performance measures in
biometric systems: FAR, FRR, FTE rate, EER and ATV rate. Assessing the
privacy risks of biometrics - Designing privacy sympathetic biometric
systems, Different biometric standards, Application properties.
5 15
MODULE: 2
Physiological biometrics: Finger scan, Facial scan, Iris scan, Retina scan,
Ear scan- components, working principles, competing technologies,
strengths and weaknesses, Other Physiological Biometrics: Palm print,
Hand vascular geometry analysis, Knuckle, DNA, Dental, Cognitive
Biometrics - ECG, EEG. Automated fingerprint identification systems
7 15
FIRST INTERNAL TEST
MODULE: 3
Behavioural biometrics: Signature scan, Keystroke scan, Voice scan, Gait
recognition, Gesture recognition, Video face, Mapping the body technology: components, working principles, strengths and weaknesses
7 15
MODULE: 4
User interfaces: Biometric interfaces: Human machine interface - BHMI
structure, Human side interface: Iris image interface - Hand geometry and
fingerprint sensor, Machine side interface: Parallel port - Serial port -
Network topologies, Case study: Palm Scanner interface.
7 15
SECOND INTERNAL TEST
MODULE: 5 Biometric applications: Categorizing biometric applications, Application
areas: Criminal and citizen identification – Surveillance - PC/network access
- E-commerce and retail/ATM, Costs to deploy, Issues in deployment,
Biometrics in medicine, cancellable biometrics.
Assessing the Privacy Risks of Biometrics: Designing Privacy-
Sympathetic Biometric Systems - Need for standards - different biometric
standards.
7 20
MODULE: 6
Fusion in biometrics: Multi-biometrics, information fusion in biometrics,
Levels of fusion: Sensor level - Feature level - Rank level - Decision level
fusion - Score level fusion, Fusion incorporating ancillary information.
7 20
21
Course No: 02ME6451.5
Course Title: DIGITAL IMAGE PROCESSING
Credits: 3-0-0: 3
Course Objectives
(1) To extend the knowledge on DSP to 2-D signal processing and hence to analyze digital
Images.
(2) To study the various aspects of image processing like restoration, enhancement,
compression, etc.
Syllabus
Gray scale and colour Images, image sampling, quantization and reconstruction, Human visual
perception, transforms: DFT, FFT, WHT, Haar transform, KLT, DCT, Filters in spatial and
frequency domains, histogram-based processing, Edge detection - non parametric and model
based approaches, LOG filters, Image Restoration - PSF, circulant and block-circulant
matrices, deconvolution, restoration using inverse filtering, Wiener filtering and maximum
entropy-based methods, Binary morphology, dilation, erosion, opening and closing, gray scale
morphology, applications, thinning and shape decomposition, Image and video compression :
Lossy and lossless compression, Transform based sub-band decomposition, Entropy Encoding,
JPEG, JPEG2000, MPEG, Computer tomography - parallel beam projection, Radon transform,
Back-projection, Fourier-slice theorem, CBP and FBP methods, Fan beam projection, Image
texture analysis - co-occurrence matrix, statistical models, Hough Transform, boundary
detection, chain coding, segmentation and thresholding methods.
Expected Outcomes
The students are expected to:
(1) Attain an ability to extend the one-dimensional DSP principles to two-dimension;
(2) Have good knowledge in various image processing methodologies.
References
1. A. K. Jain, Fundamentals of digital image processing, PHI, 1989.
2. Gonzalez and Woods, Digital image processing ,3/E Prentice Hall, 2008.
3. S Jayaraman, S Esakkirajan, T Veerakumar, Digital image processing, Tata McGraw Hill,
2015.
4. R.M. Haralick, and L.G. Shapiro, Computer and Robot Vision, Addison Wesley, 1992.
5. R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, MGH International Edition, 1995.
6. W. K. Pratt, Digital image processing, Prentice Hall, 1989.
7. David Forsyth & Jean Ponce, Computer Vision: A modern approach, Pearson Edn., 2003
8. C . M. Bishop, Pattern Recognition & Machine Learning, Springer 2006
22
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.5 DIGITAL IMAGE PROCESSING 3-0-0 3 2020
Pre-requisites: Basic knowledge in DSP and Linear Algebra at UG level.
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Image representation - Gray scale and colour Images, Representation of 2D
signals, image sampling, quantization and reconstruction.
Two dimensional orthogonal transforms- Digital images, Human visual
perception, transforms: DFT, FFT, DCT, WHT, Haar transform, KLT, and
Singular Value Decomposition.
2D convolution and correlation- Graphical Method and Matrix method
8 15
MODULE: 2
Image enhancement - filters in spatial and frequency domains, histogram-
based processing, image subtraction, image averaging, Spatial filtering-
smoothing filters, sharpening filters, Frequency domain methods: low pass
filtering, high pass filtering, homomorphic filtering.
Edge detection - non parametric and model based approaches, LOG filters,
localization problem.
8 15
FIRST INTERNAL TEST
MODULE: 3
Image Restoration - PSF, circulant and block-circulant matrices,
deconvolution, restoration using inverse filtering, Wiener filtering and
maximum entropy-based methods.
Image texture analysis - co-occurrence matrix, measures of textures,
statistical models for textures. Hough Transform, boundary detection, chain
coding, segmentation and thresholding methods.
8 15
MODULE: 4
Mathematical morphology - binary morphology, dilation, erosion, opening
and closing, duality relations, gray scale, morphology, applications such as
hit-and-miss transform, thinning and shape decomposition.
8 15
SECOND INTERNAL TEST
MODULE: 5
Image and Video Compression Standards: Lossy and lossless compression
schemes: Transform Based, Sub-band Decomposition, Entropy Encoding,
JPEG, JPEG2000, MPEG
6 20
MODULE: 6
Computer tomography - parallel beam projection, Radon transform, and its
inverse, Back-projection operator, Fourier-slice theorem, CBP and FBP
methods, ART, Fan beam projection.
6 20
23
Course No: 02ME6451.6
Course Title: PATTERN RECOGNITION
Credits: 3-0-0: 3
Course Objectives:
• Study the fundamental algorithms for pattern recognition
• To instigate the various classification techniques
• To originate the various structural pattern recognition and feature extraction
techniques.
Syllabus
Basics of pattern recognition, Parametric and Non Parametric technique, Unsupervised
Methods, Linear discriminant based classifiers and tree classifiers, Regression, Graphical
methods, Recent Advances in Pattern Recognition.
Expected Outcomes:
• Understand and apply various algorithms for pattern recognition
• Realize the clustering concepts and algorithms
• Bring out structural pattern recognition and feature extraction techniques Program
References:
1. Pattern Classification, R.O.Duda, P.E.Hart and D.G.Stork, John Wiley, 2001
2. Pattern Recognition, S.Theodoridis and K.Koutroumbas, 4th Ed., Academic Press, 2009
3. Pattern Recognition and Machine Learning, C.M.Bishop, Spring
24
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.6 PATTERN RECOGNITION 3-0-0 3 2020
Pre-requisites: Base knowledge of Probability and Statistics
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Introduction to pattern and classification, supervised and unsupervised
learning, Clustering vs classification, Bayesian Decision Theory-Minimum
error rate classification Classifiers, discriminant functions, decision surfaces
-The normal density and discriminant-functions for the Normal density.
6 15
MODULE: 2
Parametric and Non Parametric technique: Parametric estimation
Technique:-Maximum-Likelihood (ML) estimation, Bayesian estimation,
Non Parametric density estimation:-Parzen-window method, K-Nearest
Neighbour method.
6 15
FIRST INTERNAL TEST
MODULE: 3
Linear discriminant based classifiers and tree classifiers: Linear
discriminant function based classifiers-Perceptron-Minimum Mean Squared
Error (MME) method, Support Vector machine, Decision Trees: CART,
C4.5, ID3
8 15
MODULE: 4
Unsupervised Methods: Component Analysis and Dimension Reduction:-
The Curse of Dimensionality ,Principal Component Analysis ,Fisher Linear
Discriminant analysis. Clustering:- Basics of Clustering; similarity /
dissimilarity measures; clustering criteria. Different distance functions and
similarity measures, K-means algorithm.
6 15
SECOND INTERNAL TEST
MODULE: 5
Regression, Graphical methods: Regression:- Introduction to Linear
models for regression, Polynomial regression and Bayesian regression,
Graphical Models:-Bayesian belief network and Hidden Markov Models
8 20
MODULE: 6
Recent Advances:- Neural network structures for pattern recognition - Self
organizing networks - Fuzzy logic – Fuzzy pattern classifiers -Pattern
classification using Genetic Algorithms.
6 20
25
Course No: 02ME6451.7
Course Title: INTRODUCTION TO HUMAN COMPUTER INTERFACE
Credits: 3-0-0:
Course Objectives
• Introduce the student to the concepts of human-computer interaction.
• Understand the need of good user interface design
• Provide user interface design with concepts and strategies for making design
decisions.
• Expose user interface design tools, techniques, and ideas for interface design.
Syllabus
HCI foundation and history; Usability life cycle and methods; Design rules and guidelines;
Empirical research methods; Models in HCI- GOMS, Fitts’ law and Hick-Hyman’s law; Task
analysis; Dialogue design; Cognitive architecture and HCI ; Graphic User Interfaces &
aesthetics; Usability Testing; UML,OOP,OOM; Design Case Studies.
Course Outcomes
• Familiarization with basics of HCI
• Comprehend and understand various user interface and design methodologies
• Equip to select appropriate design contents to develop cognitive model
• Develop sufficient technical know-how to apply the fundamental concepts of HCI to
solve real world problems
References
1. Human – Computer Interaction. ALAN DIX, JANET FINCAY, GRE GORYD,
ABOWD, RUSSELL BEALG,., 3rd Edition, Pearson Education, 5th edition, ISBN-13:
978-0130461094, ISBN-10: 0130461091, 2014
2. “Interaction Design”, Prece, Rogers, Sharps, Wiley, ISBN: 978-1-119-02075-2., 3rd
Edition, 2011.
3.The essential guide to user interface design”, Wilbert O Galitz, “Wiley, 3rdEd,
2007, ISBN: 978-0-471-27139-0.
4. B.Shneiderman; Designing the User Interface,Addison Wesley 2000 (Indian
Reprint).
26
Course No: Name of Course L-T-P Credits Year of
Introduction
02ME6451.7 INTRODUCTION TO HUMAN
COMPUTER INTERFACE 3-0-0 3 2020
Pre-requisites: Nil
MODULES Contact
hours
Sem Exam
Marks %
MODULE: 1
Introduction to HCI: Why Design for Usability? Historical Perspective:
machinery, computers, PCs and GUI networks, mobile, Possible Futures
Human Perception, Information Presentation and Layout: Perception,
gestalt perception, typography, Color, Graphic design Displays, Paper, and
other Output Devices, Information Visualization
6 15
MODULE: 2
Model Based System Design: Basic idea, introduction to different types of
models, GOMS family of models (KLM and CMN-GOMS), Fitts’ law and
HickHyman’s law
The Human Body and Device Design: Input Devices and Ergonomics,
Virtual Reality, GOMS Keystroke- Level Modelling, Time scales and the
illusion of Multitasking, Hypothesis Testing and Statistical Significance
7 15
FIRST INTERNAL TEST
MODULE: 3
Guidelines in HCI: Shneiderman’s eight golden rules, Norman’s seven
Principles, Norman’s model of interaction, Nielsen’s ten heuristics with
example of its use, Heuristic evaluation, Contextual inquiry, Cognitive
walkthrough
6 15
MODULE: 4
Task Modeling and Analysis: Hierarchical task analysis (HTA),
Engineering task models and Concur Task Tree (CTT)
7 15
SECOND INTERNAL TEST
MODULE: 5
Dialog Design: Introduction to formalism in dialog design, design using
FSM (finite state machines) State charts and (classical) Petri Nets in dialog
design
7 20
MODULE: 6
Cognitive Architecture: Introduction to CA, CA types,relevance of CA in IS
design, Model Human Processor (MHP)
Case studies
6 20
27
Course No: 02CA6001
Course Title: RESEARCH METHODOLOGY
L-T-P-Credits: 1-1-0 : 2
Course Objectives
• Students should get the ability to identify problem related to research topic and to characterize
the research problems. • To developed physical insight about the research design and to develop a more reliable
design. • To study about the research by the methods of data analysis and to develop report and thesis
according to the data.
Syllabus
Introduction to research, objectives of research-types of research, research problems review of
literature, research design, data collection and analysis, research reporting, research application
and ethics.
Expected outcomes
Students will develop an understanding of the potential benefits and technical challenges
associated with conducting a research and the development of thesis and reports according to
the research carried out.
References:
1. Donald R. Cooper, Pamela S. Schindler, Business Research Methods, Tata McGraw-
Hill.
2. Stuart Melville and Wayne Goddard, Research Methodology: An Introduction for Science and
Engineering Students, Wiley
3. C. R. Kothari, Research Methodology Methods and Technique, Tata McGraw-Hill.
4. Leedy, P.D. and Ormirod, J.E., Practical Research : Planning and Design, Prentice Hall
5. Donald H. McBurney, Research Methods, Thomson Learning.
6. Turabian, K.L Revised by Grossman, J. and Bennert, A., A Manual for writers of term papers, thesis and dissertation, University of Chicago press.
28
Course No: Name of Course L-T-P Credits Year of
Introduction
02CA6001 RESEARCH METHODOLOGY 1-1-0 2 2020
Pre-requisites: Nil
MODULES Contact
Hours
Sem
Exam
Marks %
Module I:
Introduction To Research : Meaning and definition of Research- Motivation
and Objectives of research-Types of research- fundamental – applied
descriptive-analytical– qualitative-quantitative-conceptual empirical-research
and scientific methods-research process-criteria for good research
5 9
Module II:
Research Problems : Sources Of Research Problems-Characteristics Of A
Research Problem- Problem Defining Techniques-Sources Of Literature
Review Of Literature-Issues And Gap Areas Identification-Purpose of study-
exploratory and descriptive-qualities of good hypothesis-null and alternative
hypothesis- importance of hypothesis testing
4 9
FIRST ASSESSMENT Module III:
Research Design: Features of good design- different research designs –
Laboratory and field experiments- measurement concepts- scales and levels-
Measurement of variables- Factors affecting validation- Internal and external
validation- Reliability- Stability methods- Development of experimental and
sample designs.
4 9
Module IV:
Data Collection And Analysis: Methods of data collection- Data sources –
Surveys and questionnaires- Methods of data collection and their utility-
Concepts of statistical population- Sampling techniques – Probabilistic and
non-probabilistic samples- Sample size determination issues- Primary and
secondary data analysis- Use of computers, internet and library- Data analysis
with statistical packages- Preparation of data for analysis
5 9
SECOND ASSESSMENT
Module V:
Research Reporting : Purpose of written reports- Concept of audience- Types
of reports- Structure and components of reports- Technical report and thesis-
Features of a good thesis- Layout and language of reports- Illustrations- Tables-
Referencing- Footnotes- Intellectual contents of the thesis- Making oral
presentations- Effective communications- Publishing research findings-
Defending the thesis.
5 12
Module VI:
Research Application And Ethics: Application of results of research
outcome- environmental impacts- Professional ethics- Ethical issues and
committees- Copy right- Royalty- Intellectual property rights- Patent laws and
patenting- Reproduction of published material- Plagiarism- Citation and
acknowledgement- Reproducibility and accountability- Developing research
proposals.
5 12
END SEMESTER ASSESSMENT
29
SEMINAR
Course No: 02ME6461
Course Title: SEMINAR I
Credits: 0-0-2:
Internal marks: 100
To enable a student to be familiar with communication skills. Each student is required to select a
topic on advanced technologies in Artificial Intelligence and allied subject domains and get it
approved by the faculty-in-charge of seminar. He/she should give a presentation with good
quality slides. An abstract of the seminar should be submitted to the faculty members well in
advance before the date of seminar. He/she should also prepare a well-documented report on
the seminar in approved format and submit to the department
Student is expected to learn:
a. How to Make a Presentation
1. Verbal
2. Non Verbal
3. Power Point
b. How to write a report
1. Abstract
2. Body
3. Conclusions
4. Executive Summary
c. Group Discussion
1. Share the work with a group
2. Modularization of the work
d. Communication
1. Horizontal
2. Vertical
Evaluation: A committee with the Head of the department as the chairman and two faculty
members from the department as members shall evaluate the seminar based on the coverage of
the topic, presentation and ability to answer the questions put forward by the committee.
Students will be Given a Topic of Importance and are expected
A. To Present the Topic Verbally in 30 minutes + Question Answering
B. To Present the Topic as a Report in not less than 50 Pages
30
Course No: 02ME6471
Course Title: PROGRAMMING LAB
Course Credit: 0-0-2:1 Year: 2020
Internal Mark: 100
Course Objectives:
• To implement the applications of linear and non-linear data structures.
• To implement algorithms for various sorting and searching techniques.
• To develop program with minimum time and space complexity
Syllabus
The syllabus consists of 20 experiments covering the topics of the course “Data Structures and
Algorithms”. Student requires a strong knowledge in any one of the programming languages.
Expected Course Outcomes:
At the end of the course, student will able to
• Develop strong knowledge on the implementation aspects of algorithms
• Implement efficient algorithms by identifying suitable data structures to solve real world
problems.
• Implement existing algorithms and compare the time complexity.
• Analyze the efficiency of algorithms by implementing programs using different data
structures.
List of Experiments:
1. a. Write a program for the following operations on Single Linked List.
(i) Creation (ii) insertion (iii) deletion (iv) traversal
b. To store a polynomial expression in memory using single linked list
2 Write a program to implement following using single linked list
(i). to check whether list is Palindrome.
(ii). Reverse a singly Link List
(iii). Move last element to front of a singly link list.
(iv). Detect a Loop in Singly Link List. [Hint: Floyd’s Cycle-Finding Algorithm.]
3. a. Write a program for the following operations on Circular Linked List.
31
(i) Creation (ii) insertion (iii) deletion (iv) traversal
4. Write a program to implement following using doubly link list
(i). Swap kth node from beginning with kth node from end
(ii). Merge two sorted arrays with O(1) extra space
5. Write a program to
i). To find occurrence of each element with minimal time complexity
ii) to construct a sparse matrix
6. Write a program to implement the following:
(i). Uses Stack operations to convert infix expression into postfix expression.
(ii). Uses Stack operations for evaluating the postfix expression.
(iii). Tower of Hanoi
7. Write a program to implement circular queue with following operation
(i) Creation (ii) insertion (iii) deletion (iv) traversal
8 Write a program to check if a queue can be sorted into another queue using a stack
9. Write a program to implement priority queue using max Heap.
10. Write a program to find the kth largest and smallest element in a list of number without
sorting.[Hint: Max Heap]
11. Write a program to represent graph using
(i). Adjacency Matrix
(ii). Adjacency List
12. Write a program to perform the following:
a. Create a binary search tree.
b. Display the BST using preorder, post order and in order.
c. Find distance between two nodes of a Binary Search Tree
d. Remove all leaf nodes in a BST.
13. Write a program to implement
a. AVL Tree
b. RB Tree
with following operations
(i) insertion (ii) deletion (iii) display
32
14. Write a program to implement search techniques
a. Linear Search
b. Binary Search
c. Hashing using Linear Probing
15. Write a program to implement following sorting technique
a. Merge Sort
b. Quick Sort
c. Radix Sort
16. Write a program with optimized time to search an element in a sorted and rotated array.
[Hint: A[] ={23,56,78,12,18,21], from index k=3 onwards the array A is a sorted rotated
array.]
17. Write a python program to sort an array containing two types of elements in O(n) time.
Hint: A[] ={1,0,0,1,1,0,0,1}
18. (i). Write a program to generate Fibonacci series using dynamic programming methodology.
(ii). Write a program to implement matrix chain multiplication.
19. Write a program to implement the following graph traversal algorithms:
a. Depth first search.
b. Breadth first search.
20. Write a program to implement
i) .Dijkstra’s Algorithm
ii). Minimum spanning tree of a weighted graph