M. Tech – I Year – I Sem. (Computer Science)
ADVANCED DATA STRUCTURES AND ALGORITHMS
Syllabus
ADVANCED DATA STRUCTURES AND ALGORITHMS
Syllabus
- UNIT I
- Algorithms, Performance analysis- time complexity and space complexity, Asymptotic Notation-Big
- Oh, Omega and Theta notations, Complexity Analysis Examples.
- Data structures-Linear and non linear data structures, ADT concept, Linear List ADT, Array
- representation, Linked representation, Vector representation, singly linked lists -insertion, deletion,
- search operations, doubly linked lists-insertion, deletion operations, circular lists. Representation of
- single, two dimensional arrays, Sparse matrices and their representation.
- UNIT II
- 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, java.util package-ArrayList, Linked List, Vector classes, Stacks and
- Queues in java.util, Iterators in java.util.
- UNIT III
- Searching–Linear and binary search methods, Hashing-Hash functions, Collision Resolution
- methods-Open Addressing, Chaining, Hashing in java.util-HashMap, HashSet, Hashtable.
- Sorting –Bubble sort, Insertion sort, Quick sort, Merge sort, Heap sort, Radix sort, comparison of
- sorting methods.
- UNIT IV
- Trees- Ordinary and Binary trees terminology, Properties of Binary trees, Binary tree ADT,
- representations, recursive and non recursive traversals, Java code for traversals, Threaded binary
- trees.
- Graphs- Graphs terminology, Graph ADT, representations, graph traversals/search methods-dfs and
- bfs, Java code for graph traversals, Applications of Graphs-Minimum cost spanning tree using
- Kruskal’s algorithm, Dijkstra’s algorithm for Single Source Shortest Path Problem.
- UNIT V
- 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, Trees in
- java.util- TreeSet, Tree Map Classes, Tries(examples only),Comparison of Search trees.
- Text compression-Huffman coding and decoding, Pattern matching-KMP algorithm.
- TEXT BOOKS:
- 1. Data structures, Algorithms and Applications in Java, S.Sahni, Universities Press.
- 2. Data structures and Algorithms in Java, Adam Drozdek, 3rd edition, Cengage Learning.
- 3. Data structures and Algorithm Analysis in Java, M.A.Weiss, 2nd edition, Addison-Wesley
- (Pearson Education).
- REFERENCE BOOKS:
- 1. Java for Programmers, Deitel and Deitel, Pearson education.
- 2. Data structures and Algorithms in Java, R.Lafore, Pearson education.
- 3. Java: The Complete Reference, 8th editon, Herbert Schildt, TMH.
- 4. Data structures and Algorithms in Java, M.T.Goodrich, R.Tomassia, 3rd edition,
- Wiley India Edition.
- 5. Data structures and the Java Collection Frame work,W.J.Collins, Mc Graw Hill.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
COMPUTER SYSTEM DESIGN
Objectives:
To apply the fundamentals of Computer Systems Design and IT in devising IT solutions.
To Design, simulate, and analyze digital hardware.
To Interface between basic hardware and software computing systems.
To Simulate and evaluate different computing architectures.
UNIT I
Computer structure – hardware, software, system software, Von-Neumann architecture – case
study. IA -32 Pentium: registers and addressing, instructions, assembly language, program flow
control, logic and shift/rotate instructions, multiply, divide MMX, SIMD instructions, I/O operations,
subroutines.
Input/output organization, interrupts, DMA, Buses, Interface circuits, I/O interfaces, device drivers in
windows, interrupt handlers
UNIT II
Processing Unit: Execution of a complete instruction, multiple bus organization, hardwired control,
micro programmed control.
Pipelining: data hazards, instruction hazards, influence on instruction sets, data path & control
consideration, and RISC architecture introduction.
UNIT – III
Memory: types and hierarchy, model level organization, cache memory, performance considerations,
mapping, virtual memory, swapping, paging, segmentation, replacement policies.
UNIT – IV
Processes and Threads: processes, threads, inter process communication, classical IPC problems,
Deadlocks.
UNIT – V
File system: Files, directories, Implementation, Unix file system
Security: Threats, intruders, accident data loss, basics of cryptography, user authentication.
TEXT BOOKS:
1. Computer Organization – Car Hamacher, Zvonks Vranesic, SafeaZaky, Vth Edition,
McGraw Hill.
2. Modern Operating Systems, Andrew S Tanenbaum 2nd edition Pearson/PHI
REFERENCE BOOKS:
1. Computer Organization and Architecture – William Stallings Sixth Edition, Pearson /PHI
2. Morris Mano- Computer System Architecture –3rd Edition-Pearson Education.
3. Operating System Principles- Abraham Silberchatz, Peter B. Galvin, Greg Gagne 7th
Edition, John Wiley
4. Operating Systems – Internals and Design Principles Stallings, Fifth Edition–2005,
Pearson Education/PHI
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
ADVANCED OPERATING SYSTEMS
Objectives:
To understand main components of Real time Operating system and their working
To study the operations performed by OS as a resource manager
To understand the scheduling policies of DOS
To implement the working principles of OS
To study different OS and compare their features
UNIT I:
Real-time operating systems: Design issues, principles and case study.
UNIT II:
Distributed operating system: Design issues, features and principles of working, case study.
UNIT III:
Network operating system: Design issues, working principles and characteristic features, case
study.
UNIT IV:
Kernel development: Issues and development principles, case study.
UNIT V:
Protection, privacy, access control and security issues, solutions.
TEXT BOOKS:
1. A. Silberschatz - Applied Operating System Concepts, Wiley, 2000.
2. Lubemir F Bic and Alan C. Shaw - Operating System Principles, Pearson Education, 2003.
REFERENCE BOOKS:
1. Operating Systems : Internal and Design Principles - Stallings, 6th ed., PE.
2. Modern Operating Systems, Andrew S Tanenbaum 3rd ed., PE.
3. Operating System Principles- Abraham Silberchatz, Peter B. Galvin, Greg Gagne, 7th ed.,, John
Wiley
4. UNIX User Guide – Ritchie & Yates.
5. UNIX Network Programming - W.Richard Stevens ,1998, PHI.
6. The UNIX Programming Environment – Kernighan & Pike, PE.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
SOFTWARE PROCESS AND PROJECT MANAGEMENT
Objectives:
Describe and determine the purpose and importance of project management from the
perspectives of planning, tracking and completion of project.
Compare and differentiate organization structures and project structures.
Implement a project to manage project schedule, expenses and resources with the
application of suitable project management tools.
UNIT I
Software Process Maturity :Software maturity Framework, Principles of Software Process Change,
Software Process Assessment, The Initial Process, The Repeatable Process, The Defined Process,
The Managed Process, The Optimizing Process.
Process Reference Models : Capability Maturity Model (CMM), CMMI, PCMM, PSP, TSP.
UNIT II
Software Project Management Renaissance : Conventional Software Management, Evolution of
Software Economics, Improving Software Economics, The old way and the new way.
Life-Cycle Phases and Process artifacts : Engineering and Production stages, inception phase,
elaboration phase, construction phase, transition phase, artifact sets, management artifacts,
engineering artifacts and pragmatic artifacts, model based software architectures.
UNIT III
Workflows and Checkpoints of process : Software process workflows, Iteration workflows, Major
milestones, Minor milestones, Periodic status assessments.
Process Planning: Work breakdown structures, Planning guidelines, cost and schedule estimating
process, iteration planning process, Pragmatic planning.
UNIT IV
Project Organizations: Line-of- business organizations, project organizations, evolution of
organizations, process automation.
Project Control and process instrumentation : The seven core metrics, management indicators,
quality indicators, life-cycle expectations, Pragmatic software metrics, and metrics automation.
UNIT V
CCPDS-R Case Study and Future Software Project Management Practices : Modern Project
Profiles, Next-Generation software Economics, Modern Process Transitions.
TEXT BOOKS:
1. Managing the Software Process, Watts S. Humphrey, Pearson Education,1999
2. Software Project Management, Walker Royce, Pearson Education,1998
REFERENCE BOOKS:
1. Effective Project Management: Traditional, Agile, Extreme, Robert Wysocki, Sixth edition, Wiley
India, rp2011.
2. An Introduction to the Team Software Process, Watts S. Humphrey, Pearson Education, 2000
3. Process Improvement essentials, James R. Persse, O’Reilly, 2006
3. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, TMH, 2006
4. Applied Software Project Management, Andrew Stellman & Jennifer Greene, O’Reilly, 2006.
5. Head First PMP, Jennifer Greene & Andrew Stellman, O’Reilly, 2007
6. Software Engineering Project Managent, Richard H. Thayer & Edward Yourdon, 2nd edition,
Wiley India, 2004.
7. The Art of Project Management, Scott Berkun, SPD, O’Reilly, 2011.
8. Applied Software Project Management, Andrew Stellman & Jennifer Greene, SPD, O’Reilly,
rp2011.
9. Agile Project Management, Jim Highsmith, Pearson education, 2004.
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
SOFTWARE PROCESS AND PROJECT MANAGEMENT
Objectives:
Describe and determine the purpose and importance of project management from the
perspectives of planning, tracking and completion of project.
Compare and differentiate organization structures and project structures.
Implement a project to manage project schedule, expenses and resources with the
application of suitable project management tools.
UNIT I
Software Process Maturity :Software maturity Framework, Principles of Software Process Change,
Software Process Assessment, The Initial Process, The Repeatable Process, The Defined Process,
The Managed Process, The Optimizing Process.
Process Reference Models : Capability Maturity Model (CMM), CMMI, PCMM, PSP, TSP.
UNIT II
Software Project Management Renaissance : Conventional Software Management, Evolution of
Software Economics, Improving Software Economics, The old way and the new way.
Life-Cycle Phases and Process artifacts : Engineering and Production stages, inception phase,
elaboration phase, construction phase, transition phase, artifact sets, management artifacts,
engineering artifacts and pragmatic artifacts, model based software architectures.
UNIT III
Workflows and Checkpoints of process : Software process workflows, Iteration workflows, Major
milestones, Minor milestones, Periodic status assessments.
Process Planning: Work breakdown structures, Planning guidelines, cost and schedule estimating
process, iteration planning process, Pragmatic planning.
UNIT IV
Project Organizations: Line-of- business organizations, project organizations, evolution of
organizations, process automation.
Project Control and process instrumentation : The seven core metrics, management indicators,
quality indicators, life-cycle expectations, Pragmatic software metrics, and metrics automation.
UNIT V
CCPDS-R Case Study and Future Software Project Management Practices : Modern Project
Profiles, Next-Generation software Economics, Modern Process Transitions.
TEXT BOOKS:
1. Managing the Software Process, Watts S. Humphrey, Pearson Education,1999
2. Software Project Management, Walker Royce, Pearson Education,1998
REFERENCE BOOKS:
1. Effective Project Management: Traditional, Agile, Extreme, Robert Wysocki, Sixth edition, Wiley
India, rp2011.
2. An Introduction to the Team Software Process, Watts S. Humphrey, Pearson Education, 2000
3. Process Improvement essentials, James R. Persse, O’Reilly, 2006
3. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, TMH, 2006
4. Applied Software Project Management, Andrew Stellman & Jennifer Greene, O’Reilly, 2006.
5. Head First PMP, Jennifer Greene & Andrew Stellman, O’Reilly, 2007
6. Software Engineering Project Managent, Richard H. Thayer & Edward Yourdon, 2nd edition,
Wiley India, 2004.
7. The Art of Project Management, Scott Berkun, SPD, O’Reilly, 2011.
8. Applied Software Project Management, Andrew Stellman & Jennifer Greene, SPD, O’Reilly,
rp2011.
9. Agile Project Management, Jim Highsmith, Pearson education, 2004.
M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
DISTRIBUTED SYSTEMS
(ELECTIVE-I)
Objectives:
To explain what a distributed system is, why you would design a system as a distributed
system, and what the desired properties of such systems are;
To list the principles underlying the functioning of distributed systems, describe the problems
and challenges associated with these principles, and evaluate the effectiveness and
shortcomings of their solutions;
To recognize how the principles are applied in contemporary distributed systems, explain how
they affect the software design, and be able to identify features and design decisions that may
cause problems;
To design a distributed system that fulfills requirements with regards to key distributed
systems properties (such as scalability, transparency, etc.), be able to recognize when this is
not possible, and explain why;
To build distributed system software using basic OS mechanisms as well as higher-level
middleware and languages.
UNIT I
Characterization of Distributed Systems- Introduction, Examples of Distributed systems, Resource
sharing and web, challenges, System models- Introduction, Architectural and Fundamental models,
Networking and Internetworking, Interprocess Communication.
Distributed objects and Remote Invocation-Introduction, Communication between distributed objects,
RPC, Events and notifications, Case study-Java RMI.
UNIT II
Operating System Support- Introduction, OS layer, Protection, Processes and Threads,
Communication and Invocation, Operating system architecture, Distributed File Systems-Introduction,
File Service architecture, case study- SUN network file systems.
Name Services-Introduction, Name Services and the Domain Name System, Case study of the Global
Name Service, Case study of the X.500 Directory Service.
UNIT III
Peer to Peer Systems-Introduction, Napster and its legacy, Peer to Peer middleware, Routing
overlays, Overlay case studies-Pastry, Tapestry, Application case studies-Squirrel, OceanStore.
Time and Global States-Introduction, Clocks, events and Process states, Synchronizing physical
clocks, logical time and logical clocks, global states, distributed debugging.
Coordination and Agreement - Introduction, Distributed mutual exclusion, Elections, Multicast
communication, consensus and related problems.
UNIT IV
Transactions and Concurrency control - Introduction, Transactions, Nested Transactions, Locks,
Optimistic concurrency control, Timestamp ordering, Comparison of methods for concurrency
controls. Distributed Transactions - Introduction, Flat and Nested Distributed Transactions, Atomic
commit protocols, Concurrency control in distributed transactions, Distributed deadlocks, Transaction
recovery, Replication-Introduction, System model and group communication, Fault tolerant services,
Transactions with replicated data.
UNIT V
Security - Introduction, Overview of Security techniques, Cryptographic algorithms, Digital signatures,
Case studies-Kerberos, TLS, 802.11 WiFi.
Distributed shared memory, Design and Implementation issues, Sequential consistency and Ivy case
study, Release consistency and Munin case study, other consistency models, CORBA case study-
Introduction, CORBA RMI, CORBA Services.
TEXT BOOKS:
M. TECH. COMPUTER SCIENCE-R13 Regulations
1. Distributed Systems Concepts and Design, G Coulouris, J Dollimore and T Kindberg, Fourth
Edition, Pearson Education.
2. Distributed Systems, S.Ghosh, Chapman & Hall/CRC, Taylor & Francis Group, 2010.
REFERENCE BOOKS:
1. Distributed Computing, S.Mahajan and S.Shah, Oxford University Press.
2. Distributed Operating Systems Concepts and Design, Pradeep K.Sinha, PHI.
3. Advanced Concepts in Operating Systems, M Singhal, N G Shivarathri, Tata McGraw-Hill
Edition.
4. Reliable Distributed Systems, K.P.Birman, Springer.
5. Distributed Systems – Principles and Paradigms, A.S. Tanenbaum and M.V. Steen, Pearson
Education.
6. Distributed Operating Systems and Algorithm Analysis, R.Chow, T.Johnson, Pearson.
7. Distributed Operating Systems, A.S.Tanenbaum, Pearson education.
8. Distributed Computing, Principles, Algorithms and Systems, Ajay D. Kshemakalyani &
Mukesh Singhal, Cambrigde, rp 2010
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M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
NATURAL LANGUAGE PROCESSING
(ELECTIVE-I)
Objectives:
To acquire basic understanding of linguistic concepts and natural language complexity,
variability.
To acquire basic understanding of machine learning techniques as applied to language.
To implement N-grams Models.
UNIT I
Introduction and Overview What is Natural Language Processing, hands-on demonstrations.
Ambiguity and uncertainty in language. The Turing test. Regular Expressions Chomsky hierarchy,
regular languages, and their limitations. Finite-state automata. Practical regular expressions for
finding and counting language phenomena. A little morphology. Exploring a large corpus with regex
tools. Programming in Python An introduction to programming in Python. Variables, numbers,
strings, arrays, dictionaries, conditionals, iteration. The NLTK (Natural Language Toolkit) String Edit
Distance and Alignment Key algorithmic tool: dynamic programming, a simple example, use in
optimal alignment of sequences. String edit operations, edit distance, and examples of use in spelling
correction, and machine translation.
UNIT II
Context Free Grammars Constituency, CFG definition, use and limitations. Chomsky Normal Form.
Top-down parsing, bottom-up parsing, and the problems with each. The desirability of combining
evidence from both directions Non-probabilistic Parsing Efficient CFG parsing with CYK, another
dynamic programming algorithms. Early parser. Designing a little grammar, and parsing with it on
some test data. Probability Introduction to probability theory Joint and conditional probability,
marginals, independence, Bayes rule, combining evidence. Examples of applications in natural
language. Information Theory The "Shannon game"--motivated by language! Entropy, crossentropy,
information gain. Its application to some language phenomena.
UNIT III
Language modeling and Naive Bayes
Probabilistic language modeling and its applications. Markov models. N-grams. Estimating the
probability of a word, and smoothing. Generative models of language. Part of Speech Tagging and
Hidden Markov Models, Viterbi Algorithm for Finding Most Likely HMM Path Dynamic programming
with Hidden Markov Models, and its use for part-of-speech tagging, Chinese word segmentation,
prosody, information extraction, etc.
UNIT IV
Probabilistic Context Free Grammars
Weighted context free grammars. Weighted CYK. Pruning and beam search.
Parsing with PCFGs
A tree bank and what it takes to create one. The probabilistic version of CYK. Also: How do humans
parse? Experiments with eye-tracking. Modern parsers.
Maximum Entropy Classifiers
The maximum entropy principle and its relation to maximum likelihood. Maximum entropy classifiers
and their application to document classification, sentence segmentation, and other language tasks
UNIT V
Maximum Entropy Markov Models & Conditional Random Fields
Part-of-speech tagging, noun-phrase segmentation and information extraction models that combine
maximum entropy and finite-state machines. State-of-the-art models for NLP.
Lexical Semantics Mathematics of Multinomial and Dirichlet distributions, Dirichlet as a smoothing
for multinomial’s.
Information Extraction & Reference Resolution- Various methods, including HMMs. Models of
anaphora resolution. Machine learning methods for co reference.
M. TECH. COMPUTER SCIENCE-R13 Regulations
TEXT BOOKS:
1. "Speech and Language Processing": Jurafsky and Martin, Prentice Hall
2. "Statistical Natural Language Processing"- Manning and Schutze, MIT Press
3. “Natural Language Understanding”. James Allen. The Benajmins/Cummings Publishing Company
REFERENCES BOOKS:
1. Cover, T. M. and J. A. Thomas: Elements of Information Theory. Wiley.
2. Charniak, E.: Statistical Language Learning. The MIT Press.
3. Jelinek, F.: Statistical Methods for Speech Recognition. The MIT Press.
4. Lutz and Ascher - "Learning Python", O'Reilly
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M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
PATTERN RECOGNITION
(ELECTIVE – I)
Objectives:
To implement pattern recognition and machine learning theories
To design and implement certain important pattern recognition techniques
To apply the pattern recognition theories to applications of interest
To implement the entropy minimization, clustering transformation and feature ordering
UNIT I
INTRODUCTION - Basic concepts, Applications, Fundamental problems in pattern Recognition
system design, Design concepts and methodologies, Examples of Automatic Pattern recognition
systems, Simple pattern recognition model
DECISION AND DISTANCE FUNCTIONS - Linear and generalized decision functions, Pattern space
and weight space, Geometrical properties, implementations of decision functions, Minimum-distance
pattern classifications.
UNIT II
PROBABILITY - Probability of events: Random variables, Joint distributions and densities,
Movements of random variables, Estimation of parameter from samples. STATISTICAL DECISION
MAKING - Introduction, Baye’s theorem, Multiple features, Conditionally independent features,
Decision boundaries, Unequal cost of error, estimation of error rates, the leaving-one-out-techniques,
characteristic curves, estimating the composition of populations. Baye’s classifier for normal patterns.
UNIT III
NON PARAMETRIC DECISION MAKING - Introduction, histogram, kernel and window estimation,
nearest neighbour classification techniques. Adaptive decision boundaries, adaptive discriminate
functions, Minimum squared error discriminate functions, choosing a decision making techniques.
CLUSTERING AND PARTITIONING - Hierarchical Clustering: Introduction, agglomerative clustering
algorithm, the single-linkage, complete-linkage and average-linkage algorithm. Ward’s method
Partition clustering-Forg’s algorithm, K-means’s algorithm, Isodata algorithm.
UNIT IV
PATTERN PREPROCESSING AND FEATURE SELECTION:
Introduction, distance measures, clustering transformation and feature ordering, clustering in feature
selection through entropy minimization, features selection through orthogonal expansion, binary
feature selection.
UNIT V
SYNTACTIC PATTERN RECOGNITION & APPLICATION OF PATTERN RECOGNITION
Introduction, concepts from formal language theory, formulation of syntactic pattern recognition
problem, syntactic pattern description, recognition grammars, automata as pattern recognizers,
Application of pattern recognition techniques in bio-metric, facial recognition, IRIS scon, Finger prints,
etc.,
TEXT BOOKS:
1. Gose. Johnsonbaugh. Jost. “ Pattern recognition and Image Analysis”, PHI.
2. Tou. Rafael. Gonzalez. “Pattern Recognition Principle”, Pearson Education
REFERENCE BOOK:
1. Richard duda, Hart, David Strok, “Pattern Classification”, John Wiley.
2. Digital Image Processing, M.Anji Reddy, Y.Hari Shankar, BS Publications.
M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
MACHINE LEARNING
(ELECTIVE –II)
Objectives:
To be able to formulate machine learning problems corresponding to different applications.
To understand a range of machine learning algorithms along with their strengths and
weaknesses.
To understand the basic theory underlying machine learning.
To be able to apply machine learning algorithms to solve problems of moderate complexity.
To be able to read current research papers and understands the issues raised by current
research.
UNIT I
INTRODUCTION - Well-posed learning problems, Designing a learning system, Perspectives and
issues in machine learning
Concept learning and the general to specific ordering – Introduction, A concept learning task,
Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and the
candidate elimination algorithm, Remarks on version spaces and candidate elimination, Inductive bias
UNIT II
Decision Tree learning – Introduction, Decision tree representation, Appropriate problems for
decision tree learning, The basic decision tree learning algorithm, Hypothesis space search in
decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning
Artificial Neural Networks – Introduction, Neural network representation, Appropriate problems for
neural network learning, Perceptions, Multilayer networks and the back propagation algorithm,
Remarks on the back propagation algorithm, An illustrative example face recognition
Advanced topics in artificial neural networks
Evaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, A
general approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing
learning algorithms
UNIT III
Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
likelihood and least squared error hypotheses, Maximum likelihood hypotheses for predicting
probabilities, Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, An example learning to classify text, Bayesian belief networks The EM algorithm
Computational learning theory – Introduction, Probability learning an approximately correct
hypothesis, Sample complexity for Finite Hypothesis Space, Sample Complexity for infinite
Hypothesis Spaces, The mistake bound model of learning - Instance-Based Learning- Introduction,
k -Nearest Neighbour Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based
Reasoning, Remarks on Lazy and Eager Learning
Genetic Algorithms – Motivation, Genetic Algorithms, An illustrative Example, Hypothesis Space
Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms
UNIT IV
Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets:
Summary, Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as Inverted
Deduction, Inverting Resolution
Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on
Explanation-Based Learning, Explanation-Based Learning of Search Control Knowledge
UNIT V
Combining Inductive and Analytical Learning – Motivation, Inductive-Analytical Approaches to
Learning, Using Prior Knowledge to Initialize the Hypothesis, Using Prior Knowledge to Alter the
Search Objective, Using Prior Knowledge to Augment Search Operators,
Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic,
Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to
Dynamic Programming
JNTUWORLD
M. TECH. COMPUTER SCIENCE-R13 Regulations
TEXT BOOKS:
Machine Learning – Tom M. Mitchell, - MGH
2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)
REFERENCE BOOKS:
1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W
Hsieh, Cambridge Univ Press.
2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley &
Sons Inc., 2001
3. Chris Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995
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M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
PARALLEL AND DISTRIBUTED ALGORITHMS
(ELECTIVE –II)
Objectives:
To learn parallel and distributed algorithms development techniques for shared memory and
message passing models.
To study the main classes of parallel algorithms.
To study the complexity and correctness models for parallel algorithms.
UNIT-I
Basic Techniques, Parallel Computers for increase Computation speed, Parallel & Cluster Computing
UNIT-II
Message Passing Technique- Evaluating Parallel programs and debugging, Portioning and Divide
and Conquer strategies examples
UNIT-III
Pipelining- Techniques computing platform, pipeline programs examples
UNIT-IV
Synchronous Computations, load balancing, distributed termination examples, programming with
shared memory, shared memory multiprocessor constructs for specifying parallelist sharing data
parallel programming languages and constructs, open MP
UNIT-V
Distributed shared memory systems and programming achieving constant memory distributed shared
memory programming primitives, algorithms – sorting and numerical algorithms.
TEXT BOOK:
1. Parallel Programming, Barry Wilkinson, Michael Allen, Pearson Education, 2nd Edition.
REFERENCE BOOK:
1. Introduction to Parallel algorithms by Jaja from Pearson, 1992.
M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
SOFTWARE ARCHITECTURE AND DESIGN PATTERNS
(ELECTIVE –II)
Objectives:
After completing this course, the student should be able to:
To understand the concept of patterns and the Catalog.
To discuss the Presentation tier design patterns and their affect on: sessions, client access,
validation and consistency.
To understand the variety of implemented bad practices related to the Business and
Integration tiers.
To highlight the evolution of patterns.
To how to add functionality to designs while minimizing complexity
To understand what design patterns really are, and are not
To learn about specific design patterns.
To learn how to use design patterns to keep code quality high without overdesign.
UNIT I
Envisioning Architecture
The Architecture Business Cycle, What is Software Architecture, Architectural patterns, reference
models, reference architectures, architectural structures and views.
Creating an Architecture
Quality Attributes, Achieving qualities, Architectural styles and patterns, designing the Architecture,
Documenting software architectures, Reconstructing Software Architecture.
UNIT II
Analyzing Architectures
Architecture Evaluation, Architecture design decision making, ATAM, CBAM.
Moving from one system to many
Software Product Lines, Building systems from off the shelf components, Software architecture in
future.
UNIT III
Patterns
Pattern Description, Organizing catalogs, role in solving design problems, Selection and usage.
Creational and Structural patterns
Abstract factory, builder, factory method, prototype, singleton, adapter, bridge, composite, façade,
flyweight.
UNIT IV
Behavioral patterns
Chain of responsibility, command, Interpreter, iterator, mediator, memento, observer, state, strategy,
template method, visitor.
UNIT V
Case Studies
A-7E – A case study in utilizing architectural structures, The World Wide Web - a case study in
interoperability, Air Traffic Control – a case study in designing for high availability, Celsius Tech – a
case study in product line development,
TEXT BOOKS:
1. Software Architecture in Practice, second edition, Len Bass, Paul Clements & Rick Kazman,
Pearson Education, 2003.
2. Design Patterns, Erich Gamma, Pearson Education, 1995.
REFERENCE BOOKS:
1. Beyond Software architecture, Luke Hohmann, Addison wesley, 2003.
2. Software architecture, David M. Dikel, David Kane and James R. Wilson, Prentice Hall PTR,
2001
M. TECH. COMPUTER SCIENCE-R13 Regulations
3. Software Design, David Budgen, second edition, Pearson education, 2003
4. Head First Design patterns, Eric Freeman & Elisabeth Freeman, O’REILLY, 2007.
5. Design Patterns in Java, Steven John Metsker & William C. Wake, Pearson education, 2006
6. J2EE Patterns, Deepak Alur, John Crupi & Dan Malks, Pearson education, 2003.
7. Design Patterns in C#, Steven John metsker, Pearson education, 2004.
8. Pattern Oriented Software Architecture, F.Buschmann & others, John Wiley & Sons.
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M. TECH. COMPUTER SCIENCE-R13 Regulations
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
M. Tech – I Year – I Sem. (Computer Science)
ADVANCED DATA STRUCTURES AND ALGORITHMS LAB
Objectives:
The fundamental design, analysis, and implementation of basic data structures.
Basic concepts in the specification and analysis of programs.
Principles for good program design, especially the uses of data abstraction.
Sample Problems on Data structures:
1. Write Java programs that use both recursive and non-recursive functions for
implementing the following searching methods:
a) Linear search b) Binary search
2. Write Java programs to implement the following using arrays and linked lists
a) List ADT
3. Write Java programs to implement the following using an array.
a) Stack ADT b) Queue ADT
4. Write a Java program that reads an infix expression and converts the expression to postfix form.
(Use stack ADT).
5. Write a Java program to implement circular queue ADT using an array.
6. Write a Java program that uses both a stack and a queue to test whether the given string is a
palindrome or not.
7. Write Java programs to implement the following using a singly linked list.
a) Stack ADT b) Queue ADT
8. Write Java programs to implement the deque (double ended queue) ADT using
a) Array b) Singly linked list c) Doubly linked list.
9. Write a Java program to implement priority queue ADT.
10. Write a Java program to perform the following operations:
a) Construct a binary search tree of elements.
b) Search for a key element in the above binary search tree.
c) Delete an element from the above binary search tree.
11. Write a Java program to implement all the functions of a dictionary (ADT) using Hashing.
12. Write a Java program to implement Dijkstra’s algorithm for Single source shortest
path problem.
13. Write Java programs that use recursive and non-recursive functions to traverse the
given binary tree in
a) Preorder b) Inorder c) Postorder.
14. Write Java programs for the implementation of bfs and dfs for a given graph.
15. Write Java programs for implementing the following sorting methods:
a) Bubble sort d) Merge sort g) Binary tree sort
b) Insertion sort e) Heap sort
c) Quick sort f) Radix sort
16. Write a Java program to perform the following operations:
a) Insertion into a B-tree b) Searching in a B-tree
17. Write a Java program that implements Kruskal’s algorithm to generate minimum cost
spanning tree.
18. Write a Java program that implements KMP algorithm for pattern matching.
REFERENCE BOOKS:
1. Data Structures and Algorithms in java, 3rd edition, A.Drozdek, Cengage Learning.
2. Data Structures with Java, J.R.Hubbard, 2nd edition, Schaum’s Outlines, TMH.
3. Data Structures and algorithms in Java, 2nd Edition, R.Lafore, Pearson Education.
4. Data Structures using Java, D.S.Malik and P.S. Nair, Cengage Learning.
5. Data structures, Algorithms and Applications in java, 2nd Edition, S.Sahani, Universities
Press.
6. Design and Analysis of Algorithms, P.H.Dave and H.B.Dave, Pearson education.
7. Data Structures and java collections frame work, W.J.Collins, Mc Graw Hill.
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8. Java: the complete reference, 7th editon, Herbert Schildt, TMH.
9. Java for Programmers, P.J.Deitel and H.M.Deitel, Pearson education / Java: How to
Program P.J.Deitel and H.M.Deitel , 8th edition, PHI.
10. Java Programming, D.S.Malik,Cengage Learning.
11. A Practical Guide to Data Structures and Algorithms using Java, S.Goldman & K.Goldman,
Chapman & Hall/CRC, Taylor & Francis Group.
( Note: Use packages like java.io, java.util, etc)
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