Department Computer Science and Engineering

Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Programming for Problem Solving

Course code CSE101
Category Professional Core Course
Course title Programming for Problem Solving(Theory)
Scheme and Credits Credits 3+0 Semester –I/II
Course Objectives:
Students will be able to develop logics which will help them to create programs, applications
in C also by learning the basic programming constructs they can easily switch over to any
other language in future
Introduction to Programming: Introduction to components of a computer 10 (Lectures)
Unit-1 system, Algorithm: Representation of Algorithm, Flowchart, Pseudo code
with examples, From algorithms to programs, source code. Programming
Basics: Structure of C program: writing and executing the first C program,
Syntax and logical errors in compilation, object and executable code.
Components of C language: Standard I/O in C, Fundamental data types,
Variables and memory locations, Storage classes. Arithmetic expressions
& Conditional Branching: Arithmetic expressions and precedence:
Operators and expression using numeric and relational operators, mixed
operands, type conversion, logical operators, bit operations, assignment
operator, operator precedence and associatively. Conditional Branching:
Applying if and switch statements, nesting if and else, use of break and
default with switch case.
Unit-2 Loops & Functions: Iteration and loops: use of while, do while and for 8 (Lectures)
loops, multiple loop variables, use of break and continue statements.
Functions: Introduction, types of functions, functions with array, passing
parameters to functions, call by value, call by reference, recursive.
Arrays & Basic Algorithms: Arrays: Array notation and representation, 10 (Lectures)
Unit-3 manipulating array elements, using multi-dimensional arrays. Character
arrays and strings, Structure, union, enumerated data types, Array of
structures, Passing arrays to functions. Basic Algorithms: Searching &Basic
Sorting Algorithms (Bubble, Insertion and Selection), Finding roots of
equations, Notion of order of complexity.

Unit-4 Pointer& File Handling: Pointers: Introduction, declaration, applications, 8 (Lectures)

Introduction to dynamic memory allocation (malloc, calloc, realloc, free), Use
of pointers in self-referential structures, notion of linked list (no
implementation) File handling: File I/O functions, Standard C pre-
processors, defining and calling macros, command-line arguments.
Course Outcomes (CO)
At the end of this course students will be able to:
CO1 To develop simple algorithms for arithmetic and logical problems.
CO2 To translate the algorithms to programs & execution (in C language).
CO3 To implement conditional branching, iteration and recursion.
CO4 To decompose a problem into functions and synthesize a complete program using divide
and conquer approach.
CO5 To use arrays, pointers and structures to develop algorithms and programs.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Textbooks & References:

1. The C programming by Kernighan Brain W. and Ritchie Dennis M., Pearson Education.
2. Computer Basics and C Programming by V.Rajaraman , PHI Learning Pvt. Limited,
2015.
3. Computer Concepts and Programming in C, E Balaguruswami, McGraw Hill
4. Computer Science- A Structured Programming Approach Using C, by Behrouz A.
Forouzan, Richard F. Gilberg, Thomson, Third Edition , Cengage Learning - 2007.
5. Let Us C By Yashwant P. Kanetkar,BPB Publication.
6. Problem Solving and Program Design in C, by Jeri R. Hanly, Elliot B. Koffman, Pearson
Addison-Wesley, 2006.
7. Programming in C by Kochan Stephen G. Pearson Education – 2015.
8. Computer Concepts and Programming by Anami, Angadi and Manvi, PHI Publication.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Mathematics for Machine Learning

Course code CSE201
Category Professional Core Course
Course title Mathematics for Machine Learning (Theory)
Scheme and Credits Credits 3+0 Semester –III
Course Objectives:
 To understand the basic theory underlying machine learning.
 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 be able to apply machine learning algorithms to solve problems of moderate complexity.
 To apply the algorithms to a real-world problem, optimize the models learned and report on the
expected accuracy that can be achieved by applying the models.
Machine Learning: Introduction to Machine Learning, Probability Theory, 10 (Lectures)
Unit-1 Model Selection, The Curse of Dimensionality, Decision Theory,
Information Theory
Unit-2 Probability Distributions: Binary Variables, Multinomial Variables, The 8 (Lectures)
Gaussian distribution, The Exponential Family, and Nonparametric
Methods.
Linear Models for Regression: Linear Basis Function Models, The Bias- 10 (Lectures)
Unit-3 Variance Decomposition, Bayesian Linear Regression, Bayesian Model
Comparison, The Evidence Approximation, Limitations of Fixed Basis
Functions
Unit-4 Neural Networks and Kernel Methods 8 (Lectures)
Course Outcomes (CO)
At the end of course, student will be able to
CO1 Explain theory underlying machine learning.
CO2 Construct algorithms to learn linear and non-linear models.
CO3 Implement data clustering algorithms.
CO4 Construct algorithms to learn tree and rule-based models.
CO5 Apply reinforcement learning techniques.
Textbooks & References:
1. Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information
Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
2. Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong,2020, Mathematics For Machine
Learning. MIT-Press, Link: https://mml-book.com
3. Ethem Alpaydın -Introduction to Machine Learning Third Edition, MIT Press, 2004.
4. P. Flach, Machine Learning: The art and science of algorithms that make sense of data,
Cambridge University Press, 2012.
5. K. P. Murphy, Machine Learning: A probabilistic perspective, MIT Press, 2012.
6. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
7. D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.
8. M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, MIT Press,
2012.
9. T. M. Mitchell, Machine Learning, McGraw Hill, 1997.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Data Structure
Course code CSE202
Category Professional Core Course
Course title Data Structure (Theory)
Scheme and Credits Credits 4+0 Semester –III
Course Objectives:
An overview of data structure concepts, arrays, stack, queues, trees, and graphs. Discussion of various
implementations of these data objects, programming styles, and run-time representations. Course also
examines algorithms for sorting, searching and some graph algorithms.
Unit-1 Introduction: Basic Terminology, Elementary Data Organization, Built in 14 (Lectures)
Data Types in C. Algorithm, Efficiency of an Algorithm, Time and Space
Complexity, Asymptotic notations: Big Oh, Big Theta and Big Omega, Time-
Space trade-off. Abstract Data Types (ADT) Arrays: Definition, Single and
Multidimensional Arrays, Representation of Arrays: Row Major Order, and
Column Major Order, Derivation of Index Formulae for 1-D,2-D,3-D and n-D
Array Application of arrays, Sparse Matrices, and their representations.
Linked lists: Array Implementation and Pointer Implementation of Singly
Linked Lists, Doubly Linked List, Circularly Linked List, Operations on a
Linked List. Insertion, Deletion, Traversal, Polynomial Representation and
Addition Subtraction & Multiplications of Single variable & Two variables
Polynomial.
Unit-2 Stacks and Queues: Abstract Data Type, Primitive Stack operations: Push 10 (Lectures)
& Pop, Array and Linked Implementation of Stack in C, Application of stack:
Prefix and Postfix Expressions, Evaluation of postfix expression, Iteration
and Recursion- Principles of recursion, Tail recursion, Removal of
recursion Problem solving using iteration and recursion with examples
such as binary search, Fibonacci numbers, and Hanoi towers. Tradeoffs
between iteration and recursion. Queues: Operations on Queue: Create,
Add, Delete, Full and Empty, Circular queues, Array and linked
implementation of queues in C, Dequeue and Priority Queue.
Unit-3 Searching & Sorting: Concept of Searching, Sequential search, Index 10 (Lectures)
Sequential Search, Binary Search. Concept of Hashing & Collision resolution
Techniques used in Hashing. Sorting: Insertion Sort, Selection, Bubble Sort,
Quick Sort, Merge Sort, Heap Sort and Radix Sort.
Unit-4 Trees: Basic terminology used with Tree, Binary Trees, Binary Tree 14 (Lectures)
Representation: Array Representation and Pointer (Linked List)
Representation, Binary Search Tree, Strictly Binary Tree, Complete Binary
Tree. Extended Binary Trees, Tree Traversal algorithms: In-order, Preorder
and Post-order, Constructing Binary Tree from given Tree Traversal,
Operation of Insertion, Deletion, Searching & Modification of data in Binary
Search. Threaded Binary trees, Traversing Threaded Binary trees. Huffman
coding using Binary Tree. Concept & Basic Operations for AVL Tree, B Tree
& Binary Heaps.
Graphs: Terminology used with Graph, Data Structure for Graph
Representations: Adjacency Matrices, Adjacency List, Adjacency. Graph
Traversal: Depth First Search and Breadth First Search, Connected
Component, Spanning Trees, Minimum Cost Spanning Trees: Prims and
Kruskal algorithm. Transitive Closure and Shortest Path algorithm:
Warshal Algorithm and Dijikstra Algorithm.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Course Outcomes (CO)

At the end of course, student will be able to
CO1 Describe how arrays, linked lists, stacks, queues, trees, and graphs are represented in
memory, used by the algorithms and their common applications.
CO2 Discuss the computational efficiency of the sorting and searching algorithms.
CO3 Implementation of Trees and Graphs and perform various operations on these data
structure.
CO4 Understanding the concept of recursion, application of recursion and its implementation
and removal of recursion.
CO5 Identify the alternative implementations of data structures with respect to its
performance to solve a real-world problem.
Textbooks & References:
1. Aaron M. Tenenbaum, Yedidyah Langsam and Moshe J. Augenstein, “Data Structures Using C and
C++”, PHI Learning Private Limited, Delhi India
2. Horowitz and Sahani, “Fundamentals of Data Structures”, Galgotia Publications Pvt Ltd Delhi
India.
3. Lipschutz, “Data Structures” Schaum’s Outline Series, Tata McGraw-hill Education (India) Pvt. Ltd.
4. Thareja, “Data Structure Using C” Oxford Higher Education.
5. AK Sharma, “Data Structure Using C”, Pearson Education India.
6. Rajesh K. Shukla, “Data Structure Using C and C++” Wiley Dreamtech Publication.
7. Michael T. Goodrich, Roberto Tamassia, David M. Mount “Data Structures and Algorithms in C++”,
Wiley India.
8. P. S. Deshpandey, “C and Data structure”, Wiley Dreamtech Publication.
9. R. Kruse etal, “Data Structures and Program Design in C”, Pearson Education.
10. Berztiss, AT: Data structures, Theory and Practice, Academic Press.
11. Jean Paul Trembley and Paul G. Sorenson, “An Introduction to Data Structures with applications”,
McGraw Hill.
12. Adam Drozdek “Data Structures and Algorithm in Java”, Cengage Learning.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Python with Linux

Course code CSE203
Category Professional Core Course
Course title Python with Linux (Theory)
Scheme and Credits Credits 2+0 Semester –III
Course Objectives:
An overview of data structure concepts, arrays, stack, queues, trees, and graphs. Discussion of
various implementations of these data objects, programming styles, and run-time representations.
Course also examines algorithms for sorting, searching and some graph algorithms.
Unit-1 Introduction: The Programming Cycle for Python, Python IDE, Interacting 5 (Lectures)
with Python Programs, Elements of Python, Type Conversion. Basics:
Expressions, Assignment Statement, Arithmetic Operators, Operator
Precedence, Boolean Expression.
Unit-2 Conditional Statements: Conditional statement in Python (if-else statement, 6 (Lectures)
its working and execution), Nested-if statement and Elif statement in Python,
Expression Evaluation & Float Representation. Loops: Purpose and working
of loops, while loop including its working, For Loop, Nested Loops, Break and
Continue.
Unit-3 Function: Parts of A Function, Execution of A Function, Keyword and Default 5 (Lectures)
Arguments, Scope Rules. Strings: Length of the string and perform
Concatenation and Repeat operations in it. Indexing and Slicing of Strings.
Python Data Structure: Tuples, Unpacking Sequences, Lists, Mutable
Sequences, List Comprehension, Sets, Dictionaries Higher Order Functions:
Treat functions as first-class Objects, Lambda Expressions, Recursion.
Unit-4 Sieve of Eratosthenes: generate prime numbers with the help of an 8 (Lectures)
algorithm given by the Greek Mathematician named Eratosthenes, whose
algorithm is known as Sieve of Eratosthenes. File I/O: File input and output
operations in Python Programming Exceptions and Assertions Modules:
Introduction, Importing Modules, Abstract Data Types: Abstract data types
and ADT interface in Python Programming. Classes: Class definition and
other operations in the classes, Special Methods (such as _init_, _str_,
comparison methods and Arithmetic methods etc.), Class Example,
Inheritance, Inheritance and OOP. Searching and Sorting Techniques.
Linux: Introduction to Linux, Architecture of Linux, General Purpose
Commands, Execution of codes on Linux.
Course Outcome (CO)
At the end of course, the student will be able to
CO1 Read and write simple Python programs.
CO2 Develop Python programs with conditionals and loops.
CO3 Define Python functions and to use Python data structures –- lists, tuples, dictionaries.
CO4 Do input/output with files in Python.
CO5 Do searching, sorting in Python.
Textbooks & References:
1. Allen B. Downey, ``Think Python: How to Think Like a Computer Scientist ‘‘, 2nd edition,
Updated for Python 3, Shroff/O ‘Reilly Publishers, 2016
(http://greenteapress.com/wp/thinkpython/)
2. Guido van Rossum and Fred L. Drake Jr, ―An Introduction to Python – Revised and updated
for Python 3.2, Network Theory Ltd., 2011.
3. 3.John V Guttag, ―Introduction to Computation and Programming Using Python ‘‘, Revised
and expanded Edition, MIT Press, 2013
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

4. 4.Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in

Python: An Inter-disciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016.
5. 5.Timothy A. Budd, ―Exploring Python‖, Mc-Graw Hill Education (India) Private Ltd., 2015.
6. 6.Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE Learning, 2012.
7. 7.Charles Dierbach, ―Introduction to Computer Science using Python: A Computational
ProblemSolving Focus, Wiley India Edition, 2013.
8. 8.Paul Gries, Jennifer Campbell and Jason Montojo, ―Practical Programming: An Introduction
to Computer Science using Python 3, Second edition, Pragmatic Programmers, LLC, 2013.
9. Robert Love, “Linux Kernel Development”, Pearson Education
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Discrete Mathematics
Course code CSE204
Category Professional Core Course
Course title Discrete Mathematics (Theory)
Scheme and Credits Credits 4+0 Semester –III
Course Objectives:
The objective of this course is to teach students how to think logically and mathematically. The
course stresses on mathematical reasoning and describes different ways in which mathematical
problems could be solved. There are four thematic areas covered in this course: mathematical
reasoning, combinatorial analysis, discrete structures, and mathematical modeling.
Unit-1 Set Theory: Introduction, Combination of sets, Multisets, Ordered pairs. 14 (Lectures)
Proofs of some general identities on sets.
Relations: Definition, Operations on relations, Properties of relations,
Composite Relations, Equality of relations, Recursive definition of relation,
Order of relations.
Functions: Definition, Classification of functions, Operations on functions,
recursively defined functions. Growth of Functions.
Natural Numbers: Introduction, Mathematical Induction, Variants of
Induction, Induction with Nonzero Base cases. Proof Methods, Proof by
counter, Proof by contradiction.
Unit-2 Algebraic Structures: Definition, Groups, Subgroups and order, Cyclic 14 (Lectures)
Groups, Cosets, Lagrange's theorem, Normal Subgroups, Permutation and
Symmetric groups, Group Homomorphisms, Definition and elementary
properties of Rings and Fields.
Lattices: Definition, Properties of lattices – Bounded, Complemented,
Modular and Complete lattice.
Boolean Algebra: Introduction, Axioms and Theorems of Boolean algebra,
Algebraic manipulation of Boolean expressions. Simplification of Boolean
Functions, Karnaugh maps, Logic gates, Digital circuits and Boolean
algebra.
Unit-3 Propositional Logic: Proposition, well-formed formula, Truth tables, 10 (Lectures)
Tautology, Satisfiability, Contradiction, Algebra of proposition, Theory of
Inference.
Predicate Logic: First order predicate, well-formed formula of predicate,
quantifiers, Inference theory of predicate logic.
Unit-4 Trees: Definition, Binary tree, Binary tree traversal, Binary search tree. 10 (Lectures)
Graphs: Definition and terminology, Representation of graphs,
Multigraphs, Bipartite graphs, Planar graphs, Isomorphism and
Homeomorphism of graphs, Euler and Hamiltonian paths, Graph coloring,
Recurrence Relation & Generating function: Recursive definition of
functions, Recursive algorithms, Method of solving recurrences.
Combinatorics: Introduction, Counting Techniques, Pigeonhole Principle
Course Outcome (CO)
At the end of course, the student will be able to
CO1 Write an argument using logical notation and determine if the argument is or is not
valid.
CO2 Understand the basic principles of sets and operations in sets.
CO3 Demonstrate an understanding of relations and functions and be able to determine their
properties.
CO4 Demonstrate different traversal methods for trees and graphs.
CO5 Model problems in Computer Science using graphs and trees.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Textbooks & References:

1. Koshy, Discrete Structures, Elsevier Pub. 2008 Kenneth H. Rosen, Discrete
Mathematics and Its Applications, 6/e, cGraw-Hill, 2006.
2. B. Kolman, R.C. Busby, and S.C. Ross, Discrete Mathematical Structures, 5/e, Prentice
Hall, 2004.
3. E. Scheinerman, Mathematics: A Discrete Introduction, Brooks/Cole, 2000.
4. R.P. Grimaldi, Discrete and Combinatorial Mathematics, 5/e, Addison Wesley, 2004
5. Liptschutz, Seymour, “Discrete Mathematics”, McGraw Hill.
6. Trembley, J.P & R. Manohar, “Discrete Mathematical Structure with Application to
Computer Science”, McGraw Hill.
7. Deo, Narsingh, “Graph Theory With application to Engineering and Computer
Science.”, PHI.
8. Krishnamurthy, V., “Combinatorics Theory & Application”, East-West Press Pvt. Ltd.,
New Delhi
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Artificial Intelligence
Course code CSE301
Category Professional Core Course
Course title Artificial Intelligence (Theory)
Scheme and Credits Credits 4+0 Semester –V
Course Objectives:
This course sheds light on the fundamental of Artificial Intelligence and its applications in various
areas.
Unit-1 Introduction: Definition, Future of Artificial Intelligence, Characteristics of 12 (Lectures)
Intelligent Agents, Typical Intelligent Agents, Problem Solving Approach to
Typical AI problems.
Unit-2 Problem Solving Methods: Problem solving Methods, Search Strategies, 12 (Lectures)
Uninformed, Informed, Heuristics, Local Search Algorithms and
Optimization Problems, Searching with Partial Observations, Constraint
Satisfaction Problems, Constraint Propagation, Backtracking Search, Game
Playing, Optimal Decisions in Games, Alpha, Beta Pruning, Stochastic
Games.
Unit-3 Knowledge Representation: First Order Predicate Logic, Prolog 12 (Lectures)
Programming, Unification, Forward Chaining, Backward Chaining,
Resolution, Knowledge Representation, Ontological Engineering,
Categories and Objects, Events, Mental Events and Mental Objects,
Reasoning Systems for Categories, Reasoning with Default Information.
Unit-4 Software Agents & Applications: Architecture for Intelligent Agents, 12 (Lectures)
Agent communication, Negotiation and Bargaining, Argumentation among
Agents, Trust and Reputation in Multi-agent systems.
APPLICATIONS: AI applications, Language Models, Information Retrieval,
Information Extraction, Natural Language Processing, Machine Translation,
Speech Recognition, Robot, Hardware, Perception, Planning, Moving.

Course Outcomes (CO)

At the end of course, student will be able to
CO1 Understand the basics of the theory and practice of Artificial Intelligence as a discipline
and about intelligent agents.
CO2 Understand search techniques and gaming theory.
CO3 Apply knowledge representation techniques and problem-solving strategies to common
AI applications.
CO4 Aware of techniques used for classification and clustering.
CO5 Aware of basics of pattern recognition and steps required for it.
Textbooks & References:
1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach‖, Prentice Hall, Third
Edition, 2009.
2. I. Bratko, “Prolog: Programming for Artificial Intelligence”, Fourth edition, Addison-Wesley
Educational Publishers Inc., 2011.
3. M. Tim Jones, ―Artificial Intelligence: A Systems Approach, Jones and Bartlett Publishers,
Inc.First Edition, 2008
4. Nils J. Nilsson, ―The Quest for Artificial Intelligence, Cambridge University Press, 2009.
5. William F. Clocksin and Christopher S. Mellish, Programming in Prolog: Using the ISO Standard,
Fifth Edition, Springer, 2003.
6. Gerhard Weiss, ―Multi Agent Systems, Second Edition, MIT Press, 2013.
7. David L. Poole and Alan K. Mackworth, ―Artificial Intelligence: Foundations of Computational
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Agents, Cambridge University Press, 2010.

 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Database Management System

Course code CSE302
Category Professional Core Course
Course title Database Management System (Theory)
Scheme and Credits Credits 3+0 Semester –V
Course Objectives:
• To explain basic database concepts, applications, data models, schemas, and instances.
• To demonstrate the use of constraints and relational algebra operations.
• To emphasize the importance of normalization in databases.
• To facilitate students in Database design
• To familiarize issues of concurrency control and transaction management
Unit-1 Introduction: Overview, Database System vs File System, Database System 9 (Lectures)
Concept and Architecture, Data Model Schema and Instances, Data
Independence and Database Language and Interfaces, Data Definitions
Language, DML, Overall Database Structure. Data Modeling Using the Entity
Relationship Model: ER Model Concepts, Notation for ER Diagram, Mapping
Constraints, Keys, Concepts of Super Key, Candidate Key, Primary Key,
Generalization, Aggregation, Reduction of an ER Diagrams to Tables,
Extended ER Model, Relationship of Higher Degree.
Unit-2 Relational Data Model and Language: Relational Data Model Concepts, 9 (Lectures)
Integrity Constraints, Entity Integrity, Referential Integrity, Keys Constraints,
Domain Constraints, Relational Algebra, Relational Calculus, Tuple and
Domain Calculus.
Introduction on SQL: Characteristics of SQL, Advantage of SQL, SQL Data
Type and Literals, Types of SQL Commands, SQL Operators and Their
Procedure, Tables, Views and Indexes, Queries and Sub Queries, Aggregate
Functions, Insert, Update and Delete Operations, Joins, Unions, Intersection,
Minus, Cursors, Triggers, Procedures in SQL/PL SQL.
Unit-3 Data Base Design & Normalization: Functional dependencies, Normal 8 (Lectures)
forms, First normal form, Second normal form, Third normal form, BCNF,
Inclusion dependence, Loss less join decompositions, Normalization using
FD, MVD, and JDs, Alternative approaches to database design.
Unit-4 Transaction Processing Concept: Transaction System, Testing of 10
Serializability, Serializability of Schedules, Conflict & View Serializable (Lectures)
Schedule, Recoverability, Recovery from Transaction Failures, Log Based
Recovery, Checkpoints, Deadlock Handling.
Distributed Database: Distributed Data Storage, Concurrency Control, and
Directory System.
Concurrency Control Techniques: Concurrency Control, Locking
Techniques for Concurrency Control, Time Stamping Protocols for
Concurrency Control, Validation Based Protocol, Multiple Granularity, Multi
Version Schemes, Recovery with Concurrent Transaction, Case Study of
Oracle.
Course Outcomes (CO)
At the end of course, student will be able to
CO1 Apply knowledge of database for real life applications.
CO2 Apply query processing techniques to automate the real time problems of databases.
CO3 Identify and solve the redundancy problem in database tables using normalization.
CO4 Understand the concepts of transactions, their processing so they will be familiar with a
broad range of database management issues including data integrity, security and recovery.
CO5 Design, develop and implement a small database project using database tools.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Textbooks & References:

1. Korth, Silbertz, Sudarshan,” Database Concepts”, McGraw Hill
2. Date C J, “An Introduction to Database Systems”, Addision Wesley
3. Elmasri, Navathe, “Fundamentals of Database Systems”, Addision Wesley
4. O’Neil, Databases, Elsevier Pub.
5. Ramakrishnan"Database Management Systems",McGraw Hill
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Design and Analysis of Algorithm

Course code CSE303
Category Professional Core Course
Course title Design and Analysis of Algorithm (Theory)
Scheme and Credits Credits 4+0 Semester –V
Course Objectives:
The goal of this course is to provide a solid background in the design and analysis of the major
classes of algorithms. At the end of the course students will be able to develop their own versions for
a given computational task and to compare and contrast their performance.
Unit-1 Introduction: Algorithms, Analyzing Algorithms, Complexity of 12 (Lectures)
Algorithms, Growth of Functions, Performance Measurements, Sorting and
Order Statistics - Shell Sort, Quick Sort, Merge Sort, Heap Sort, Comparison
of Sorting Algorithms, Sorting in Linear Time.
Unit-2 Advanced Data Structures: Red-Black Trees, B – Trees, Binomial Heaps, 12 (Lectures)
Fibonacci Heaps, Tries, Skip List.
Unit-3 Divide and Conquer with Examples Such as Sorting, Matrix 12 (Lectures)
Multiplication, Convex Hull and Searching. Greedy Methods with
Examples Such as Optimal Reliability Allocation, Knapsack, Minimum
Spanning Trees – Prim’s and Kruskal’s Algorithms, Single Source Shortest
Paths - Dijkstra’s and Bellman Ford Algorithms.
Unit-4 Dynamic Programming with Examples Such as Knapsack, All Pair 12 (Lectures)
Shortest Paths – Warshal’s and Floyd’s Algorithms, Resource Allocation
Problem. Backtracking, Branch and Bound with Examples Such as
Travelling Salesman Problem, Graph Coloring, n-Queen Problem,
Hamiltonian Cycles and Sum of Subsets.
Course Outcome (CO)
At the end of course, the student will be able to
CO1 Design new algorithms, prove them correct, and analyze their asymptotic and absolute
runtime and memory demands.
CO2 Find an algorithm to solve the problem (create) and prove that the algorithm solves the
problem correctly (validate).
CO3 Understand the mathematical criterion for deciding whether an algorithm is efficient
and knows many practically important problems that do not admit any efficient algorithms.
CO4 Apply classical sorting, searching, optimization and graph algorithms.
CO5 Understand basic techniques for designing algorithms, including the techniques of
recursion, divide-and-conquer, and greedy.
Textbooks & References:
1. Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest, “Introduction to Algorithms”,
Printice Hall of India.
2. E. Horowitz & S Sahni, "Fundamentals of Computer Algorithms",
3. Aho, Hopcraft, Ullman, “The Design and Analysis of Computer Algorithms” Pearson
Education, 2008.
4. LEE "Design & Analysis of Algorithms (POD)”, McGraw Hill
5. Richard E.Neapolitan "Foundations of Algorithms" Jones & Bartlett Learning
6. Jon Kleinberg and Éva Tardos, Algorithm Design, Pearson, 2005.
7. Michael T Goodrich and Roberto Tamassia, Algorithm Design: Foundations, Analysis, and
Internet Examples,Second Edition, Wiley, 2006.
8. Harry R. Lewis and Larry Denenberg, Data Structures and Their Algorithms, Harper Collins,
1997
9. Robert Sedgewick and Kevin Wayne, Algorithms, fourth edition, Addison Wesley, 2011.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

10. Harsh Bhasin,”Algorithm Design and Analysis”,First Edition,Oxford University Press.

11. Gilles Brassard and Paul Bratley, Algorithmics:Theory and Practice,Prentice Hall,1995.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Computer Networks
Course code CSE304
Category Professional Core Course
Course title Computer Networks (Theory)
Scheme and Credits Credits 4+0 Semester –V
Course Objectives:
This course studies the standard models for the layered approach to communication between
autonomous machines in a network, and the main characteristics of data transmission across
various physical link types. It considers how to design networks and protocols for diverse
situations, analyses several application and support protocols from a distributed systems viewpoint,
and identifies significant problem areas in networked communications.
Unit-1 Introductory Concepts: Goals and applications of networks, Categories of 12 (Lectures)
networks, Organization of the Internet, ISP, Network structure and
architecture (layering principles, services, protocols, and standards), The
OSI reference model, TCP/IP protocol suite, Network devices and
components.
Physical Layer: Network topology design, Types of connections,
Transmission media, Signal transmission and encoding, Network
performance and transmission impairments, Switching techniques and
multiplexing.
Unit-2 Link Layer: Framing, Error Detection and Correction, Flow control 12 (Lectures)
(Elementary Data Link Protocols, Sliding Window protocols). Medium
Access Control and Local Area Networks: Channel allocation, Multiple
access protocols, LAN standards, Link layer switches & bridges (learning
bridge and spanning tree algorithms).
Unit-3 Network Layer: Point-to-point networks, Logical addressing, Basic 12 (Lectures)
internetworking (IP, CIDR, ARP, RARP, DHCP, ICMP), Routing, forwarding
and delivery, Static and dynamic routing, Routing algorithms and protocols,
Congestion control algorithms, IPv6.
Unit-4 Transport Layer: Process-to-process delivery, Transport layer protocols 12 (Lectures)
(UDP and TCP), Multiplexing, Connection management, Flow control and
retransmission, Window management, TCP Congestion control, Quality of
service.
Application Layer: Domain Name System, World Wide Web and Hyper
Text Transfer Protocol, Electronic mail, File Transfer Protocol, Remote
login, Network management, Data compression, Cryptography – basic
concepts.
Course Outcomes (CO)
At the end of course, student will be able to
CO1 Explain basic concepts, OSI reference model, services, and role of each layer of OSI model
and TCP/IP, networks devices and transmission media, Analog and digital data transmission.
CO2 Apply channel allocation, framing, error, and flow control techniques.
CO3 Describe the functions of Network Layer i.e., Logical addressing, subnetting & Routing
Mechanism.
CO4 Explain the different Transport Layer functions i.e., Port addressing, Connection
Management, Error control and Flow control mechanism.
CO5 Explain the functions offered by session and presentation layer and their
Implementation.
CO6 Explain the different protocols used at application layer i.e., HTTP, SNMP, SMTP, FTP,
TELNET and VPN.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Textbooks & References:

1. Behrouz Forouzan, “Data Communication and Networking”, McGraw Hill
2. Andrew Tanenbaum “Computer Networks”, Prentice Hall.
3. William Stallings, “Data and Computer Communication”, Pearson.
4. Kurose and Ross, “Computer Networking- A Top-Down Approach”, Pearson.
5. Peterson and Davie, “Computer Networks: A Systems Approach”, Morgan Kaufmann
6. W. A. Shay, “Understanding Communications and Networks”, Cengage Learning.
7. D. Comer, “Computer Networks and Internets”, Pearson.
8. Behrouz Forouzan, “TCP/IP Protocol Suite”, McGraw Hill.
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

Internet Programming using Java

Course code IT305
Category Professional Elective Course
Course title Internet Programming using Java (Theory)
Scheme and Credits Credits 3+0 Semester –V
Course Objectives:
• To learn why Java is useful for the design of desktop and web applications.
• To learn how to implement object-oriented designs with Java.
• To identify Java language components and how they work together in applications.
• To design and program stand-alone Java applications.

Unit-1 Introduction: Introduction and Web Development Strategies, History of 9 (Lectures)

Web and Internet, Protocols governing Web, Writing Web Projects,
Connecting to Internet, Introduction to Internet services and tools,
Introduction to client-server computing.
Core Java: Introduction, Operator, Data type, Variable, Arrays, Methods &
Classes, Inheritance, Package and Interface, Exception Handling, Multithread
programming, I/O, Java Applet, String handling, Event handling, Introduction
to AWT, AWT controls, Layout managers.
Unit-2 Web Page Designing & Scripting 9 (Lectures)
HTML: list, table, images, frames, forms, CSS, Document type definition.
XML: DTD, XML schemes, Object Models, presenting and using XML, Using
XML
Processors: DOM and SAX, Dynamic HTML.
Java script: Introduction, documents, forms, statements, functions, objects;
introduction to AJAX, VB Script, Introduction to Java Beans, Advantage,
Properties, BDK, Introduction to EJB, Java Beans API.
Unit-3 Server Site Programming: Introduction to active server pages (ASP), 9 (Lectures)
Introduction to Java Server Page (JSP), JSP Application Design, JSP objects,
Conditional Processing, declaring variables and methods, sharing data
between JSP pages, Sharing Session and Application Data, Database
Programming using JDBC, development of java beans in JSP, Introduction to
Servlets, Lifecycle, JSDK, Servlet API, Servlet Packages, Introduction to
COM/DCOM/CORBA.
Unit-4 PHP (Hypertext Preprocessor): Introduction, syntax, variables, strings, 9 (Lectures)
operators, if-else, loop, switch, array, function, form, mail, file upload,
session, error, exception, filter, PHP-ODBC.
Course Outcomes (CO)
At the end of course, student will be able to
CO1 Introduce the fundamentals of Java.
CO2Construct basic websites using HTML and Scripting.
CO3 Introduce the fundamentals of Server Site Programming.
CO4 Develop modern interactive web applications using PHP, XML and MySQL
Textbooks & References:
1. Burdman, Jessica, “Collaborative Web Development” Addison Wesley
2. Xavier, C, “ Web Technology and Design” , New Age International
3. Ivan Bayross,” HTML, DHTML, Java Script, Perl & CGI”, BPB Publication
4. Bhave, “Programming with Java”, Pearson Education
 Department Computer Science and Engineering
Institute of Engineering and Technology, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur

5. Herbert Schieldt, “The Complete Reference:Java”, TMH.

6. Hans Bergsten, “Java Server Pages”, SPD O’Reilly 6. Ullman, “PHP for the Web: Visual QuickStart
Guide”, Pearson Education
7. Margaret Levine Young, “The Complete Reference Internet”, TMH 8. Naughton, Schildt, “The
Complete Reference JAVA2”, TMH 9. Balagurusamy E, “Programming in JAVA”, TMH
8. Ramesh Bangia, “Internet and Web Design” , New Age International
9. Ivan Bayross,” HTML, DHTML, Java Script, Perl & CGI”, BPB Publication
10. Deitel, “Java for programmers”, Pearson Education
11. Chris Bates, “Web Programing Building Internet Applications”, 2nd Edition, WILEY, Dreamtech
12. Joel Sklar , “Principal of web Design” Vikash and Thomas Learning 6. Horstmann, “CoreJava”,
Addison Wesley