CSE 7th Semester Syllabus

Unit 1Overview and Introduction

Overview of software development methodology and software quality model, different models of software development and their issues. · Introduction to software architecture, evolution of software architecture, software components and connectors, common software architecture frameworks, architecture business cycle – architectural patterns – reference model.

Unit 2Architecture Models and Styles

Software architecture models: structural models, framework models, dynamic models, process models. · Architectural styles: dataflow architecture, pipes and filters architecture, call‑and‑return architecture, data‑centered architecture, layered architecture, agent‑based architecture, micro‑services architecture, reactive architecture, representational state transfer (REST) architecture, etc.

Unit 3Implementation Technologies

Software Architecture Description Languages (ADLs), Struts, Hibernate, Node.js, AngularJS, J2EE – JSP, Servlets, EJBs; middleware: JDBC, JNDI, JMS, RMI and CORBA, etc. · Role of UML in software architecture.

Unit 4Analysis, Design, and Evaluation

Requirements for architecture and the life‑cycle view of architecture design and analysis methods. · Architecture‑based economic analysis: Cost Benefit Analysis Method (CBAM), Architecture Tradeoff Analysis Method (ATAM). · Active Reviews for Intermediate Design (ARID), Attribute‑Driven Design (ADD), architecture reuse, domain‑specific software architecture.

Unit 5Documentation

Principles of sound documentation, refinement, context diagrams, variability, software interfaces. · Documenting the behavior of software elements and software systems, documentation package using a seven‑part template.

Unit 1Introduction to Computational Intelligence

Types of Computational Intelligence · Components of Computational Intelligence · Concept of Learning/Training model · Parametric Models · Non‑parametric Models · Multilayer Networks – Feed‑forward network · Multilayer Networks – Feedback network

Unit 2Fuzzy Systems

Fuzzy set theory – fuzzy sets and operations · Membership functions · Fuzzy relations and their composition · Fuzzy measures · Fuzzy logic – fuzzy rules and inferencing · Fuzzy control – selection of membership functions · Fuzzification · Rule‑based design & inferencing · Defuzzification

Unit 3Genetic Algorithms

Basic genetics and concepts · Working principle of genetic algorithms · Creation of offsprings · Encoding schemes · Fitness function design · Selection functions · Genetic operators – reproduction, crossover, mutation · Genetic modeling · Benefits of genetic algorithms

Unit 4Rough Set Theory and Related Models

Introduction to rough set theory · Fundamental concepts of rough sets · Set approximation · Rough membership · Attributes and optimization in rough sets · Hidden Markov Models · Decision tree model

Unit 5Swarm Intelligence

Introduction to swarm intelligence · Ant Colony Optimization · Particle Swarm Optimization · Bee Colony Optimization · Other swarm‑intelligence techniques · Applications of computational intelligence

Unit 1Foundations of Deep Learning

History of Deep Learning · McCulloch‑Pitts neuron · Thresholding Logic · Activation functions · Gradient Descent (GD) · Momentum based GD · Nesterov accelerated GD · Stochastic GD · AdaGrad · RMSProp · Adam · Eigenvalue Decomposition · Recurrent Neural Networks · Backpropagation through time (BPTT) · Vanishing and exploding gradients · Truncated BPTT · Gated Recurrent Unit (GRU) · Long Short‑Term Memory (LSTM) · Encoder‑Decoder models · Attention Mechanism · Attention over images

Unit 2Autoencoders and Regularization

Autoencoders and their relation to PCA · Regularization in autoencoders · Denoising autoencoders · Sparse autoencoders · Contractive autoencoders · Bias–Variance trade‑off · L2 regularization · Early stopping · Dataset augmentation · Parameter sharing and tying · Injecting noise at input · Ensemble methods · Dropout · Batch Normalization · Instance Normalization · Group Normalization

Unit 3Convolutional Architectures and Pre‑training

Greedy layerwise pre‑training · Advanced activation functions · Weight initialization methods · Learning vector representations of words · Convolutional Neural Networks (CNNs) · LeNet · AlexNet · ZF‑Net · VGGNet · GoogLeNet · ResNet · Visualizing CNNs · Guided backpropagation · Deep Dream · Deep Art · Recent trends in deep learning architectures

Unit 4Fundamentals of Reinforcement Learning

Introduction to Reinforcement Learning (RL) · Bandit algorithms – UCB, PAC, Median Elimination · Policy Gradient methods · Full RL & Markov Decision Processes (MDPs) · Bellman optimality principle · Dynamic programming – value iteration, policy iteration · Q‑learning · Temporal‑Difference (TD) methods · Eligibility traces · Function approximation · Least‑squares methods

Unit 5Advanced RL Techniques

Fitted Q · Deep Q‑Learning (DQN) · Advanced Q‑learning algorithms · Learning policies by imitating optimal controllers · Policy Gradient algorithms for full RL · Hierarchical RL · Partially Observable MDPs (POMDPs) · Actor‑Critic methods · Inverse Reinforcement Learning · Maximum entropy deep inverse RL · Generative Adversarial Imitation Learning · Recent trends in RL architectures

Unit 1Review of Traditional Networks

Traditional networks: LAN, MAN, WAN, Intranet, Internet · Interconnectivity devices: bridges, routers · TCP/IP protocol architecture: ARP/RARP, IP addressing, IP datagram format, routing table format, ICMP messages, subnetting, supernetting, CIDR, DNS · Network Address Translation (NAT): private addressing, SNAT, DNAT, NAT firewalls · VLANs: concepts, comparison with LANs, VLAN types, tagging · IPv6: address structure, address space, header format

Unit 2Traditional Routing and Transport

Routing protocols – BGP: hidden networks, autonomous system concept, BGP messages · Interior Gateway Protocols – RIP, OSPF · Transport multiplexing and port concepts · TCP segment format, sockets, synchronization, three‑way handshake, variable window size, flow control, timeout and retransmission algorithms, connection control, silly window syndrome · TCP variants – Tahoe, Reno, SACK · UDP message encapsulation, format, pseudo‑header

Unit 3Wireless LAN, Mobile IP and Ad Hoc Routing

Wireless LAN transmission media and MAC problems (hidden/exposed terminals, near/far terminals) · Infrastructure vs ad‑hoc WLANs · IEEE 802.11 system architecture, protocol architecture, physical layer, spread‑spectrum concepts, MAC management, power management, security mechanisms · Mobile IP: limitations of traditional IP, goals, terminology, agent advertisement/discovery, registration, tunneling techniques · Ad‑hoc network routing vs traditional IP routing, routing protocol categories, examples OLSR, DSDV, DSR, ZRP

Unit 4Mobile Transport Layer and Cellular Networks

Limitations of traditional TCP for mobile environments · Mobile‑specific transport protocols: I‑TCP, S‑TCP, M‑TCP · Cellular network fundamentals and comparison with WLANs · GSM services, system architecture, localization, call handling, handover, roaming

Unit 5Mobile Operating Systems, SDKs and M‑Commerce

Mobile device operating systems: special constraints, requirements, commercial OS examples · Software Development Kits (SDKs): iOS, Android · M‑Commerce structure, advantages and disadvantages, mobile payment systems, security concerns

Unit 1Introduction to Big Data

Introduction to Big Data · Big data characteristics · Types of big data · Traditional versus Big data · Evolution of Big data · Challenges with Big Data · Technologies available for Big Data · Infrastructure for Big data · Use of Data Analytics · Desired properties of Big Data system

Unit 2Hadoop Ecosystem and Processing

Introduction to Hadoop · Core Hadoop components · Hadoop Eco system · Hive Physical Architecture · Hadoop limitations · RDBMS Versus Hadoop · Hadoop Distributed File system · Processing Data with Hadoop · Managing Resources and Application with Hadoop YARN · MapReduce programming

Unit 3Hive and Pig for Data Processing

Introduction to Hive · Hive Architecture · Hive Data types · Hive Query Language · Introduction to Pig · Anatomy of Pig · Pig on Hadoop · Use Case for Pig · ETL Processing · Data types in Pig · Running Pig · Execution model of Pig · Operators, functions, and data types of Pig

Unit 4NoSQL Databases

Introduction to NoSQL · NoSQL Business Drivers · NoSQL Data architectural patterns · Variations of NoSQL architectural patterns using NoSQL to Manage Big Data · Introduction to MangoDB

Unit 5Social Network Graph Mining

Mining social Network Graphs · Applications of social Network mining · Social Networks as a Graph · Types of social Networks · Clustering of social Graphs · Direct Discovery of communities in a social graph · Introduction to recommender system

Unit 1Mathematical Background for Cryptography

Abstract Algebra · Number Theory · Modular Inverse · Extended Euclid Algorithm · Fermat's Little Theorem · Euler Phi‑Function · Euler's theorem · Principles of Cryptography · Classical Cryptosystem · Cryptanalysis on Substitution Cipher (Frequency Analysis) · Play Fair Cipher · Block Cipher · Data Encryption Standard (DES) · Triple DES · Modes of Operation · Stream Cipher

Unit 2Advanced Encryption Standard and Public‑Key Cryptosystem

Advanced Encryption Standard (AES) · Introduction to Public Key Cryptosystem · Discrete Logarithmic Problem · Diffie‑Hellman Key Exchange · Computational & Decisional Diffie‑Hellman Problem · RSA Assumptions & RSA Cryptosystem · RSA Signatures & Schnorr Identification Schemes · Primality Testing · Elliptic Curve over the Reals · Elliptic Curve modulo a Prime · Chinese Remainder Theorem

Unit 3Message Authentication, Digital Signatures, Key Management and Hash Functions

Message Authentication · Digital Signature · Key Management · Key Exchange · Hash Function · Universal Hashing · Cryptographic Hash Function · MD (Message Digest) · Secure Hash Algorithm (SHA) · Digital Signature Standard (DSS) · Cryptanalysis: Time‑Memory Trade‑off Attack · Differential Cryptanalysis · Secure channel and authentication system (Kerberos)

Unit 4Information Security and Network Security

Threats in Networks · Network Security Controls – Architecture · Wireless Security · Honeypots · Traffic Flow Security · Firewalls – Design and Types of Firewalls · Personal Firewalls · Intrusion Detection Systems (IDS) · Email Security – Services and attacks through emails · Privacy‑Authentication of source message · Pretty Good Privacy (PGP) · S‑MIME · IP Security – Overview of IPSec · IPv6 Authentication · Encapsulation Security Payload (ESP) · Internet Key Exchange (IKE) · Web Security – SSL/TLS and basic security protocols · Secure Electronic Transaction (SET) encoding

Unit 5Cryptography and Information Security Tools

Spoofing tools (e.g., Arping, etc.) · Footprinting tools (nslookup, dig, Whois, etc.) · Vulnerability scanning tools (Angry IP, HPing2, IP Scanner, Global Network Inventory Scanner, Net Tools Suite Pack) · NetBIOS enumeration using NetView tool · Steganography tools (STools, Steghide, Steganos, Stegdetect, StegSpy) · Trojan detection tools (Netstat, fPort, TCPView, CurrPorts, Process Viewer) · LAN scanner tools (look@LAN, Wireshark, Tcpdump) · DoS attack understanding tools (Jolt2, Bubonic.c, Land and LaTierra, Targa, Nemesy Blast, Panther2, Crazy Pinger, Some Trouble, UDP Flood, FSMax)

Unit 1Data Warehousing

Introduction · Delivery Process · Data Warehouse Architecture · Data Cleaning · Data Integration and Transformation · Data Reduction · Data Warehouse Schema · Partitioning Strategy · Data Warehouse Implementation · Data Marts · Meta Data · Multidimensional Data Model · Pattern Warehousing

Unit 2OLAP Systems

Basic concepts of OLAP · OLAP queries · Types of OLAP servers · OLAP operations · Data Warehouse Hardware Design · Security · Backup and Recovery

Unit 3Introduction to Data & Data Mining

Data Types · Data Quality · Data Preprocessing · Similarity Measures · Summary Statistics · Data Distributions · Basic Data Mining Tasks · Data Mining vs Knowledge Discovery in Databases · Issues in Data Mining · Fuzzy Sets · Fuzzy Logic

Unit 4Supervised Learning

Classification – Statistical‑based algorithms · Classification – Distance‑based algorithms · Classification – Decision‑tree‑based algorithms · Classification – Neural‑network‑based algorithms · Classification – Rule‑based algorithms · Probabilistic Classifiers

Unit 5Clustering & Association Rule Mining

Hierarchical Clustering Algorithms · Partitional Clustering Algorithms · Clustering large databases – BIRCH · Clustering large databases – DBSCAN · Clustering large databases – CURE · Association Rules – Apriori algorithm · Association Rules – FP‑growth algorithm

Unit 1Fundamentals of Agile Process

Introduction and background · Agile Manifesto and Principles · Stakeholders and Challenges · Overview of Agile Development Models: Scrum, Extreme Programming, Feature Driven Development, Crystal, Kanban, Lean Software Development

Unit 2Agile Projects

Planning for Agile Teams: Scrum Teams, XP Teams, General Agile Teams, Team Distribution · Agile Project Lifecycles: Typical Agile Project Lifecycles, Phase Activities, Product Vision · Release Planning: Creating the Product Backlog, User Stories, Prioritizing and Estimating, Creating the Release Plan · Monitoring and Adapting: Managing Risks and Issues, Retrospectives

Unit 3Introduction to Scrum

Agile Scrum Framework · Scrum Artifacts · Scrum Meetings, Activities and Roles · Scrum Team Simulation · Scrum Planning Principles · Product and Release Planning · Sprinting: Planning, Execution, Review and Retrospective · User story definition and Characteristics · Acceptance tests and Verifying stories · Burn down chart · Daily scrum · Scrum Case Study

Unit 4Introduction to Extreme Programming (XP)

XP Lifecycle · The XP Team · XP Concepts: Refactoring, Technical Debt, Timeboxing, Stories, Velocity · Adopting XP: Pre-requisites, Challenges · Applying XP: Pair Programming, Collaborating, Release, Planning, Development · XP Case Study

Unit 5Agile Software Design and Development

Agile design practices · Role of design Principles · Need and significance of Refactoring · Refactoring Techniques · Continuous Integration · Automated build tools · Version control · Agility and Quality Assurance: Agile Interaction Design · Agile approach to Quality Assurance · Test Driven Development · Pair programming: Issues and Challenges

Unit 1INTRODUCTION TO DISASTERS

Definition of Disaster, Hazard, Vulnerability, Resilience, Risks · Types of disasters: Earthquake, Landslide, Flood, Drought, Fire, etc. · Classification, Causes, and Impacts (social, economic, political, environmental, health, psychosocial) · Differential impacts based on caste, class, gender, age, location, disability · Global trends in disasters: urban disasters, pandemics, complex emergencies, climate change · Dos and Don'ts during various types of disasters

Unit 2APPROACHES TO DISASTER RISK REDUCTION

Disaster cycle phases · Culture of safety, prevention, mitigation, and preparedness · Community‑based Disaster Risk Reduction (DRR) · Structural and non‑structural measures · Roles and responsibilities of community, PRIs/ULBs, States, Centre, and other stakeholders · Institutional processes and frameworks at state and central levels · State Disaster Management Authority (SDMA) · Early warning systems and advisories from appropriate agencies

Unit 3INTER‑RELATIONSHIP BETWEEN DISASTERS AND DEVELOPMENT

Factors affecting vulnerabilities and differential impacts · Impact of development projects (dams, embankments, land‑use changes) · Climate change adaptation and IPCC scenarios in the Indian context · Relevance of indigenous knowledge, appropriate technology, and local resources

Unit 4DISASTER RISK MANAGEMENT IN INDIA

Hazard and vulnerability profile of India · Components of disaster relief: water, food, sanitation, shelter, health, waste management · Institutional arrangements for mitigation, response, and preparedness · Disaster Management Act, related policies, plans, programmes, and legislation · Role of GIS and Information Technology in preparedness, risk assessment, response, and recovery · Disaster damage assessment

Unit 5DISASTER MANAGEMENT: APPLICATIONS AND CASE STUDIES AND FIELD WORKS

Landslide hazard zonation case studies · Earthquake vulnerability assessment of buildings and infrastructure case studies · Drought assessment case studies · Coastal flooding and storm surge assessment case studies · Fluvial and pluvial flooding case studies · Forest fire case studies · Man‑made disaster case studies · Space‑based inputs for disaster mitigation and management · Field works related to disaster management