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Syllabus of B.Tech. VI SEM AIDS (RGPV)

Updated: Oct 15, 2023

Syllabus of B.tech. VI SEM AIDS (RGPV)

Syllabus of B. Tech. VI Sem AIDS (RGPV)

​ Syllabus of AD-601 Deep Learning

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Deep Learning

  • Introduction to Deep Learning

  • Basics: Biological Neuron, Idea of computational units,

  • McCulloch–Pitts Neural Model,

  • Linear Perceptron, Perceptron Learning,

  • Feed Forward and Back Propagation Networks.

UNIT-2 :

  • Feedforward Networks

  • Feedforward Networks : Multilayer Perceptron,

  • Gradient Descent, Backpropagation,

  • Empirical Risk Minimization,

  • regularization,

  • auto encoders.

UNIT-3 :

  • Convolutional Networks

  • Convolutional Networks : The Convolution Operation,

  • Variants of the Basic Convolution Function,

  • Structured Outputs, Efficient Convolution Algorithms,

  • Random or Unsupervised Features,

  • LeNet, AlexNet

UNIT-4 : Recurrent Neural Networks

  • Recurrent Neural Networks : Bidirectional RNNs, Deep Recurrent Networks Recursive Neural Networks,

  • The Long Short-Term Memory and Other Gated RNNs

UNIT-5 : Deep Generative Models

  • Deep Generative Models : Boltzmann Machines,

  • Restricted Boltzmann Machines,

  • Introduction to MCMC and Gibbs Sampling,

  • Gradient computations in RBMs,

  • Deep Belief Networks,

  • Deep Boltzmann Machines



APPLICATIONS :

  • Image Processing,

  • Speech Recognition,

  • Natural Language Processing

LAB Experiments

  1. Write a Program to implement Linear Perceptron.

  2. Write a Program to implement Multi-Layer Perceptron.

  3. Write a Program to implementAutoencoders.

  4. Write a Program to implement basic Convolutional Neural Network for Image Classification.

  5. Write a Program to implement LeNet for image classification

  6. Write a Program to implement AlexNet for image classification

  7. Write a Program to implement RNN for text classification

  8. Write a Program to implement LSTM for text prediction.

  9. Write a Program to implementBoltzmann Machines for any real world classification problem.

  10. Write a Program to implement restricted Boltzmann Machines for any real world classification problem

== END OF UNITS==


Syllabus of AD-602 Computer Networks

Source: (rgpv.ac.in)

UNIT-1 :

  • Computer Network : Definitions, goals, components,

  • Architecture, Classifications & Types.

  • Layered Architecture : Protocol hierarchy, Design Issues,

  • Interfaces and Services, ConnectionOriented & Connectionless Services,

  • Service primitives,

  • Design issues & its functionality.

  • ISOOSI Reference Model : Principle, Model,

  • Descriptions of various layers and its comparison withTCP/IP.

  • Principals of physical layer: Media,

  • Bandwidth,

  • Data rate and Modulations.


UNIT-2 :

  • Data Link Layer : Need, Services Provided,

  • Framing, Flow Control, Error control.

  • Data LinkLayer Protocol : Elementary &Sliding Window protocol: 1-bit,

  • Go-Back-N, Selective Repeat, H

  • ybrid ARQ. MAC Sub layer : MAC Addressing,

  • Binary Exponential Back-off (BEB) Algorithm,

  • DistributedRandom Access Schemes/Contention Schemes : for Data Services (ALOHA and SlottedALOHA),

  • for Local-Area Networks (CSMA, CSMA/CD, CSMA/CA).

  • IEEE Standards 802 series & their variant.

UNIT-3 :

  • Network Layer : Need, Services Provided,

  • Design issues,

  • Routing algorithms : Least CostRouting algorithm,

  • Dijkstra's algorithm, Bellman-ford algorithm,

  • Hierarchical Routing,Broadcast Routing,

  • Multicast Routing.

  • IP Addresses, Header format, Packet forwarding,

  • Fragmentation and reassembly, ICMP,

  • Comparative study of IPv4 & IPv6.

UNIT-4 :

  • Transport Layer : Design Issues,

  • UDP : Header Format, Per-Segment Checksum,

  • CarryingUnicast/Multicast Real-Time Traffic,

  • TCP : Connection Management,

  • Reliability of DataTransfers,

  • CP Flow Control,

  • TCP Congestion Control,

  • TCP Header Format,

  • TCP TimerManagement.

UNIT-5 :

  • Application Layer : WWW and HTTP, FTP, SSH,

  • Email (SMTP, MIME, IMAP),DNS, Network Management (SNMP).

  • Network Security : Introduction to security,

  • Traditional Ciphers,

  • Modern Ciphers,

  • Message Integrity and Authentication.

List of Experiments


  1. Study of Different Type of LAN& Network Equipment.

  2. Study and Verification of standard Network topologies i.e. Star, Bus, Ring etc.

  3. LAN installations and Configurations.

  4. Write a program to implement various types of error correcting techniques.

  5. Write a program to implement various types of framing methods.

  6. Study of Tool Command Language (TCL).

  7. Study and Installation of Standard Network Simulator: N.S-2, N.S3.OpNet, QualNetetc .

  8. Study & Installation of ONE (Opportunistic Network Environment) Simulator for High Mobility Networks.

  9. Configure 802.11 WLAN.

  10. Implement &Simulate various types of routing algorithm.

  11. Study & Simulation of MAC Protocols like Aloha, CSMA, CSMA/CD and CSMA/CA usingStandard Network Simulators.

  12. Study of Application layer protocols-DNS, HTTP, HTTPS, FTP and TelNet.

== END OF UNITS==


Syllabus of AD -603 (A) Data Mining and Warehousing

(Departmental Elective –)

Source: (rgpv.ac.in)

UNIT-1 :

  • Data Warehousing : Introduction, Delivery Process,

  • Data warehouse Architecture,

  • DataPre-processing : Data cleaning, Data Integration and transformation,

  • Data reduction.

  • Data warehouseDesign : Data warehouse schema,

  • Partitioning strategy Data warehouse Implementation,

  • Data Marts,Meta Data,

  • Example of a Multidimensional Data model.

  • Introduction to Pattern Warehousing.

UNIT-2 :

  • OLAP Systems : Basic concepts, OLAP queries,

  • Types of OLAP servers,

  • OLAP operations etc.

  • Data Warehouse Hardware and Operational Design : Security, Backup And Recovery.

UNIT-3 :

  • Introduction to Data& Data Mining : Data Types, Quality of data,

  • Data Preprocessing, Similaritymeasures,

  • Summary statistics, Data distributions,

  • Basic data mining tasks,

  • Data Mining V/sknowledge discovery in databases.

  • Issues in Data mining.

  • Introduction to Fuzzy sets and fuzzylogic.

UNIT-4 :

  • Supervised Learning : Classification: Statistical-based algorithms,

  • Distance-based algorithms,

  • Decision tree-based algorithms,

  • Neural network-based algorithms,

  • Rule-based algorithms,

  • Probabilistic Classifiers

UNIT-5 :

  • Clustering & Association Rule mining : Hierarchical algorithms,

  • Partitional algorithms,

  • Clusteringlarge databases – BIRCH, DBSCAN,

  • CURE algorithms.

  • Associationrules : Parallel and distributedalgorithms such as Apriori and FP growth algorithms.


== END OF UNITS==


Syllabus of AD-603 (B) Digital Image Processing

(Departmental Elective )

Source: (rgpv.ac.in)

UNIT-1 :

  • Digital Image fundamentals,

  • A simple image model,

  • Sampling and Quantization.

  • Relationship between pixels.

  • Imaging geometry.

  • Image acquisition systems,

  • Different types of digital images

UNIT-2 :

  • Image transformations,

  • Introduction to Fourier transforms,

  • Discrete Fourier transforms,

  • Fast Fourier transform,

  • Walsh transformation,

  • Hadmord transformation,

  • Discrete Cosine Transformation.

UNIT-3 :

  • Image enhancement,

  • Filters in spatial and frequency domains,

  • Histogram based processing.

  • Image subtraction, Averaging,

  • Image smoothing,

  • Nedion filtering,

  • Low pass filtering,

  • Image sharpening by High pass filtering.

UNIT-4 :

  • Image encoding and segmentation,

  • Encoding : Mapping, Quantizer, Coder.

  • Error free compression,

  • Lossy Compression schemes.

  • JPEG Compression standard.

  • Detection of discontinuation by point detection,

  • Line detection, edge detection,

  • Edge linking and boundary detection,

  • Local analysis,

  • Global processing via Hough transforms and graph theoretic techniques

UNIT-5 :

  • Mathematical morphology- Binary,

  • Dilation, crosses, Opening and closing,

  • Simple methods of representation,

  • Signatures, Boundary segments, Skeleton of a region,

  • Polynomial approximation,

  • Recent advancement in DIP,

  • Machine learning for image processing application


== END OF UNITS==


Syllabus of AD-603 (C) Information Retrieval

(Departmental Elective)


Source: (rgpv.ac.in)

UNIT-1 :

  • Introduction - History of IR- Components of IR -

  • Issues -

  • Open source Search engine.

  • Frameworks -

  • The Impact of the web on IR -

  • The role of artificial intelligence (AI) in IR –

  • IR Versus Web Search -

  • Components of a search engine,

  • characterizing the web.

UNIT- 2 :

  • Boolean and Vector space retrieval models-

  • Term weighting -

  • TF-IDF weightingcosinesimilarity -

  • Pre-processing -

  • Inverted indices -

  • efficient processing with sparse vectors LanguageModel based IR -

  • Probabilistic IR -

  • Latent Semantic indexing -

  • Relevance feedback and queryexpansion.

UNIT-3 :

  • Web search overview,

  • web structure the user paid placement search engine optimization,

  • Web Search Architectures -

  • crawling - meta-crawlers,

  • Focused Crawling -

  • web indexes -

  • Near duplicate detection -

  • Index Compression -

  • XML retrieval.

UNIT-4 :

  • Link Analysis -hubs and authorities -

  • Page Rank and HITS algorithms -

  • Searching and Ranking Relevance Scoring and ranking for Web -

  • Similarity -

  • Hadoop & Map Reduce -

  • Evaluation -

  • Personalized search -

  • Collaborative filtering and content-based recommendation of documents And products - handling invisible Web -

  • Snippet generation,

  • Summarization.

  • QuestionAnswering,

  • Cross-Lingual Retrieval.

UNIT-5 :

  • Information filtering : organization and relevance feedback -

  • Text Mining-

  • Text classification andclustering -

  • Categorization algorithms,

  • naive Bayes,

  • decision trees and nearest neighbor -

  • Clustering algorithms : agglomerative clustering,

  • k-means,

  • expectation maximization (EM).

== END OF UNITS==


Syllabus of AD-604 (A) Internet of Things

(Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

  • IoT definition, Characteristics,

  • IoT conceptual and architectural framework,

  • Componentsof IoT ecosystems,

  • Physical and logical design of IoT,

  • IoT enablers, Modern day IoTapplications,

  • M2M communications, IoT vs M2M, IoT vs WoT,

  • IoT reference architecture,

  • IoTNetwork configurations,

  • IoT LAN, IoT WAN, IoT Node,

  • IoT Gateway, IoT Proxy,

  • Review ofBasic Microcontrollers and interfacing.

UNIT-2 :

  • Define Sensor, Basic components and challenges of a sensor node,

  • Sensor features,Sensor resolution;

  • Sensor classes: Analog, Digital,

  • Scalar, Vector Sensors;

  • Sensor Types, bias,drift,

  • Hysteresis error, quantization error; Actuator;

  • Actuator types : Hydraulic, Pneumatic,electrical,

  • thermal/magnetic,

  • mechanical actuators,

  • soft actuators.

UNIT-3 :

  • Basics of IoT Networking,

  • IoT Components,

  • Functional components of IoT,

  • IoT serviceoriented architecture,

  • IoT challenges, 6LowPAN,

  • IEEE 802.15.4, ZigBee and its types,

  • RFIDFeatures, RFID working principle and applications,

  • NFC (Near Field communication),Bluetooth,

  • Wireless Sensor Networks and its Applications

UNIT-4 :

  • MQTT, MQTT methods and components,

  • MQTT communication, topics andapplications, SMQTT,

  • CoAP, CoAP message types,

  • CoAP Request-Response model, XMPP,

  • AMQP features and components,

  • AMQP frame types.

UNIT-5 :

  • IoT Platforms, Arduino, Raspberry Pi Board,

  • Other IoT Platforms; Data Analytics forIoT,

  • Cloud for IoT, Cloud storage models & communication APIs,

  • Attacks in IoT system, vulnerability analysis in IoT,

  • IoT case studies : Smart Home, Smart framing etc.


== END OF UNITS==


Syllabus of AD-604 (B) Block Chain Technologies (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

  • Introduction : Overview of Block chain,

  • Public Ledgers, Bit coin,

  • Smart Contracts, Block in a Block chain,

  • Transactions, Distributed Consensus,

  • Public vs Private Block chain,

  • Understanding Crypto currency to Block chain,

  • Permissioned Model of Block chain,

  • Overview of Security aspects of Block chain;

  • Basic Crypto Primitives : Cryptographic HashFunction,

  • Properties of a hash function,

  • Hash pointer and Merkle tree,

  • Digital Signature,Public Key Cryptography,

  • A basic crypto currency

UNIT-2 :

  • Understanding Block chain with Crypto currency : Bit coin and Block chain: Creation ofcoins, Payments and double spending,


  • Bit coin Scripts, Bit coin P2P Network,

  • Transaction inBit coin Network,

  • Block Mining, Block propagation and block relay.

  • Working withConsensus in Bit coin : Distributed consensus in open environments,

  • Consensus in a Bitcoinnetwork,

  • Proof of Work (PoW) – basic introduction,

  • Hash Cash PoW, Bit coin PoW,

  • Attackson PoW and the monopoly problem,

  • Proof of Stake, Proof of Burn and Proof of ElapsedTime,

  • The life of a Bitcoin Miner,

  • Mining Difficulty, Mining Pool.

UNIT-3 :

  • Understanding Block chain for Enterprises : Permissioned Block chain: Permissioned model and use cases,

  • Design issues for Permissioned block chains,

  • Execute contracts, State machinereplication,

  • Overview of Consensus models for permissioned block chain Distribute dconsensus in closed environment, Paxos,

  • RAFT Consensus, Byzantine general problem,

  • Byzantine fault tolerant system,

  • Lamport-Shostak-Pease BFT Algorithm,

  • BFT overAsynchronous systems.

UNIT-4 :

  • Enterprise application of Block chain :

  • Cross border payments,

  • Know Your Customer (KYC),

  • Food Security, Mortgage over Block chain,

  • Block chain enabled Trade,

  • We Trade – Trade Finance Network,

  • Supply Chain Financing, and Identity on Block chain

UNIT-5 :

  • Block chain application development :

  • Hyperledger Fabric- Architecture,

  • Identities andPolicies, Membership and Access Control,

  • Channels, Transaction Validation,

  • Writing smartcontract using Hyperledger Fabric,

  • Writing smart contract using Ethereum,

  • Overview of Ripple and Corda

== END OF UNITS==


Syllabus of AD-604 (C) Compiler Design (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction to compiling & Lexical Analysis

  • Introduction of Compiler,

  • Major data Structure in compiler,

  • types of Compiler, Front-end and Back-endof compiler,

  • Compiler structure : analysis-synthesis model of compilation,

  • various phases of a compiler,

  • Lexical analysis : Input buffering,

  • Specification & Recognition of Tokens,

  • Design of a Lexical AnalyzerGenerator, LEX.

UNIT-2 : Syntax Analysis &Syntax Directed Translation

  • Syntax analysis: CFGs, Top down parsing,

  • Brute force approach, recursive descent parsing,

  • transformation on the grammars,

  • predictive parsing, bottom up parsing,

  • operator precedence parsing,

  • LRparsers (SLR, LALR, LR),Parser generation.

  • Syntax directed definitions : Construction of Syntax trees,

  • Bottom up evaluation of S-attributed definition,

  • Lattribute definition, Top down translation,

  • Bottom Upevaluation of inherited attributes Recursive Evaluation,

  • Analysis of Syntax directed definition

UNIT-3 : Type Checking & Run Time Environment

  • Type checking : type system, specification of simple type checker,

  • equivalence of expression,

  • types, typeconversion,

  • overloading of functions and operations,

  • polymorphic functions.

  • Run time Environment : storage organization,

  • Storage allocation strategies,

  • parameter passing, dynamic storage allocation,

  • Symbol table, Error Detection & Recovery,

  • Ad-Hoc and Systematic Methods.

UNIT-4 : Code Generation

  • Intermediate code generation : Declarations, Assignment statements,

  • Boolean expressions, Casestatements,

  • Back patching,

  • Procedure calls Code Generation : Issues in the design of code generator,

  • Basicblock and flow graphs,

  • Register allocation and assignment,

  • DAG representation of basic blocks,

  • peepholeoptimization,

  • generating code from DAG.

UNIT-5 : Code Optimization

  • Introduction to Code optimization: sources of optimization of basic blocks,

  • loops in flow graphs, deadcode elimination,

  • loop optimization, Introduction to global data flow analysis,

  • Code Improvingtransformations,

  • Data flow analysis of structure flow graph Symbolic debugging of optimized code.

== END OF UNITS==


==End of Syllabus==



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