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RGPV B.Tech · 3rd Year

AIDS 6th Semester Syllabus

Artificial Intelligence & Data Science (AD): the official syllabus of all 8 6th sem subjects, unit by unit, with previous year papers, notes and important questions. Free to read.

Subjects

AD 6th semester syllabus, unit by unit

Every subject of RGPV Artificial Intelligence & Data Science 6th semester (3rd year), as the official syllabus lists it.

Deep Learning (DL)

AD-601

  1. Unit 1Introduction to Deep Learning

    Biological Neuron · Idea of computational units · McCulloch–Pitts Neural Model · Linear Perceptron · Perceptron Learning · Feed Forward and Back Propagation Networks

  2. Unit 2Feedforward Networks

    Multilayer Perceptron · Gradient Descent · Backpropagation · Empirical Risk Minimization · Regularization · Autoencoders

  3. Unit 3Convolutional Networks

    Convolution Operation · Variants of the Basic Convolution Function · Structured Outputs · Efficient Convolution Algorithms · Random or Unsupervised Features · LeNet · AlexNet

  4. Unit 4Recurrent Neural Networks

    Bidirectional RNNs · Deep Recurrent Networks · Recursive Neural Networks · Long Short-Term Memory (LSTM) · Other Gated RNNs

  5. Unit 5Deep Generative Models

    Boltzmann Machines · Restricted Boltzmann Machines · MCMC and Gibbs Sampling · Gradient Computations in RBMs · Deep Belief Networks · Deep Boltzmann Machines

Computer Networks (CN)

AD-602

  1. Unit 1Computer Network Fundamentals and Physical Layer

    Definitions, goals, components, Architecture, Classifications & Types · Layered Architecture: Protocol hierarchy, Design Issues, Interfaces and Services, Connection‑Oriented & Connectionless Services, Service primitives, Design issues & its functionality · ISO/OSI Reference Model: Principle, Model, Descriptions of various layers and its comparison with TCP/IP · Principles of Physical Layer: Media, Bandwidth, Data rate and Modulations

  2. Unit 2Data Link Layer and MAC Sub‑layer

    Need, Services Provided, Framing, Flow Control, Error control · Data Link Layer Protocols: Elementary & Sliding Window protocols – 1‑bit, Go‑Back‑N, Selective Repeat, Hybrid ARQ · MAC Sub‑layer: MAC Addressing, Binary Exponential Back‑off (BEB) Algorithm, Distributed Random Access/Contention Schemes – ALOHA, Slotted ALOHA, CSMA, CSMA/CD, CSMA/CA · IEEE 802 series standards and their variants

  3. Unit 3Network Layer and Routing

    Need, Services Provided, Design issues · Routing algorithms: Least Cost Routing, 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

  4. Unit 4Transport Layer

    Design Issues · UDP: Header Format, Per‑Segment Checksum, Carrying Unicast/Multicast Real‑Time Traffic · TCP: Connection Management, Reliability of Data Transfers, Flow Control, Congestion Control, Header Format, Timer Management

  5. Unit 5Application Layer and Network Security

    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

Data Mining and Warehousing (DMW)

AD-603 (A)

  1. 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 · Example of a Multidimensional Data model · Introduction to Pattern Warehousing

  2. Unit 2OLAP Systems

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

  3. Unit 3Introduction to Data & Data Mining

    Data Types · Quality of data · Data Preprocessing · Similarity measures · Summary statistics · Data distributions · Basic data mining tasks · Data Mining vs knowledge discovery in databases · Issues in Data mining · Introduction to Fuzzy sets and fuzzy logic

  4. Unit 4Supervised Learning: Classification

    Statistical-based algorithms · Distance-based algorithms · Decision tree-based algorithms · Neural network-based algorithms · Rule-based algorithms · Probabilistic Classifiers

  5. Unit 5Clustering & Association Rule Mining

    Hierarchical algorithms · Partitional algorithms · Clustering large databases - BIRCH · DBSCAN · CURE algorithms · Parallel and distributed association rule algorithms such as Apriori · FP growth algorithms

Digital Image Processing (DIP)

AD-603 (B)

  1. Unit 1Digital Image Fundamentals

    Digital image fundamentals · Simple image model · Sampling and quantization · Relationship between pixels · Imaging geometry · Image acquisition systems · Different types of digital images

  2. Unit 2Image Transformations

    Image transformations · Fourier transforms · Discrete Fourier transforms · Fast Fourier transform · Walsh transformation · Hadamard transformation · Discrete Cosine Transformation

  3. Unit 3Image Enhancement

    Image enhancement · Filters in spatial domain · Filters in frequency domain · Histogram based processing · Image subtraction · Averaging · Image smoothing · Median filtering · Low pass filtering · Image sharpening by high pass filtering

  4. Unit 4Image Encoding and Segmentation

    Image encoding and segmentation · Encoding: mapping · Quantizer · Coder · Error-free compression · Lossy compression schemes · JPEG compression standard · Detection of discontinuities by point detection · Line detection · Edge detection · Edge linking and boundary detection · Local analysis · Global processing via Hough transforms · Global processing via graph theoretic techniques

  5. Unit 5Mathematical Morphology

    Mathematical morphology · Binary morphology · Dilation · Crosses · Opening and closing · Simple methods of representation · Signatures · Boundary segments · Skeleton of a region · Polynomial approximation · Recent advancements in DIP · Machine learning for image processing applications

Information Retrieval (IR)

AD-603 (C)

  1. Unit 1Introduction

    History of Information Retrieval · Components of Information Retrieval · Issues in Information Retrieval · Open source search engine frameworks · Impact of the web on Information Retrieval · Role of artificial intelligence (AI) in Information Retrieval · IR versus Web Search · Components of a search engine · Characterizing the web

  2. Unit 2Retrieval Models and Weighting

    Boolean retrieval model · Vector space retrieval model · Term weighting · TF‑IDF weighting · Cosine similarity · Pre‑processing techniques · Inverted indices · Efficient processing with sparse vectors · Language‑model based IR · Probabilistic IR · Latent Semantic Indexing (LSI) · Relevance feedback · Query expansion

  3. Unit 3Web Search and Crawling

    Web search overview · Web structure · User‑paid placement and search engine optimization · Web search architectures · Crawling techniques · Meta‑crawlers · Focused crawling · Web indexes · Near‑duplicate detection · Index compression · XML retrieval

  4. Unit 4Link Analysis and Ranking

    Link analysis · Hubs and authorities · PageRank algorithm · HITS algorithm · Searching and ranking · Relevance scoring for the web · Similarity measures · Hadoop & MapReduce for IR · Evaluation of IR systems · Personalized search · Collaborative filtering · Content‑based recommendation · Handling the invisible web · Snippet generation and summarization · Question answering · Cross‑lingual retrieval

  5. Unit 5Information Filtering and Text Mining

    Information filtering · Organization and relevance feedback · Text mining · Text classification · Text clustering · Categorization algorithms (Naïve Bayes, decision trees, nearest neighbor) · Clustering algorithms (agglomerative clustering, k‑means, expectation‑maximisation (EM))

Internet of Things (IT)

AD-604 (A)

  1. Unit 1Internet of Things – Fundamentals

    IoT definition · Characteristics of IoT · IoT conceptual and architectural framework · Components of IoT ecosystems · Physical and logical design of IoT · IoT enablers · Modern day IoT applications · M2M communications · IoT vs M2M · IoT vs WoT · IoT reference architecture · IoT network configurations · IoT LAN · IoT WAN · IoT node · IoT gateway · IoT proxy · Review of basic microcontrollers and interfacing

  2. Unit 2Sensors and Actuators

    Definition of 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 · Actuators · Actuator types: hydraulic, pneumatic, electrical, thermal/magnetic, mechanical, soft actuators

  3. Unit 3IoT Networking Technologies

    Basics of IoT networking · IoT functional components · IoT service‑oriented architecture · IoT challenges · 6LoWPAN · IEEE 802.15.4 · ZigBee and its variants · RFID features and working principle · RFID applications · NFC (Near Field Communication) · Bluetooth · Wireless Sensor Networks and applications

  4. Unit 4IoT Communication Protocols

    MQTT – methods, components, communication, topics and applications · SMQTT · CoAP – message types, request‑response model · XMPP · AMQP – features, components, frame types

  5. Unit 5IoT Platforms, Cloud and Security

    IoT platforms – Arduino, Raspberry Pi, other platforms · Data analytics for IoT · Cloud for IoT · Cloud storage models and communication APIs · Attacks in IoT systems · Vulnerability analysis in IoT · IoT case studies – Smart Home, Smart farming, etc.

Block Chain Technologies (BCT)

AD-604 (B)

  1. Unit 1Introduction

    Overview of Blockchain · Public Ledgers · Bitcoin · Smart Contracts · Block in a Blockchain · Transactions · Distributed Consensus · Public vs Private Blockchain · Understanding Cryptocurrency to Blockchain · Permissioned Model of Blockchain · Overview of Security Aspects of Blockchain · Cryptographic Hash Function · Properties of a Hash Function · Hash Pointer and Merkle Tree · Digital Signature · Public Key Cryptography · Basic Cryptocurrency

  2. Unit 2Understanding Block chain with Crypto currency

    Creation of Coins · Payments and Double Spending · Bitcoin Scripts · Bitcoin P2P Network · Transaction in Bitcoin Network · Block Mining · Block Propagation and Block Relay · Distributed Consensus in Open Environments · Consensus in a Bitcoin Network · Proof of Work (PoW) – Basic Introduction · Hash Cash PoW · Bitcoin PoW · Attack on PoW and Monopoly Problem · Proof of Stake · Proof of Burn · Proof of Elapsed Time · Life of a Bitcoin Miner · Mining Difficulty · Mining Pool

  3. Unit 3Understanding Block chain for Enterprises

    Permissioned Model and Use Cases · Design Issues for Permissioned Blockchains · Execute Contracts · State Machine Replication · Consensus Models for Permissioned Blockchains · Distributed Consensus in Closed Environment · Paxos · RAFT Consensus · Byzantine Generals Problem · Byzantine Fault Tolerant System · Lamport‑Shostak‑Pease BFT Algorithm · BFT over Asynchronous Systems

  4. Unit 4Enterprise application of Block chain

    Cross‑Border Payments · Know Your Customer (KYC) · Food Security · Mortgage over Blockchain · Blockchain‑Enabled Trade · Trade Finance Network (We Trade) · Supply Chain Financing · Identity on Blockchain

  5. Unit 5Block chain application development

    Hyperledger Fabric – Architecture · Hyperledger Fabric – Identities and Policies · Hyperledger Fabric – Membership and Access Control · Hyperledger Fabric – Channels · Hyperledger Fabric – Transaction Validation · Writing Smart Contracts using Hyperledger Fabric · Writing Smart Contracts using Ethereum · Overview of Ripple · Overview of Corda

Compiler Design (CD)

AD-604 (C)

  1. Unit 1Introduction to compiling & Lexical Analysis

    Introduction of Compiler · Major data structures in compiler · Types of compiler · Front-end and back-end of 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 analyzer generator · LEX

  2. Unit 2Syntax 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 · LR parsers (SLR, LALR, LR) · Parser generation · Syntax directed definitions: construction of syntax trees · Bottom-up evaluation of S-attributed definition · L-attributed definition · Top-down translation · Bottom-up evaluation of inherited attributes · Recursive evaluation · Analysis of syntax directed definition

  3. Unit 3Type Checking & Run Time Environment

    Type checking: type system · Specification of simple type checker · Equivalence of expression types · Type conversion · 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

  4. Unit 4Code Generation

    Intermediate code generation: declarations · Assignment statements · Boolean expressions · Case statements · Back patching · Procedure calls · Code generation: issues in the design of code generator · Basic block and flow graphs · Register allocation and assignment · DAG representation of basic blocks · Peephole optimization · Generating code from DAG

  5. Unit 5Code Optimization

    Introduction to code optimization: sources of optimization of basic blocks · Loops in flow graphs · Dead code elimination · Loop optimization · Introduction to global data flow analysis · Code improving transformations · Data flow analysis of structure flow graph · Symbolic debugging of optimized code