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Syllabus of B.tech. VIII SEM AIML (RGPV)

Syllabus of B. Tech. VIII Sem AIML (RGPV)

 

Syllabus of AL-801 Business Intelligence

AL801 Business Intelligence.pdf

Source: (rgpv.ac.in)

UNIT-1 : BUSINESS INTELLIGENCE

  • Effective and timely decisions 
  • Data, information and knowledge 
  • Role of mathematical models 
  • Business intelligence architectures
  • Cycle of a business intelligence analysis 
  • Enabling factors in business intelligence
    projects 
  • Development of a business intelligence system 
  • Ethics and business intelligence

UNIT-2 : KNOWLEDGE DELIVERY

  • The business intelligence user types,
  • Standard reports, Interactive Analysis and Ad Hoc Querying,
  • Parameterized Reports and Self-Service Reporting,
  • Dimensional analysis, Alerts/Notifications, Visualization:
  • Charts, Graphs, Widgets, Scorecards and Dashboards,
  • Geographic Visualization, Integrated Analytics,
  • Considerations: Optimizing the Presentation for the Right Message

UNIT-3 : EFFICIENCY

  • Efficiency measures – The CCR model:
  • Definition of target objectives- Peer groups – Identification of good
  • operating practices; cross efficiency analysis –
  • virtual inputs and outputs –Other models.
  • Pattern matching – cluster analysis, outlier analysis

UNIT-4 : BUSINESS INTELLIGENCE APPLICATIONS

  • Marketing models 
  • Logistic and Production models – Case studies.

UNIT-5 : FUTURE OF BUSINESS INTELLIGENCE

  • Future of business intelligence – Emerging Technologies,
  • Machine Learning, Predicting the Future,
  • BI Search & Text Analytics 
  • Advanced Visualization – Rich Report,
  • Future beyond Technology

== END OF UNITS==

 

Syllabus of AL-802(A) Block Chain Technologies (Departmental Elective)

AL 802(A) Block Chain Technologies.pdf

Source: (rgpv.ac.in)

UNIT-1 : Introduction

  • Overview of Block chain, Public Ledgers, Bitcoin, Smart Contracts, Block in a Block chain,
  • Transactions, Distributed Consensus,
  • Public vs Private Block chain, Understanding Cryptocurrency to Block chain,
  • Permissioned Model of Block chain,
  • Overview of Security aspects of Block chain;
  • Basic Crypto Primitives:
  • Cryptographic Hash Function, Properties of a hash function,
  • Hash pointer and Merkle tree, Digital
    Signature,
  • Public Key Cryptography, A basic

UNIT-2 : Understanding Block chain with Crypto currency

  • Bitcoin and Block chain: Creation of coins,
    Payments and double spending,
  • Bitcoin Scripts, Bitcoin P2P Network, Transaction in Bitcoin Network,
  • Block Mining, Block propagation and block relay.
  • Working with Consensus in Bitcoin: Distributed consensus in open environments,
  • Consensus in a Bitcoin network, Proof of Work (PoW) – basic introduction, Hash Cash PoW, Bitcoin PoW,
  • Attacks on PoW and the monopoly problem,
  • Proof of Stake, Proof of Burn and Proof of Elapsed
    Time,
  • 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 machine replication,
  • Overview of Consensus models for permissioned block chain- Distributed consensus in closed environment, Paxos, RAFT Consensus,
  • Byzantine general problem, Byzantine fault tolerant system,
  • Lamport-Shostak-Pease BFT Algorithm, BFT over Asynchronous 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,
  • Identity on Block chain

UNIT-5 : Block chain application development

  • Hyperledger Fabric- Architecture, Identities andPolicies,
  • Membership and Access Control, Channels, Transaction Validation,
  • Writing smart contract using Hyperledger Fabric,
  • Writing smart contract using Ethereum,
  • Overview of Ripple and Corda

== END OF UNITS==

 

Syllabus of AL-802 (B) High Performance computing (Departmental Elective)

AL802 (B) High Performance computing.pdf

Source: (rgpv.ac.in)

UNIT-1 : Introduction to modern processors

  • General Purpose cache based architecture-performance metric and bench marks,
  • Moors Law, pipelining, super clarity, SIMD.
  • Memory Hierarchies, Multi core processors,
  • Multi-threaded processors, Vector processors- Design principle,
  • Max performance estimates, programming
    for vector architecture.
  • Basic Optimizations for serial codes:- Scalar profiling, common sense optimizations,
  • Simple measures and their impacts, role of compilers, C++ optimizations.

UNIT-2 : Data access optimizations

  • balance analysis and light speed estimates,
  • storage order, Algorithm
  • classifications and assess optimizations,
  • case studies for data access optimizations.
  • Parallel Computers: Shared memory computers,
  • Distributed memory computers, hybrid systems,
  • Network computers.

UNIT-3 : Basics of parallel computing

  • data and functional parallelism, parallel scalability-
  • laws, metrics, factors, efficiency and load imbalance.
  • Shared memory parallel programming with Open MP: Parallel
  • execution, data scoping, work sharing using loops,
  • synchronization, Reductions,
  • loop scheduling and Tasking.

UNIT-4 : Efficient Open MP Programming

  • Introduction to reinforcement learning(RL)
  • Reinforcement Learning Program profiling,
  • Performance pitfalls, improving the impact of
    open MP work sharing constructs,
  • determining overheads for short loops,
  • Serilisation and false sharing.

UNIT-5 : Distributed Memory parallel programming with MPI

  • Message passing, Message and point to point
    communication,
  • collective communication, non blocking point-to-point communication,
  • virtual topologies.
  • Efficient MPI Programming: MPI performance tools,
  • communication parameters,
  • impact of synchronizations sterilizations and contentions,
  • reductions in communication overhead.

== END OF UNITS==

 

Syllabus of AL-802 (C) Big Data Analytics (Departmental Elective)

AL802 (C) Big Data Analytics.pdf

Source: (rgpv.ac.in)

UNIT-1 : Introduction 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-2 : Introduction to Hadoop

  • 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,
  • Map Reduce programming.

UNIT-3 : Introduction to Hive

  • 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, Data types of Pig.

UNIT-4 : Introduction to NoSQL

  • Introduction to NoSQL, NoSQL Business Drivers,
  • NoSQL Data architectural patterns,
  • Variations of NOSQL architectural patterns
  • using NoSQL to Manage Big Data,
  • Introduction to Mango DB.

UNIT-5 : Mining social Network Graphs

  • Introduction 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.

  •  

== END OF UNITS==

 

Syllabus of AL-802(D) Quantum Computing (Departmental Elective)

AL802(D) Quantum Computing.pdf

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Quantum Computing

  • Motivation for studying Quantum Computing ,
  • Major players in the industry (IBM, Microsoft,
    Rigetti, D-Wave etc.),
  • Origin of Quantum Computing Overview of major concepts in Quantum Computing:
  • Qubits and multi-qubits states, Braket notation,
  • Bloch Sphere representation,
  • Quantum Superposition,
  • Quantum Entanglement

UNIT- 2 : Math Foundation for Quantum Computing:

  • Matrix Algebra: basis vectors and orthogonality,
  • inner product and Hilbert spaces,
  • matrices and tensors, unitary operators and projectors,
  • Dirac notation,
  • Eigen values and Eigen vectors

UNIT-3 : Building Blocks for Quantum Program:

  •  Architecture of a Quantum Computing platform,
  • Details of q-bit system of information representation:
  • Block Sphere, Multi-qubits States, Quantum
    superposition of qubits (valid and invalid superposition),
  • Quantum Entanglement, Useful states from quantum algorithmic perceptive e.g. Bell State,
  • Operation on qubits: Measuring and transforming
    using gates.
  • Quantum Logic gates and Circuit: Pauli, Hadamard,
  • Phase shift, controlled gates, Ising, Deutsch, swap etc.

UNIT-4 : Programming model

  • Programming model for a Quantum Computing Program: Steps performed on classical computer,
  • Steps performed on Quantum Computer, Moving data between bits and qubits.
  • Basic techniques exploited by quantum algorithms,
  • Amplitude amplification, Quantum Fourier Transform,
  • Phase Kick-back, Quantum Phase estimation,
  • Quantum Walks

UNIT-5 : Major Algorithms

  • Major Algorithms: Shor’s Algorithm, Grover’s Algorithm,
  • Deutsch’s Algorithm, Deutsch -Jozsa Algorithm OSS
  • Toolkits for implementing Quantum program: IBM quantum experience,
  • Microsoft Q, RigettiPyQuil (QPU/QVM)

== END OF UNITS==

 

Syllabus of AL-803(A) Introduction to IOT (Open Elective)

AL 803(A) Introduction to IOT.pdf

Source: (rgpv.ac.in)

UNIT-1 :

  • IoT definition, Characteristics, 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.

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 service oriented
    architecture,
  • IoT challenges, 6LowPAN, IEEE 802.15.4,
  • ZigBee and its types, RFID Features,
  • 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 and applications,
  • 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 for IoT, 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 AL-803(B) Bio Informatics (Open Elective)

AL 803(B) Bio Informatics.pdf

Source: (rgpv.ac.in)

UNIT-1 : Introduction

  • Introduction to bioinformatics,
  • objectives of bioinformatics,
  • Basic chemistry of nucleic acids, structure of DNA & RNA, Genes,
  • structure of bacterial chromosome,
  • cloning methodology,
  • Data maintenance and Integrity Tasks.

UNIT-2 : Bioinformatics Databases & Image Processing

  • Types of databases, Nucleotide sequence databases,
  • Protein sequence databases, Protein structure databases, Normalization,
  • Data cleaning and transformation,
  • Protein folding, protein function,
  • protein purification and characterization,
  • Introduction to Java clients, CORBA,
  • Using MYSQL, Feature Extraction.

UNIT-3 : Sequence Alignment and database searching

  • Introduction to sequence analysis,
  • Models for sequence analysis,
  • Methods of optimal alignment,
  • Tools for sequence alignment,
  • Dynamics Programming,
  • Heuristic Methods,
  • Multiple sequences Alignment

UNIT-4 : Gene Finding and Expression

  • Cracking the Genome, Biological decoder ring,
  • finding genes through mathematics & learning,
  • Genes prediction tools,
  • Gene Mapping, Application of Mapping,
  • Modes of Gene Expression data,
  • mining the Gene Expression Data.

UNIT-5 : Proteomics & Problem solving in Bioinformatics

  • Proteome analysis, tools for proteome analysis,
  • Genetic networks, Network properties and analysis,
  • complete pathway simulation: E-cell,
  • Genomic analysis for DNA & Protein sequences,
  • Strategies and options for similarity search,
  • flowcharts for protein structure prediction

== END OF UNITS==

 

Syllabus of AL-803(C) Managing Innovation and Entrepreneurship (Open Elective)

AL 803(c ) Managing Innovation and Entrepreneurship.pdf

Source: (rgpv.ac.in)

UNIT-1 :

  • Innovation, the basic definition and classification:
  • The relationship of innovation and entrepreneurship,
  • creation of competitive advantage based on innovation.
  • Innovative models, Product, process,
  • organizational and marketing innovation and
  • their role in business development.

UNIT-2 :

  • Sources of innovation (push, pull, analogies),
  • transfer of technology.
  • Creative methods and approaches used in innovation management.
  • Approaches to management of the innovation process (agile management,
  • Six Thinking Hats, NUF test).

UNIT-3 :

  • Project approach to innovation management,
  • method Stage Gate, its essence,
  • adaptation of access to selected business models.
  • In-house business development of the innovation process in the company.
  • Open Innovation as a modern concept,
  • the limits of this method and its benefits for business development.

UNIT-4 :

  • Innovations aimed at humans,
  • role of co-creation in the innovation process.
  • The strategy of innovation process,
  • types and selection of appropriate strategies.

UNIT-5 :

  • Measurement and evaluation of the benefits of innovation for business (financial and non- financial metrics,
    their combination and choice).
  • Barriers to innovation in business,
  • innovation failure and its causes,
  • postaudits of innovative projects.
  • Organization and facilitation of an innovation workshop.

== END OF UNITS==

 

Syllabus of AL 803-(D) Human Computer Interaction (Open Elective)

AL 803(D ) Human Computer Interaction.pdf

Source: (rgpv.ac.in)

UNIT-1 :

  • Introduction, Human Computer Interaction (HCI) concepts and definitions,
  • Nature of interaction human and Machine,
  • interaction design, understanding and conceptualizing interaction,
  • understanding users, interfaces and interactions,
  • data gathering.

UNIT-2 :

  • Introduction to User Centered System Design (UCSD),
  • Natural computing, user centered
  • system design, core concepts,
  • interactive design and its strength and weakness,
  • types of user model, user model and evaluation,
  • Heuristic evaluation.

UNIT-3 :

  • Psychological user models. Black box models of human performance,
  • including perception, motor control,
  • memory and problem-solving.
  • Quantitative analysis of performance.
  • Human processor, keystroke level model, and
  • GOMS descriptions of user performance

UNIT-4 :

  • Modeling of system understanding.
  • Mental models and metaphor, use of design prototypes,
  • controlled experiments.
  • Cognitive walkthrough.
  • Evaluation from the perspective of a novice learning to use the system.

UNIT-5 :

  • Task analysis and design. Contextual and qualitative studies,
  • use-case driven design.
  • Research techniques.
  • Cognitive dimensions of notations, CSCW,
  • ubiquitous computing,
  • new interaction techniques,
  • programmability.

== END OF UNITS==

==End of Syllabus==