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

Syllabus of AL-801 Business Intelligence

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)

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)

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)

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)

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)

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)

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)

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)

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==