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

• AD-701 AI for Computer Vision

• AD-702(A) Cloud Computing (Departmental Elective)

• AD-702(B) Business Intelligence (Departmental Elective)

• AD-702(C) Computational Intelligence (Departmental Elective)

• AD-702(D) Predictive Analytics (Departmental Elective)

• AD-703(A) Data Visualization (Open Elective)

• AD-703(B) Mobile Application Development (Open Elective)

• AD-703(C) Advanced Statistical Analytics (Open Elective)

• AD-703(D) Social Media & Web Analytics (Open Elective)

​Syllabus of AD-701 AI for Computer Vision

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Image Formation and Processing

• Computer Vision Geometric primitives and transformations

• Photometric image formation

• digital camera Point operators

• Linear filtering More neighborhood operators

• Fourier transforms Pyramids and wavelets

• Geometric transformations

• Global optimization

UNIT-2 : Feature Detection Matching and Segmentation

• Points and patches Edges

• Lines Segmentation Active contours

• Split and merge Mean shift and mode finding

• Normalized cuts

• Graph cuts and energy-based methods.

UNIT-3 : Feature-based Alignment & Motion Estimation

• 2D and 3D feature-based alignment

• Pose estimation Geometric intrinsic calibration

• Triangulation Two-frame structure from motion

• Factorization Bundle adjustment

• Constrained structure and motion

• Translational alignment Parametric motion

• Spline-based motion

• Optical flow Layered motion.

UNIT-4 : 3D Reconstruction

• Shape from X Active range finding

• Surface representations

• Point-based representations Volumetric representations

• Model-based reconstruction

• Recovering texture maps and albedos.

UNIT-5 : Image-based Rendering and Recognition

• View interpolation Layered depth images

• Light fields and Lumigraphs

• Environment mattes Video-based rendering

• Object detection Face recognition

• Instance recognition Category recognition

• Context and scene understanding

• Recognition databases and test sets.

LABORATORY EXPERIMENTS

1. OpenCV Installation and working with Python
2. Basic Image Processing loading images Cropping Resizing Thresholding Contour analysis Bolb detection
3. Image Annotation – Drawing lines text circle rectangle ellipse on images
4. Image Enhancement Understanding Color spaces color space conversion Histogram equialization Convolution Image smoothing Gradients Edge Detection
5. Image Features and Image Alignment – Image transforms – Fourier Hough Extract ORB Image features Feature matching and cloning
6. Feature matching based image alignment
7. Image segmentation using Graphcut / Grabcut
8. Camera Calibration with circular grid
9. Pose Estimation
10. 3D Reconstruction – Creating Depth map from stereo images

== END OF UNITS==

Syllabus of AD-702(A) Cloud Computing (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction To Cloud Computing : Definition Characteristics

• Components Cloud Architecture : Software as a Service Plat form as a Service

• Infrastructure as Service.

• Cloud deployment model : Public clouds–Private clouds–Community clouds-Hybrid clouds Advantages of Cloud computing.

• Comparing cloud providers with traditional IT service providers.

UNIT-2 :

• Services Virtualization Technology and Study of Hypervisor : Utility Computing Elastic computing & grid computing.

• Study of Hypervisor Virtualization applications in enterprises

• High-performance computing

• Pitfalls of virtualization Multitenant software : Multi entity support Multi schema approach.

UNIT-3 :

• Installing cloud platforms and performance evaluation : Organizational scenarios of clouds Administering & Monitoring cloud services

• load balancing Resource optimization

• Resource dynamic reconfiguration

• implementing real time application

• Mobile Cloud Computing and edge computing.

UNIT-4 :

• Cloud security fundamentals & Issues in cloud computing : Secure Execution Environments and Communications in cloud

• General Issues and Challenges while migrating toCloud.

• The Seven-step model of migration into a cloud

• Vulnerability assessment tool for cloud

• Trusted Cloud computing

• Virtualization security management-virtual threats

• VM Security Recommendations and VM-Specific Security techniques.

• QOS Issues in Cloud Depend ability

• data migration

• challenges and risks in cloud adoption.

UNIT-5 :

• Case Study on Open Source and Commercial Clouds : Open Stack Eucalyptus Open Nebula

• Apache Cloud Stack

• Amazon (AWS)

• Microsoft Azure Google cloud etc.

== END OF UNITS==

Syllabus of AD-702(B) Business Intelligence (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Business Intelligence

• Business Intelligence (BI)

• Scope of BI solutions and their fitting into existing infrastructure

• BI Components Future of Business Intelligence

• Functional areas and description of BI tools

• Data mining & warehouse OLAP

• Drawing insights from data : DIKW pyramid Business Analytics project methodology – detailed description of each phase.

UNIT-2 : Business Intelligence Implementation

• Key Drivers Key Performance Indicators and Performance Metrics

• BI Architecture/Framework Best Practices

• Business Decision Making

• Styles of BI-vent Driven alerts – A cyclic process of Intelligence Creation

• Ethics of Business Intelligence.

UNIT-3 : Decision Support System

• Representation of decision-making system

• evolution of information system

• definition and development of decision support system

• Decision Taxonomy Principles of Decision Management Systems.

UNIT-4 : Analysis & Visualization

• Definition and applications of data mining

• data mining process analysis methodologies

• ypical pre-processing operations : combining values into one

• handling incomplete or incorrect data

• handling missing values recoding values

• sub setting sorting transforming scale

• determining percentiles data manipulation

• removing noise removing inconsistencies

• transformations standardizing normalizing

• min-max normalization z-score.

• standardization rules of standardizing data.

• Role of visualization in analytics

• different techniques for visualizing data.

UNIT-5 : Business Intelligence Applications

• Marketing models : Relational marketing

• Salesforce management

• Business case studies

• supply chain optimization

• optimization models for logistics planning

• revenue management system.

== END OF UNITS==

Syllabus of AD-702(C) Computational Intelligence (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Computational Intelligence

• Types of Computational Intelligence

• components of Computational Intelligence.

• Concept of Learning Training model.

• Parametric Models Nonparametric Models.

• Multilayer Networks : Feed Forward network

• Feedback network.

UNIT-2 : Fuzzy Systems

• Fuzzy set theory: Fuzzy sets and operations

• Membership Functions

• Concept of Fuzzy relations and their composition

• Concept of Fuzzy Measures;

• Fuzzy Logic : Fuzzy Rules Inferencing;

• Fuzzy Control – Selection of Membership Functions

• Fuzzyfication

• Rule Based Design & Inferencing

• Defuzzyfication.

UNIT-3 : Genetic Algorithms

• Basic Genetics Concepts

• Working Principle Creation of Offspring

• Encoding Fitness Function

• Selection Functions

• Genetic Operators-Reproduction

• Crossover Mutation;

• Genetic Modelling Benefits.

UNIT-4 : Rough Set Theory

• Introduction Fundamental Concepts

• Set approximation Rough membership

• Attributes Optimization.

• Hidden Markov Models

• Decision tree model.

UNIT-5 : Introduction to Swarm Intelligence

• Swarm Intelligence Techniques : Ant Colony Optimization

• Particle Swarm Optimization

• Bee Colony Optimization etc.

• Applications of Computational Intelligence.

== END OF UNITS==

Syllabus of AD-702(D) Predictive Analytics (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction and Understanding Data

• Introduction to predictive analytics –

• Business analytics: types applications-

• Models : predictive models –

• descriptive models –

• decision models –

• applications –

• analytical techniques.

• Data types and associated techniques –

• complexities of data –

• data preparation

• preprocessing –

• exploratory data analysis

UNIT- 2 : Principles and Techniques Predictive modeling

• Propensity models cluster models

• collaborative filtering

• applications and limitations –

• Statistical analysis : Univariate and Multivariate Statistical analysis.

• Model Selection – Preparing to model the data: supervised versus unsupervised methods statistical and data mining methodology

• cross-validation overfitting

• bias-variance trade-off

• balancing the training dataset establishing baseline performance.

UNIT-3 : Regression and Classification Models

• Measuring Performance in Regression Models –

• Linear Regression and Its Cousins –

• Non- Linear Regression Models –

• Regression Trees and Rule-Based Models Case Study:

• Compressive Strength of Concrete Mixtures.

• Measuring Performance in Classification Models – Discriminant Analysis and Other Linear Classification Models –

• Non-Linear Classification Models –

• Classification Trees and Rule-Based Models –

• Model Evaluation Techniques

UNIT-4 : Time Series Analysis

• Time Series Analysis : Introduction Examples of time series

• Stationary models and autocorrelation function

• Estimation and elimination of trend and seasonal components

• Stationary Process and ARMA Models —

• Basic properties and linear processes

• Introduction to ARMA models

• properties of sample mean and autocorrelation

• function Forecasting stationary time series

• ARMA(p q) processes ACF and PACF

• Modeling and Forecasting with ARMA.

UNIT-5 : Nonstationary and Seasonal Time Series Models–

• ARIMA models Identification techniques

• Unit roots in time series

• Forecasting ARIMA models

• Seasonal ARIMA models Regression with ARMA errors.

• Multivariate Time Series analysis

• State-Space Models

• Deep Learning techniques of time series forecasting

== END OF UNITS==

Syllabus of AD-703(A) Data Visualization (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : :Introduction to Data Visualization

• Overview of data visualization

• Definition Significance in AI and Data Science

• Principal of Data Visualization Methodology

• Applications

• Data pre-processing for visualization : Extraction Cleaning

• Transformation Aggregation

• Data Integration

• Data Reduction.

UNIT-2 : Data Visualization Techniques

• Data Visualization Techniques–

• Pixel-Oriented Visualization Techniques-

• Geometric Projection Visualization Techniques-

• Icon-Based Visualization Techniques-

• Hierarchical Visualization Techniques

• Visualizing Complex Data and Relations.

• Visualization Techniques

• Scalar and point techniques

• Color maps Contouring Height Plots –

• Vector visualization techniques

• Vector properties Vector Glyphs

• Vector Color Coding Stream Objects.

• Exploratory data analysis (EDA) Techniques

UNIT-3 : Data Visualization Tools

• Basic and advanced charts and graphs : bar charts line charts

• scatter plots histograms and heat maps.

• Geospatial visualization : maps choropleth maps

• geospatial heat maps

• Network visualization : node-link diagrams

• force-directed graphs

• Interactive visualization : interactivity and user engagement techniques

• Introduction to programming libraries for data visualization : Matplotlib Seaborn Plotly.

• Introduction to data visualization tools-

• Tableau Visualization using R.

UNIT-4 : Visualizing Multidimensional Data

• Multivariate visualization techniques : parallel coordinates

• scatter plot matrices

• Dimensionality reduction techniques : PCA (Principal Component Analysis)

• t-SNE (tDistributed Stochastic Neighbour Embedding)

• Clustering and classification visualization : dendrograms decision trees

• confusion matrices

• Visualizing high-dimensional data : glyphbased visualization

• parallel coordinates dimension stacking.

UNIT-5 : Advancements in Data Visualization

• Time- Series data visualization

• Big data visualization

• Text data visualization Multivariate data visualization.

• Storytelling with data

• Dashboard creation

• Ethical considerations in data visualization

• Case Studies for Finance-marketing

• and insurance healthcare.

== END OF UNITS==

Syllabus of AD-703(B) Mobile Application Development (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction to Android :

• The Android Platform

• Android SDK

• Eclipse Installation Android Installation

• Building you First Android application

• Understanding Anatomy of Android Application

• Android Manifest file.

UNIT-2 : Android Application Design Essentials

• Anatomy of an Android applications

• Android terminologies

• Application Context Activities

• Services Intents Receiving and Broadcasting Intents

• Android Manifest File and its common settings

• Using Intent Filter Permissions.

UNIT-3 : Android User Interface Design Essentials:

• User Interface Screen elements

• Designing User Interfaces with Layouts

• Drawing and Working with Animation

UNIT-4 : Testing Android applications

• Publishing Android application

• Using Android preferences

• Managing Application resources in a hierarchy

• working with different types of resources.

UNIT-5 : Using Common Android APIs:

• Using Android Data and Storage APIs

• Managing data using SQLite

• Sharing Data between Applications with Content Providers

• Using Android Networking APIs

• Using Android Web APIs

• Using Android Telephony APIs

• Deploying Android Application to the World.

== END OF UNITS==

Syllabus of AD-703(C) Advanced Statistical Analytics (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : Introduction

• Population and Sample

• Random Sampling from finite population (SRSWR and SRSWOR)

• Parameter and Statistic

• Sampling distribution of as tatistic in the context of a finite population

• Sampling distribution of sample mean and sample proportion while sampling from a finite population.

• Random sampling from an infinite population

• Sampling Distribution of sample mean and sample variance when the sample is drawn from a Normal distribution

• Problems on sampling distributions of statistics from finite and infinite populations. Statement of Lyndeberg-Levy Central Limit Theorem (CLT) and its applications.

UNIT-2 : Correlation Regression Analysis and ANOVA

• Correlation Scatter diagram

• Karl Pearson’s coefficient of correlation

• Spearman’s Rank correlation coefficient

• Methods of least square

• Simple linear Regression model

• SLR assumptions and prediction Multiple linear Regression

• MLR assumption and prediction

• Polynomial Regression

• Logistics Regression

• Poisson Regression

• Non-Linear Regression Analysis of Variance (One way & Two Way). Analysis of Covariance

• Multivariate Analysis of Variance

UNIT-3 : Testing of Hypothesis

• Testing of Hypotheses : Null and Alternative Hypothesis

• Testing Procedure (Critical region)

• Type I and Type II errors

• Level of significance & Power of a test

• p-value for symmetric null distribution.

• Tests for me an and proportion (single sample two sample; exact & large sample) Tests for variance (single sample and two samples)

• Tests for me an and correlation coefficient for paired sample (Exact & Large sample) Analysis of Variance (one way).

UNIT-4 : Parametric Point Estimation

• Problem of point estimation

• Criteria of a good Estimator

• Unbiasedness Consistency

• Efficiency Sufficiency Minimum Variance and Unbiasedness (Small sample) Method of moments

• Method of Maximum Likelihood

• Consistency & Efficiency (Large sample)

• Interval Estimation : Confidence Intervals of mean and proportion in large samples.

UNIT-5 : Bayesian Statistics

• Introduction to Bayesian inference

• Bayesian parameter estimation

• Markov Chain Monte Carlo (MCMC) methods

• Bayesian hierarchical models

• Survival analysis

• Causal inference

• High-dimensional data analysis.

== END OF UNITS==

Syllabus of AD-703(D) Social Media & Web Analytics (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 : Social Media & Analytics

• Introduction to Social Media

• Social Media Landscape

• Social Media Analytics & its Need.

• SMA in Small and Large Organisations;

• Application of SMA in Different Social Media Platforms.

• Introduction to Web Analytics : Definition Process

• Key Terms : Site References

• Keywords and Key Phrases;

• Building Block Terms : Visit Characterization Terms

• Content Characterization Terms

• Conversion Metrics;

• Categories : Offsite Web on Site Web;

• Web Analytics Platform

• Web Analytics Evolution

• Need of Web Analytics

• Advantages & Limitations.

UNIT-2 : Network Fundamentals

• The Social Networks Perspective – Nodes

• Ties and Influencers Social Network

• Web Data and Methods.

• Data Collection and Web Analytics Fundamentals : Capturing Data: Web Logs

• Web Beacons Java Script Tags

• Packet Sniffing;

• Outcome Data : E-commerce Lead Generation

• Brand/ Advocacy and Support;

• Competitive Data : Panel Based Measurement

• ISP Based Measurement Search Engine Data;

• Organisational Structure.

• Type and Size of Data

• Identifying Unique page Definition Cookies

• Link Coding Issues

UNIT-3 : Web Metrics & Analytics

• Common Metrics: Hits

• Page Views Visits

• Unique Page Views Bounce

• Bounce Rate & its Improvement

• Average Time on Site Real Time Report

• Traffic Source Report Custom Campaigns

• Content Report Google Analytics;

• Key Performance Indicator : Need Characteristics

• Perspective and Uses.

• Graphs and Matrices- Basic Measures for Individuals and Networks.

• Random Graphs & Network Evolution

• Social Context: Affiliation & Identity

• Web analytics Tools : A/B testing Online Surveys

• Web Crawling and Indexing.

• Natural Language Processing Techniques for Micro-Text Analysis

UNIT-1 : Facebook Analytics

• Introduction Parameters Demographics.

• Analyzing Page Audience : Reach and Engagement Analysis.

• Post-Performance on FB;

• Social Campaigns : Goals and Evaluating Outcomes

• Measuring and Analysing Social Campaigns

• Social Network Analysis AdWords

• Benchmarking Categories of Traffic.

• Google Analytics : Brief Introduction and Working

• Google Website Optimizer

• Implementation Technology

• Limitations

• Performance Concerns

• Privacy Issues.

UNIT-5 : Qualitative Analysis

• Heuristic Evaluations : Conducting a Heuristic Evaluation

• Benefits of Heuristic Evaluations;

• Site Visits : Conducting a Site Visit

• Benefits of Site Visits; Surveys : Website Surveys Post-Visit Surveys

• Creating and Running a Survey

• Benefits of Surveys.

• Web analytics 2.0 : Web Analytics 1.0 & its Limitations

• Introduction to WA 2.0

• Competitive Intelligence Analysis and Data Sources;

• Website Traffic Analysis : Traffic Trends

• Site Overlap and Opportunities.

== END OF UNITS==

==End of Syllabus==