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
- OpenCV Installation and working with Python
- Basic Image Processing loading images Cropping Resizing Thresholding Contour analysis Bolb detection
- Image Annotation – Drawing lines text circle rectangle ellipse on images
- Image Enhancement Understanding Color spaces color space conversion Histogram equialization Convolution Image smoothing Gradients Edge Detection
- Image Features and Image Alignment – Image transforms – Fourier Hough Extract ORB Image features Feature matching and cloning
- Feature matching based image alignment
- Image segmentation using Graphcut / Grabcut
- Camera Calibration with circular grid
- Pose Estimation
- 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==