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

 

​Syllabus of AL-601 Theory of Computation

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction of Automata Theory: Examples of automata machines

• Finite Automata as a language acceptor and translator

• Moore machines and mealy machines composite machine

• Conversion from Mealy to Moore and vice versa

UNIT-2 :

• Types of Finite Automata: Non Deterministic Finite Automata (NDFA)

• Deterministic finite automata machines

• conversion of NDFA to DFA

• minimization of automata machines regular expression

• Arden’s theorem.

• Meaning of union intersection

• concatenation and closure

• 2 way DFA.

UNIT-3 :

• Grammars: Types of grammar context sensitive grammar and context free grammar regular grammar.

• Derivation trees ambiguity in grammar

• simplification of context free grammar

• conversion of grammar to automata machine and vice versa

• Chomsky hierarchy of grammar killing null and unit productions.

• Chomsky normal form and Greibach normal form.

UNIT-4 :

• Push down Automata: example of PDA

• deterministic and non-deterministic PDA

• conversion of PDA into context free grammar and vice versa

• CFG equivalent to PDA Petrinet model.

UNIT-5 :

• Turing Machine: Techniques for construction. Universal Turing machine Multitape multihead and multidimensional Turing machine

• N-P complete problems.

• Decidability and Recursively Enumerable Languages

• decidability decidable languages undecidable languages

• Halting problem of Turing machine & the post correspondence problem

LIST OF EXPERIMENTS :

1. Design a Program for creating machine that accepts three consecutive one.
2. Design a Program for creating machine that accepts the string always ending with 101.
3. Design a Program for Mode 3 Machine
4. Design a program for accepting decimal number divisible by 2.
5. Design a program for creating a machine which accepts string having equal no. of 1’s and 0’s.
6. Design a program for creating a machine which count number of 1’s and 0’s in a given string.
7. Design a Program to find 2’s complement of a given binary number.
8. Design a Program which will increment the given binary number by 1.
9. Design a Program to convert NDFA to DFA.
10. Design a Program to create PDA machine that accept the well-formed parenthesis.
11. Design a PDA to accept WCWR where w is any string and WR is reverse of that string and C is a Special symbol.
12. Design a Turing machine that’s accepts the following language an b n c n where n>0.

== END OF UNITS==

 

Syllabus of AL-602 Computer Networks

Source: (rgpv.ac.in)

UNIT-1 :

• Computer Network: Definitions goals components Architecture Classifications & Types.

• Layered Architecture: Protocol hierarchy

• Design Issues Interfaces and Services

• Connection Oriented & Connection less Services Service primitives

• Design issues & its functionality.

• ISOOSI Reference Model: Principle Model Descriptions of various layers and its comparison with TCP/IP.

• Principals of physical layer: Media Bandwidth Data rate and Modulations.

UNIT-2 :

• Data Link Layer: Need Services Provided Framing

• Flow Control Error control.

• Data Link Layer Protocol: Elementary & Sliding Window protocol: 1-bit Go-Back-N Selective Repeat

• Hybrid ARQ.

• Protocol verification: Finite State Machine Models & Petri net models. ARP/RARP/GARP

UNIT-3 :

• MAC Sub layer: MAC Addressing Binary Exponential Back-off (BEB) Algorithm

• Distributed Random Access Schemes/Contention Schemes: for Data Services (ALOHA and Slotted ALOHA)

• for Local-Area Networks (CSMA CSMA/CD CSMA/CA)

• Collision Free Protocols: Basic Bit Map BRAP Binary Count Down

• MLMA Limited Contention Protocols: Adaptive Tree Walk

• Performance Measuring Metrics.

• IEEE Standards 802 series & their variant

UNIT-4 :

• Network Layer: Need Services Provided Design issues

• Routing algorithms: Least Cost Routing algorithm 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.

UNIT-5 :

• Transport Layer: Design Issues

• UDP: Header Format Per-Segment Checksum Carrying Unicast/Multicast Real-Time Traffic

• TCP: Connection Management Reliability of Data Transfers

• TCP Flow Control TCP Congestion Control

• TCP Header Format TCP Timer Management.

• Application Layer: WWW and HTTP FTP SSH Email (SMTP MIME IMAP) DNS Network Management (SNMP).

LIST OF EXPERIMENTS :

1. Study of Different Type of LAN& Network Equipments.
2. Study and Verification of standard Network topologies i.e. Star Bus Ring etc.
3. LAN installations and Configurations.
4. Write a program to implement various types of error correcting techniques.
5. Write a program to implement various types of farming methods.
6. Study of Tool Command Language (TCL).
7. Study and Installation of Standard Network Simulator: N.S-2 N.S3.OpNetQualNetetc.
8. Study & Installation of ONE (Opportunistic Network Environment) Simulator for High Mobility Networks .
9. Configure 802.11 WLAN.
10. Implement &Simulate various types of routing algorithm.
11. Study & Simulation of MAC Protocols like Aloha CSMA CSMA/CD and CSMA/CA using Standard Network Simulators.
12. Study of Application layer protocols-DNS HTTP HTTPS FTP and TelNet. 

== END OF UNITS==

 

Syllabus of AL-603 (A) Image and Video Processing (Departmental Elective)

Source: (rgpv.ac.in)

MODULE-1 :

• Image representation and analysis

• Introduction to computer Vision

• Numerical representation of images

• Image augmentation enhancement

• processing color transforms geometric transforms

• feature recognition and extraction

MODULE-2 :

• Image Segmentation Object detection

• breaking image into parts

• finding contours and edges of various objects in image

• Background subtraction for video.

MODULE-3 :

• Object Motion and tracking Tracking a single point over time

• motion models to define object movement over time

• analyze videos as sequences of individual image frames

• methods to track a set of features over time

• matching features from image frame to other

• tracking a moving car using optical flow

MODULE-4 :

• Robotic localization

• Bayesian statistics to locate a robot in space

• sensor measurements to safely navigate an environment

• Gaussian uncertainty

• histogram filter for robot localization in python

MODULE-5 :

• Degradation model

• noise models

• estimation of degradation function by modeling

• restoration using Weiner filters and Inverse filters

LIST OF EXPERIMENTS :

1. Various forms of image representation
2. Apply various image segmentation algorithms
3. Apply object motion and tracking
4. Apply object localization
5. Apply image restoration

== END OF UNITS==

 

Syllabus of AL-603 (B) Data and Visual Analytics (Departmental Elective)

Source: (rgpv.ac.in)

UNIT-1 : Data Definitions and Analysis Techniques

• Elements Variables and Data categorization Levels of Measurement Data management and indexing

• Introduction to Statistical Concepts: Sampling Distributions Resampling

• Statistical Inference and Descriptive Statistics

• Measures of central tendency

• Measures of location of dispersions

UNIT-2 : Advance Data analysis techniques

• Statistical hypothesis generation and testing

• Chi-Square test

• t-Test Analysis of variance

• Correlation analysis Maximum likelihood test

• Regression Modelling Multivariate Analysis

• Bayesian Modelling Inference and Bayesian Network

• Regression analysis

UNIT-3 : Data Wrangling

• Intro to Data Wrangling

• Gathering Data

• Assessing Data Cleaning Data.

• Data Visualization in Data Analysis: Design of Visualizations

• Univariate Exploration of Data

• Bivariate Exploration of Data Multivariate Exploration of Data

• Explanatory Visualizations.

UNIT-4 : Data Ecosystem

• Overview of the Data Analyst Ecosystem Types of Data

• Understanding Different Types of File Formats

• Sources of Data Overview of Data Repositories NoSQL

• Data Marts Data Lakes ETL and Data Pipelines

• Foundations of Big Data

• Big Data processing tools such as Hadoop Hadoop Distributed File System (HDFS)

• Hive and Spark

UNIT-5 : Data Visualization tools

• Python visualization libraries (matplotlib pandas seaborn ggplot plotly)

• Introduction to PowerBI tools

• Examples of inspiring (industry) projects-

• Exercise: create your own visualization of a complex dataset.

== END OF UNITS==

 

Syllabus of AL-603 (C) Pattern Recognition (Departmental Elective)

Source: (rgpv.ac.in)

MODULE-1 :

• Introduction and mathematical Preliminaries Principles of pattern recognition: Uses mathematics

• Classification and Bayesian rules

• Clustering vs classification

• Basics of linear algebra and vector spaces

• Eigen values and eigen vectors

• Rank of matrix and SVD

MODULE- 2 :

• Pattern Recognition basics Bayesian decision theory

• Classifiers Discriminant functions

• Decision surfaces Parameter estimation methods

• Hidden Markov models dimension reduction methods

• Fisher discriminant analysis

• Principal component analysis non-parametric techniques for density estimation

• non metric methods for pattern classification

• unsupervised learning

• algorithms for clustering: K means Hierarchical and other methods

MODULE-3 :

• Feature Selection and extraction Problem statement and uses

• Branch and bound algorithm

• Sequential forward and backward selection

• Cauchy Schwartz inequality

• Feature selection criteria function: Probabilistic separability based and Inter class distance based

• Feature Extraction: principles.

MODULE-4 :

• Visual Recognition Human visual recognition system

• Recognition methods: Low-level modelling (e.g. features) Midlevel abstraction (e.g. segmentation) High-level reasoning (e.g. scene understanding);

• Detection/Segmentation methods;

• Context and scenes Importance and saliency

• Large-scale search and recognition

• Egocentric vision systems

• Human-in-the-loop interactive systems

• 3D scene understanding

MODULE-5 :

• Recent advancements in Pattern Recognition Comparison between performance of classifiers

• Basics of statistics covariance and their properties

• Data condensation feature clustering

• Data visualization Probability density estimation

• Visualization and Aggregation

• FCM and soft computing techniques

• Examples of real-life datasets

LIST OF EXPERIMENTS :

1. Data extraction
2. Pre-processing of images
3. Image segmentation
4. Image classification AICTE

== END OF UNITS==

 

Syllabus of AL-604 (A) Cloud Computing (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction of Grid and Cloud computing characteristics components

• business and IT perspective

• cloud services requirements cloud models

• Security in public model public verses private clouds

• Cloud computing platforms: Amazon EC2

• Platform as Service: Google App Engine Microsoft Azure Utility Computing Elastic Computing.

UNIT-2 :

• Cloud services- SAAS PAAS IAAS

• cloud design and implementation using SOA

• conceptual cloud model cloud stack computing on demand

• Information life cycle management cloud analytics

• information security virtual desktop infrastructure storage cloud.

UNIT-3 :

• Virtualization technology: Definition benefits sensor virtualization

• HVM study of hypervisor

• logical partitioning- LPAR Storage virtualization SAN NAS

• cloud server virtualization

• virtualized data center.

UNIT-4 :

• Cloud security fundamentals

• Vulnerability assessment tool for cloud

• Privacy and Security in cloud

• Cloud computing security architecture: Architectural Considerations- General Issues

• Trusted Cloud computing

• Secure Execution Environments and Communications

• Micro architectures;

• Identity Management and Access control-Identity management

• Access control Autonomic Security

• Cloud computing security challenges: Virtualization security management virtual threats

• VM Security Recommendations

• VM-Specific Security techniques

• Secure Execution Environments and Communications in cloud

UNIT-5 :

• SOA and cloud SOA and IAAS

• cloud infrastructure benchmarks OLAP business intelligence

• e-Business ISV

• Cloud performance monitoring commands issues in cloud computing.

• QOS issues in cloud mobile cloud computing

• Inter cloud issues Sky computing

• Cloud Computing Platform Xen Cloud Platform

• Eucalyptus OpenNebula Nimbus TPlatform

• Apache Virtual Computing Lab (VCL)

• Anomaly Elastic Computing Platform.

== END OF UNITS==

 

Syllabus of AL-604 (B) Information Security & Management (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction: Needs for Security;

• Basic security terminologies e.g. threats vulnerability exploit etc.;

• Security principles(CIA) authentication non repudiation;

• security attacks and their classifications;

• Mathematical foundation – Prime Number;

• Modular Arithmetic;

• Fermat’s and Euler’s Theorem;

• The Euclidean Algorithms;

• The Chinese Remainder Theorem;

• Discrete logarithms.

UNIT-2 :

• Symmetric Key Cryptography: Classical cryptography – substitution transposition and their cryptanalysis;

• Symmetric Cryptography Algorithm – DES 3DES AES etc.;

• Modes of operation: ECB CBC etc.;

• Cryptanalysis of Symmetric Key Ciphers: Linear Cryptanalysis Differential Cryptanalysis.

UNIT-3 :

• Asymmetric Key Cryptography: Key Distribution and Management

• Diffie-Hellman Key Exchange algorithm;

• Asymmetric Key Cryptography Algorithm– RSA ECC etc.;

• Various types of attacks on Crypto systems.

UNIT-4 :

• Authentication & Integrity – MAC Hash function SHA MD5 HMAC

• Digital signature and authentication protocols;

• Authorization;

• Access control mechanism;

• X.509 Digital Certificate

UNIT-5 :

• E-mail IP and Web Security: E-mail security – PGP MIME S/MIME;

• IP security protocols;

• Web security – TLS SSL etc.;

• Secure Electronic Transaction(SET);

• Firewall and its types;

• Introduction to IDPS;

• Risk Management;

• Security Planning

== END OF UNITS==

 

Syllabus of AL-604 (C) Intelligent Systems for Robotics (Open Elective)

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction: Introduction to Robotics Fundamentals of Robotics

• Robot Kinematics: Position Analysis

• Dynamic Analysis and Forces

• Robot Programming languages & systems: Introduction the three levels of robot programming

• requirements of a robot programming language

• problems peculiar to robot programming languages.

UNIT-2 :

• Need of AI in Robotics: History state of the art Need for AI in Robotics.

• Thinking and acting humanly

• intelligent agents

• structure of agents.

UNIT-3 :

• Game Playing: AI and game playing

• plausible move generator

• static evaluation move generator

• game playing strategies

• problems in game playing.

UNIT-4 :

• Robotics fundamentals: Robot Classification Robot Specification

• notation kinematic representations and transformations

• dynamics techniques;

• trajectory planning and control.

UNIT-5 :

• Robotics and Its applications: DDD concept Intelligent robots

• Robot anatomy-Definition law of robotics

• History and Terminology of Robotics-Accuracy and repeatability of Robotics-Simple problems-Specifications of Robot-Speed of Robot

• Robot joints and links-Robot classifications Architecture of robotic systems-Robot Drive systems-Hydraulic

• Pneumatic and Electric system

== END OF UNITS==

==End of Syllabus==