Syllabus for

Academic Year  (2024)

Course Objectives

The main objectives of this course are to:

1. Introduce students to the Linux operating system, its command-line interface, and basic commands.

2. Enable students to perform essential system administration tasks such as user management, package installation, and system monitoring.

3. Provide students with the necessary skills to write and execute shell scripts for automation and task automation.

4. Introduce students to basic networking concepts, remote access, and security measures in Linux.

Lab Exercise 1: Introduction to Linux

• Familiarization with Linux environment

• Basic commands: ls, cd, mkdir, rm, cp, mv

• File system hierarchy

Lab Exercise 2: File Manipulation and Permissions

• File permissions and ownership

• File manipulation commands: touch, cat, more, less, nano

• File and directory operations: creating, moving, copying, deleting

Lab Exercise 3: Text Processing Tools

• Introduction to text editors: vi and nano

• Basic text editing commands

• Regular expressions and pattern matching using grep

Lab Exercise 4: User and Group Management

• User account management: useradd, userdel, passwd

• Group management: groupadd, groupdel

• User privileges and sudo

Lab Exercise 5: Process Management

• Process lifecycle

• Process monitoring and management: ps, top, kill, killall

• Job control: bg, fg, jobs

Lab Exercise 6: Package Management

• Package management using apt package manager

• Installing, updating, and removing packages

Lab Exercise 7: Shell Scripting Basics

• Introduction to shell scripting

• Writing and executing simple shell scripts

Lab Exercise 8: Networking and Remote Access

• Network configuration: ifconfig, ping, traceroute

• SSH for remote access and file transfer

Lab Exercise 9: System Administration

• Disk management: fdisk, mkfs, mount, df

• System monitoring and logging: dmesg, syslog

Lab Exercise 10: Security and Firewall Configuration

• Firewall management using iptables

• Basic security measures: securing SSH, setting file permissions

Lab Exercise 11: Virtualization with Linux

• Introduction to virtualization concepts

• Virtualization with VirtualBox or VMware

Lab Exercise 12: Advanced Topics

• Introduction to containers: Docker basics

• Introduction to cloud computing with Linux

Students will be able to build up an environment for developing Android applications, construct user-friendly user interfaces, manage many tasks, develop persistent applications, handle cloud data, test their apps, and release them onto the market with the help of this course.

History of Mobile Apps, Trends in Market - Web App Vs Mobile App - Mobile OS. Introduction to  Android and Kotlin: Kotlin Basics – variables - Functions. First Android App – Setup Android Studio - Deploying the app: Running and Debugging app in Android Emulator.

Kotlin Fundamentals: Classes and Objects - Inheritance. Activity: Introduction to Activity - Activity Lifecycle – Logging. Layouts in Android - Types of Layouts, Multiple activities and Intents.

Input controls:Text Box, Radio Button, Check Box, Command Button. Using Basic Views- Using Image Views to Display Pictures.

Using Picker Views -Using List Views to Display Long Lists - Fragments: Introduction - Lifecycle- Task and Back Stack. Android App Architecture - View Model - Data Binding – Live Data- Transform Live Data. Menus - Types of Menu.

Displaying lists with RecyclerView. Store Data-Room Persistency Library-Asynchronous program-Coroutines-Testing Databases.

  2.     Nagy, Robert. Simplifying Application Development with Kotlin Multiplatform Mobile: Write Robust Native Applications for IOS and Android Efficiently. United Kingdom, Packt Publishing, 2022.

3.     https://developer.android.com

2.      Kotlin for Android App Development.Sommerhoff, P. United Kingdom: Pearson Education 2018.

The main objectives of this course are to

1. introduce students to the concepts of Data Analytics. 

2. Provide students with an understanding of building Big Data.

3. Gaining practical experience in programming tools for data sciences.

4. Empowering students with tools and techniques and benefits to industrial needs

Business Intelligence -  Pattern Recognition -  Data Processing Chain :  Data – Database – Data Warehouse – Data Mining  - Data Visualization – Data Warehousing : - Caselet: University Health System – BI in Healthcare Design Considerations for DW -  DW Development Approaches - DW Architecture -  Data Sources -  Data Loading Processes -  Data Warehouse Design DW Access.

Gathering and selecting data- Data cleansing and preparation -  Outputs of Data Mining -  Evaluating Data Mining  Results  - Data Mining Techniques  - Tools and Platforms for Data Mining -  Data Mining Best Practices  - Excellence in Visualization  - Types of Charts  - Visualization Example.

Decision Tree Problem – Decision Tree Construction – Decision Tree Algorithms – Correlations and Relationship- Regression exercise and analysis – Non –linear regression – Logistic Regression – Pros and Cons – Cluster Analysis : Applications of Cluster Analysis 8 Definition of a Cluster Representing clusters Clustering techniques Clustering Exercise K-Means Algorithm for clustering Selecting the number of clusters Advantages and Disadvantages of K-Means algorithm.

Web content mining Web structure mining Web usage mining Web Mining Algorithms - Text Mining Applications Text Mining Process Term Document Matrix Mining the TDM Comparing Text Mining and Data Mining -  Caselet: WhatsApp and Private Security.

Introduction to Big Data: Types of Digital Data-Characteristics of Data – Evolution of Big Data - Definition of Big Data - Challenges with Big Data - 3Vs of Big Data - Non Definitional traits of Big Data - Business Intelligence vs. Big Data - Big Data Landscape - Business Implications of Big Data - Technology Implications of Big Data - Big Data Technologies - Management of Big Data.

1. Data Analytics Made Accessible , Anil K Maheshwari, 2nd Edition, ISBN : 9352604180, McGraw Hill Education, 2023

2. Big Data Analytics, paperback 2nd ed., Seema Acharya, Subhasini Chellappan, Wiley (2019).

3. Chandraish Sinha (2022).” Mastering Power BI”,1st Edition, BPB Publications

1. Marleen, David,” Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau”, 3rd Edition, Pakt,

2. Ramesh Sharda, Dursun, Delen, Efraim Turban (2017). “Business Intelligence: Manegerial Perspective on Analytics”, 3rd Edition, Pearson Publication.

ESE 50%

The main objectives of this course are to

1. understand the fundamental concepts of IoT and various protocols. 

2. understand the characteristics and working of sensors and actuators.

3. design and develop IoT applications with sensors, actuators, and cloud to interact with real-time environment and conditions.

Importance to Internet of Things – Opportunities – Connecting Devices – IoT Forum. Internet of Things Architecture: Basic Layer Architecture – IoT Network Layer Entities – Sensors and Actuators – Techniques for Data Acquisition and Control – Edge Layer – Cloud Data Management Architecture – Data Storage Layer.

The ISO/OSI Model – IoT Prespective – TCP/IP Model and IoT Reference Model. IoT Enabling Platforms: Edge Device Platforms – IoT Gateway Devices – IoT Cloud Platforms – IoT Application Platforms – Processors – Software: OpenWSN, TinyOS, FreeRTOS, Contikit, Web-based IoT Design platforms.

Introduction to Arduino, Arduino UNO, Installing the Software, Fundamentals of Arduino Programming. IoT Physical Devices and Endpoints - RaspberryPi: Introduction to RaspberryPi, About the RaspberryPi Board: Hardware Layout, Operating Systems on RaspberryPi, Configuring RaspberryPi, Programming RaspberryPi with Python.

Introduction to Cloud Storage models and communication APIs. Webserver – Web server for IoT, Cloud for IoT, Python web application framework. Designing RESTful web API. Connecting to APIs.

Home Automation, Smart Cities, Environment Monitoring, Retail, Logistics, Supply chain management, Agriculture and Breeding, Industry Automation, Medical and Health care

1. K.N. Raja Rao (2021), “Internet of Things: Concepts and Applications”, Wiley (ISBN: 978-93-5424-784-2)

2. Arshdeep Bahga, Vijay Madisetti (2015), “Intetnet of Things: A Hands-on Approach”, Universities Press Private Limited, India, Edition: 2022 (ISBN: 978-81-7371-954-7) 

1. Raj Kamal (2017), “Internet of Things: Architecture and Design Principles”, 1st Edition, McGraw Hill Education. (ISBN: 978-93-5260-522-4). 

2. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry,”IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things”, 1 stEdition, Pearson Education (Cisco Press Indian Reprint). (ISBN: 978-93-8687-374-3).

ESE 50%

The main objectives of this course are to

1. create systems that are semantically driven and contextually aware, facilitating the acquisition, organization, processing, sharing, and utilization of knowledge inherent in multimedia content.. 

2. focus on optimizing the automation of the entire knowledge lifecycle and attaining semantic interoperability among web resources and services.

3. Design and develop robotics which is inherently multi-disciplinary, given that robots are intricately sophisticated systems encompassing mechanical, electrical, electronic hardware, and software components.

History of AI - Symbolic AI: Cognitive Simulation, Logic-Based Approach, Rule-Based Knowledge Representation, Structural Knowledge Representation, Mathematical Linguistics Approach - Computational Intelligence: Connectionist Models, Mathematics-Based Models, Biology-Based Models.

Search Methods: State space and search tree, Blind search, Heuristic search, Adversaial search, search for constraint satifaction problems, special methods of heuristic search

Genetic algorithms , Evolution Strategies, Evolutionary Programming, Genetic Programming, Other biology-Inspired models, Rule-Bases Systems: Model of Rule-based systems, Reasoning strategies in rule-based systems, conflict resolution and rule matching, Expert systems Vs Rule-Based Systems.

Artifical Neuron, Basic Structures of Neural Networks, Concise Survey of Neural Network models, Reasoning with Imperfect Knowledge: Bayesian Inference and Bayes Networks, Dempster-Shafter Theory, Non-monotonic Reasoning, Defining Vague Notions in Knowledge-Based Systems: Model Based on Fuzzy set theory, rough set theory, cognitive Architecture: Concept of Agent, Multi-agent Systems.

Perception and Pattern recognition, Knowledge representation, problem solving, reasoning , Decision making, Natural Language processing (NLP), Learning, Manipulation and Locomation , Social Intelligence, Emotional Intelligence and creativity, Issues of AI, Potential Barriers and Challenges in AI, Determinants of AI development- Case Studies.

1. Introduction to Artificial Intelligence by Mariusz Flasinski, Springer, 2016

2. Artifical Intelligence Applications and Innovations by Ilias Maglogiannis, Lazaros Iliadis, Antonios Papaleonidas, Ioannis Chochliouros (Editors), 2023

ESE 50%

This course offers an experimental view of mastering Kotlin programming for Android app development. Objective of the course is to understand the working principle and implement essential Android features and components using Kotlin.

Lab Exercise 1: Create a simple app that displays text and an image. Use TextView for text display and ImageView for image display. You can either use static text and image resources or fetch them dynamically from the internet.

Lab Exercise 2: Create an app where users can perform basic arithmetic operations (addition, subtraction, multiplication, division). Implement functions to handle each operation and display the result.

Lab Exercise 3: Develop an app with multiple activities. Use intents to navigate between activities. For example, create a login page as one activity and a dashboard as another activity. Use intents to pass data between these activities.

Lab Exercise 4: Create an app that logs lifecycle events of an activity. Override lifecycle methods such as onCreate(), onStart(), onResume(), onPause(), onStop(), onDestroy(), and log messages in each method to understand the activity lifecycle.

Lab Exercise 5: Build a form-based app that includes various input controls like EditText, RadioButton, CheckBox, Spinner, etc. Allow users to input data using these controls and perform actions based on user input.

Lab Exercise 6: Develop an app where users can view a collection of images. Use RecyclerView to display a grid or list of images fetched from local storage or the internet. Implement click listeners to view individual images in full-screen mode.

Lab Exercise 7: Create an app with multiple fragments. Log lifecycle events of each fragment similar to activity lifecycle events. Understand how fragments behave during various stages like creation, attachment, visibility changes, etc.

Lab Exercise 8: Design an app with a menu bar. Implement options menu or context menu based on user interactions. Add menu items that perform different actions like opening settings, sharing content, etc.

Lab Exercise 9: Build an app that utilizes RecyclerView to display a list of items. Use RecyclerView.Adapter and RecyclerView.ViewHolder to manage the data and UI for each item. Implement item click listeners to perform actions when an item is clicked.

Lab Exercise 10: Create an app that stores user preferences using SharedPreferences. Allow users to customize app settings such as theme color, font size, etc., and store these preferences using SharedPreferences. Retrieve and apply these preferences when the app restarts

2.      Nagy, Robert. Simplifying Application Development with Kotlin Multiplatform Mobile: Write Robust Native Applications for IOS and Android Efficiently. United Kingdom, Packt Publishing, 2022.

3.      https://developer.android.com

2.      Kotlin for Android App Development.Sommerhoff, P. United Kingdom: Pearson Education 2018.

The main objectives of this course are to:

1. Introduce students to the concepts of Data Analytics, covering fundamental principles and techniques.

2. Provide students with an understanding of building Big Data systems, including data gathering, processing, and storage.

3. Enable students to gain practical experience in programming tools commonly used in data sciences.

4. Empower students with the tools, techniques, and understanding of industrial needs, preparing them for real-world applications in data analytics.

Lab Exercise 1: Explore the concepts of Business Intelligence and its applications in real-world scenarios.

Lab Exercise 2: Understand the data processing chain, including data collection, storage, and mining.

Lab Exercise 3: Learn various data visualization techniques and tools used in Business Intelligence.

Lab Exercise 4: Design and develop a data warehouse architecture for a given scenario.

Lab Exercise 5: Analyze a case study on BI implementation in the healthcare sector and identify design considerations for data warehousing.

Lab Exercise 6: Implement data mining techniques such as gathering, selecting, and preparing data for analysis.

Lab Exercise 7: Evaluate the results of data mining operations and interpret their significance.

Lab Exercise 8: Implement decision tree algorithms and regression analysis techniques using Python or R.

Lab Exercise 9: Explore non-linear regression models and logistic regression for classification tasks.

Lab Exercise 10: Implement cluster analysis techniques such as K-Means algorithm and evaluate their performance.

Lab Exercise 11: Study web content mining, web structure mining, and web usage mining algorithms.

Lab Exercise 12: Implement text mining techniques for extracting insights from textual data.

Lab Exercise 13: Build and analyze term-document matrices for text mining applications.

Lab Exercise 14: Compare and contrast text mining and data mining techniques based on their applications and algorithms.

Lab Exercise 15: Analyze a case study on text mining and web mining in the context of WhatsApp and private security.

Lab Exercise 16: Explore big data technologies and management strategies, including Hadoop, Spark, and NoSQL databases.

ESE 50%

The main objectives of this course are to:

1. Introduce students to the foundational concepts and practical applications of Internet of Things (IoT) technology.

2. Provide hands-on experience in working with Arduino microcontroller boards and various sensors commonly used in IoT projects.

3. Familiarize students with IoT development tools, including the Arduino IDE and cloud platforms, for data storage and analysis.

4. Enable students to design, implement, and test basic IoT applications, such as smart home automation and sensor data monitoring, to gain practical skills in IoT development.

Lab Exercise 1: Understanding Arduino UNO Board and Components 

Lab Exercise 2: Installing and work with Arduino IDE 

Lab Exercise 3: Blinking LED with Arduino 

Lab Exercise 4: Simulation of 4-Way Traffic Light with Arduino 

Lab Exercise 5: Working with Temperature, Humidity, Light, Sound and other sensors

Lab Exercise 6: Developing smart-home automation with IoT

Lab Exercise 7: Developing smart security applications with IR and other sensors

Lab Exercise 8: Interfacing Arduino with Cloud (Thingspeak API) 

Lab Exercise 9: Storing and retrieving sensor data with Cloud

Lab Exercise 10: Developing simple Mobile Application to interact with IoT sensors, Cloud

ESE 50%

The main objectives of this course are to:

1. Develop a strong understanding of fundamental graph traversal algorithms, including Breadth First Search (BFS) and Depth First Search (DFS), and their applications in solving graph-related problems.

2. Gain proficiency in implementing classic artificial intelligence (AI) algorithms, such as solving puzzles like Tic-Tac-Toe, 8-Puzzle, and the 8-Queens Problem, using Python programming language.

3. Acquire hands-on experience in applying various search algorithms, heuristic search techniques, and problem-solving strategies to solve complex problems in AI and game development domains.

4. Enhance problem-solving skills, algorithmic thinking, and programming proficiency by completing a series of practical programming exercises and projects in Python, covering a wide range of topics in computer science and AI.

Lab Exercise 1: Implement Breadth First Search (BFS) algorithm in Python.

Lab Exercise 2: Implement Depth First Search (DFS) algorithm in Python.

Lab Exercise 3: Develop a Tic-Tac-Toe game in Python.

Lab Exercise 4: Create a program to solve the 8-Puzzle problem using Python.

Lab Exercise 5: Implement the Water-Jug problem solver in Python.

Lab Exercise 6: Develop a program to solve the Travelling Salesman Problem (TSP) in Python.

Lab Exercise 7: Write a Python program to solve the Tower of Hanoi puzzle.

Lab Exercise 8: Implement the Monkey Banana Problem solver using Python.

Lab Exercise 9: Create a Python program to solve the Missionaries and Cannibals problem.

Lab Exercise 10: Implement the 8-Queens Problem solver in Python.

Lab Exercise 11: Write a Python program to solve problems using the Hill Climbing algorithm.

Lab Exercise 12: Develop a Python program to solve problems using the A* algorithm.

ESE 50%

Course Description:

This course aims at developing the ability to write the mathematical proofs. It helps the students to understand and appreciate the beauty of the abstract nature of mathematics and also to develop a solid foundation of theoretical mathematics.

Course Objectives : This course will help the learner to

COBJ1. understand the theory of matrices, concepts in vector spaces and Linear Transformations.

COBJ2. gain problems solving skills in solving systems of equations using matrices, finding eigenvalues and eigenvectors, vector spaces and linear transformations.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Elementary row operations, rank, inverse of a matrix using row operations, Echelon forms, normal forms, system of homogeneous and non-homogeneous equations, Cayley Hamilton theorem, eigenvalues and eigenvectors, diagonalization of square matrices.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Vector space-examples and properties, subspaces-criterion for a subset to be a subspace, linear span of a set, linear combination, linear independent and dependent subsets, basis and dimensions, and standard properties.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

Linear transformations, properties, matrix of a linear transformation, change of basis, range and kernel, rank and nullity, rank-nullity theorem, non-singular linear transformation, eigenvalues and eigenvectors of a linear transformation.

2. V. Krishnamurthy, V. P. Mainra, and J. L. Arora, An introduction to linear algebra. New Delhi, India: Affiliated East East-West Press Pvt Ltd., 2003.

2. S. Lang, Introduction to Linear Algebra, 2nd ed., New York: Springer-Verlag, 2005.

3. S. H. Friedberg, A. Insel, and L. Spence, Linear algebra, 4th ed., Pearson, 2015.

4. Gilbert Strang, Linear Algebra and its Applications, 4th ed., Thomson Brooks/Cole, 2007.

5. K. Hoffmann and R. A. Kunze, Linear algebra, 2nd ed., PHI Learning, 2014.

Course Description: This course is concerned with the fundamentals of mathematical modeling. It deals with finding solution to real world problems by transforming into mathematical models using differential equations. The coverage includes mathematical modeling through first order, second order and system of ordinary differential equations.

 Course objectives​: This course will help the learner to

This course will help the learner to

COBJ1.  interpret the real-world problems in the form of first and second order differential equations. 

COBJ2.  familiarize with some classical linear and nonlinear models. 

COBJ3.  analyse the solutions of systems of differential equations by phase portrait method.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

Population Dynamics, Carbon dating, Newtons law of cooling, Epidemics, Economics, Medicine, mixture problem, electric circuit problem, Chemical reactions, Terminal velocity, Continuously compounding of interest.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

The vibrations of a mass on a spring, free damped motion, forced motion, resonance phenomena, electric circuit problem, Nonlinear-Pendulum.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Phase plane analysis: Phase Portrait for Linear and Non-Linear Systems, Stability Analysis of Solution, Applications, Predator prey model: Lotka-Volterra Model, Kermack-McKendrick Model, Predator-Prey Model and Harvesting Analysis, Competitive-Hunter Model, Combat models: Lanchester Model, Battle of IWO Jima, Battle of Vietnam, Battle of Trafalgar., Mixture Models, Epidemics-SIR model, Economics.

Course Description: This course is an introductory course to the basic concepts of Graph Theory. This includes a definition of graphs, types of graphs, paths, circuits, trees, shortest paths, and algorithms to find shortest paths.

Course objectives: This course will help the learner to

COBJ 1. gain conceptual knowledge on terminologies used in graph theory.

COBJ 2. understand the results on graphs and their properties.

COBJ 3. gain proof writing and algorithm writing skills.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Graphs, connected graphs, classes of graphs, regular graphs, degree sequences, matrices, isomorphic graphs.

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Bridges, trees, minimum spanning trees, cut-vertices, blocks, traversability, Eulerian and Hamiltonian graphs, digraphs. 

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Matching, factorizations, decompositions, graceful labeling, planar graphs, Embedding graphs on surfaces.

Course description: Operations research deals with the problems on optimization or decision making that are affected by certain constraints / restrictions in the environment. This course aims at teaching solution techniques of solving linear programming models, simple queuing model, two-person zero sum games and Network models.

Course objectives: This course will help the learner to

COBJ1. gain an insight executing the algorithms for solving linear programming problems including transportation and assignment problems.

COBJ2. learn about the techniques involved in solving the two person zero sum game.

COBJ3. calculate the estimates that characteristics the queues and perform desired analysis on a network.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Introduction to simplex algorithm –Special cases in the Simplex Method –Definition of the Dual Problem – Primal Dual relationships – Dual simplex methods. Transportation Models: Determination of the starting solution – iterative computations of the transportation algorithm. Assignment Model: The Hungarian Method.

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Elements of a queuing Model – Pure Birth Model – Pure Death Model –Specialized Poisson Queues – Steady state Models: (M/M/1):(GD/∞/∞) – (M/M/1):(FCFS/∞/∞) - (M/M/1):(GD/N/∞) – (M/M/c):(GD/∞/∞) –  (M/M/∞):(GD/∞/∞).

Game Theory: Optimal solution of two person zero-sum games – Solution of Mixed strategy Games (only Linear programming solution).

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Linear programming formulation of the shortest-route Problem. Maximal Flow model:- Enumeration of cuts – Maximal Flow Algorithm – Linear Programming Formulation of Maximal Flow Model. CPM and PERT:- Network Representation – Critical path computations – Construction of the Time Schedule – Linear Programming formulation of CPM – PERT calculations.

Course description: This course aims at providing hands on experience in using Python functions to illustrate the notions vector space, linear independence, linear dependence, linear transformation and rank.

Course objectives: This course will help the learner to

COBJ1. The built in functions required to deal with vectors and Linear Transformations.

COBJ2. Python skills to handle vectors using the properties of vector spaces and linear transformations

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

1. Operations on matrices
2. Finding rank of matrices
   
3. Reducing a matrix to Echelon form
4. Inverse of a matrix by different methods
5. Solving system of equations using various methods
6. Finding eigenvalues and eigenvectors of a matrix
7. Expressing a vector as a linear combination of given set of vectors
8. Linear span, linear independence and linear dependence
9. Linear transformations and plotting of linear transformations
10. Applications of Rank-Nullity Theorem

Course description: This course provides students with an understanding of the practical and theoretical aspects of mathematical models involving ordinary differential equations (ODEs) using Python programming.

Course objectives:

This course will help the learner to 

COBJ1. various models spanning disciplines such as physics, biology, engineering, and finance. 

COBJ2. develop the basic understanding of differential equations and skills to implement numerical algorithms to solve mathematical problems using Python.

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

1. Growth of a population – Linear growth, Exponential growth, Logistic growth
2. Decay Model - Radioactive Decay
3. Numerical Methods
4. A Simple Pendulum
5. Spreading of a Disease
6. Mixture problems
7. Trajectory of a ball
8. Spring mass system
9. Electrical Circuits

Course description: The course graph theory using Python is aimed at enabling the students to appreciate and understand core concepts of graph theory with the help of technological tools. It is designed with a learner-centric approach wherein the students will understand the concepts of graph theory using programming tools and develop computational skills.

Course objectives: This course will help the learner to

COBJ1. gain familiarity in Python language using jupyter interface and NetworkX package

COBJ2. construct graphs and analyze their structural properties.

COBJ3. implement standard algorithms for shortest paths, minimal spanning trees and graph searching..

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

1. Introduction to NetworkX package
2. Construction of graphs
3. Degree and distance related parameters
4. In-built functions for different graph classes
5. Computation of graph parameters using in-built functions
6. Graph Operations and Graph Connectivity
7. Customization of Graphs
8. Digraphs
9. Matrices and Algorithms of Graphs
10. Graph as models.

Course description: Operations research deals with the problems on optimization or decision making that are affected by certain constraints / restrictions in the environment. This course aims to enhance programming skills in Python to solve problems chosen from Operations Research.

Course objectives: This course will help the learner to

COBJ1. gain a familiarity in using Python to solve linear programming problems, calculate the estimates that characteristics the queues and perform desired analysis on a network.

COBJ2. use Python for solving problems on Operations Research.

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

1. Simplex method
2. Dual simplex method
3. Balanced transportation problem
4. Unbalanced transportation problem
5. Assignment problems
6. (M/M/1) queues
7. (M/M/c) queues
8. Shortest path computations in a network
9. Maximum flow problem
10. Critical path computations

The main objectives of this course are to

1. Understand the fundamentals of data communications, including network types, protocols, and standards.

2. Explore transmission mediums and error correction techniques used in networking.

3. Gain knowledge of switching, multiplexing, and data link control mechanisms in networks.

4. Learn about wired and wireless LANs, including IEEE standards, Ethernet, Wi-Fi, Bluetooth, and ATM technologies.

Introduction to Data communications- Network Types: Local Area Network-  Wide Area Network - Protocols and standards : protocols-standards-standards organizations- internet standards Network models: OSI model – layers in the OSI model – TCP/IP protocol suite.

Transmission Medium: Guided Media - Twisted-Pair Cable- Coaxial Cable - Fiber-Optic Cable Unguided Media: Wireless -Radio Waves - Microwaves - Infrared - Transmission Mode-Digital to Analog Control-Analog to Digital Conversion-Introduction to errors-Redundancy- Detection Versus Correction.

Switching: Circuit Switched Neytworks- Packet Switching, Multiplexing - types of Multiplexing - Multiplexing Application – Spread Spectrum- Datagram Networks- Virtual Circuit Network-Framing.

IEEE Standards - Standard Ethernet: Addressing - Access Method- Efficiency of Standard Ethernet and Implementation -Fast Ethernet : Access Method, Physical layer -IEEE 802.11 : Architecture - MAC Sublayer - Addressing Mechanism - Physical Layer – Bluetooth:  Architecture-Bluetooth Layers - ATM-ATM LAN.

Repeaters Bridges- Routers - Gateway - Routing algorithms : Distance-Vector Routing - Link-State Routing - Path-Vector Routing – TCP: TCP Services - TCP Features - Segment - A TCP Connection –UDP : User Datagram -  UDP services- UDP Applications -DNS- File Transfer - World Wide Web Architecture-Web document.

1. Larry Peterson, L., and Brule Davie, S., Computer Networks – A System Approach, MarGankangmann –      Harcourt Asia, 2009.

2. Tanenbaum, A., Wetherall, D., Kurose, J., & Ross, K. (2019). Computer networks title: Computer networking: A top-down approach. Instructor, 201901.

ESE 50%

1. Provide an understanding of the fundamentals of cloud computing, including its architecture, services, and deployment models.

2. Explore virtualization technologies and their role in cloud computing environments.

3. Introduce concepts related to data storage, management, and security in cloud computing.

4. Familiarize students with various cloud computing tools, technologies, and platforms for efficient deployment and management of cloud-based services.

Cloud Computing Foundation: Introduction to Cloud Computing – Move to Cloud Computing – Types of Cloud – Working of Cloud Computing.

Cloud Computing Architecture: Cloud Computing Technology – Cloud Architecture – Cloud Modelling and Design - Virtualization: Foundation – Grid, Cloud and Virtualization – Virtualization and Cloud Computing.

Data Storage and Cloud Computing: Data Storage – Cloud Storage – Cloud Storage from LANs to WANs – Cloud Computing Services: Cloud Services – Cloud Computing at Work.

Cloud Computing and Security: Risks in Cloud Computing – Data Security in Cloud – Cloud Security Services – Cloud Computing Tools: Tools and Technologies for Cloud – Cloud Mashaps – Apache Hadoop – Cloud Tools.

Cloud Applications – Moving Applications to the Cloud – Microsoft Cloud Services – Google Cloud Applications – Amazon Cloud Services – Cloud Applications.

ESE 50%

The main objectives of this course are to

1. Introduce fundamental concepts and techniques of image processing.

2. Provide practical skills in enhancing, restoring, and compressing digital images.

3. Familiarize students with image segmentation and color image processing fundamentals.

4. Enable students to apply image processing techniques in various real-world applications.

Overview of Image Processing, Nature of Image Processing, Digital Image Representation, Types of Images, Digital Image Processing Operations, Fundamental Steps in Image Processing. Digital Imaging System: Physical Aspects of Imaging Acquisition, Biological Aspects of Image Acquisition, Review of Digital Camera, Sampling and Quantization, Image Quality, Image Storage and File Formats. Applications of Image Processing: Medical imaging, Robot vision, Character recognition, Remote Sensing.

Some basic gray-level transformations, Histogram processing, Smoothing and Sharpening spatial filters. Image Enhancement In Frequency Domain: Smoothing and Sharpening frequency domain filters, Homomorphic filtering.

Noise models, Restoration in the presence of noise only-spatial filtering, Estimating the degradation functions, Inverse filtering.

Image compression models, Loss-less and Lossy compression. Image Segmentation: Detection of discontinuities, Edge linking and boundary detection, Thresholding, Region-based segmentation.

Devices for Colour Imaging, Colour Image Storage and Processing, Colour Models, Colour Quantization Recent developments.

1. "Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods, Pearson Education, January 2022,  4th edition link: https://dl.icdst.org/pdfs/files4/01c56e081202b62bd7d3b4f8545775fb.pdf

2. Digital Image Processing Using MATLAB, 3rd edition, Rafael C. Gonzalez, University of Tennessee; Richard E. Woods, Interapptics; Steven L. Eddins, MathWorks Gatesmark Publishing, 2020,ISBN: 9780982085417.

1. A.K. Jain, Fundamentals of Digital Image Processing, Pearson Education , 2007 

2. L. R. Rabiner and B. Gold, Theory and Application of Digital Signal Processing, Pearson Education , 2004.

ESE 50%

The main objectives of this course are to

1. Introduce the foundational concepts of neural networks, including their structure, functioning, and application areas.

2. Explore the theory and practical implementation of perceptron networks, including their learning algorithms and architectures.

3. Investigate advanced neural network architectures such as convolutional neural networks (CNN) and recurrent neural networks (RNN), along with their applications.

4. Familiarize students with restricted Boltzmann machines (RBM) and autoencoders, enabling them to understand their principles and usage in various domains.

Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An Introduction- Evolution of Neural Networks-Basic Models of Artificial Neural Network- Biological neural network Vs ANN, Important Terminologies of ANNs-Supervised Learning Network.

Shallow neural networks- Perceptron Networks-Theory-Perceptron Learning Rule Architecture-Flowchart for training Process-Perceptron Training Algorithm for Single and Multiple Output Classes.

Introduction to CNN , Architecture of CNN, Advantages of CNN over other Neural Networks, Types of Neural network architectures, Applications of CNN, Different layers and its use to build a CNN model, Real-time use cases of CNN.

Introduction to RNN,How RNN is different from other Neural Network models,Structure and working of RNN,Exploding and Vanishing Gradient descend problem, Long Short-Term Memory (LSTM),How LSTM overcome the problem of Vanishing Gradient descent?,Real-time use-cases of LSTM.

Restricted Boltzmann Machine (RBM), Applications of RBM, Collaborative Filtering with RBM, Introduction to Autoencoders, Autoencoders applications,Types of encoders and applications.

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (MIT Press, 2016)

2. "Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal (Springer, 2018)

3. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (O'Reilly Media, 2019)

1. "Deep Learning for Computer Vision" by Rajalingappaa Shanmugamani (Packt Publishing, 2021)

2. "Recurrent Neural Networks with Python Quick Start Guide" by Michael Heydt (Packt Publishing, 2019)

3. "Autoencoder Applications and Beyond" by Madhusmita Panda and Nilanjan Dey (CRC Press, 2020).

ESE 50%

The main objectives of this course are to:

1. Understand cloud computing fundamentals, including architecture, deployment models, and service models.

2. Gain practical experience in setting up and configuring cloud environments using major cloud platforms.

3. Learn about various cloud services and their functionalities, such as virtualization, storage, networking, security, and monitoring.

4. Develop skills in deploying and managing applications in the cloud, including containerization, serverless computing, and DevOps practices.

Lab Exercise 1: Introduction to Cloud Computing Concepts

• Setting up cloud computing environments using popular platforms like AWS, Azure, or Google Cloud

• Exploring the basic functionalities of cloud computing services

Lab Exercise 2: Virtualization and Cloud Architecture

• Creating and managing virtual machines using cloud platforms

• Understanding the architecture of cloud computing systems

Lab Exercise 3: Data Storage in the Cloud

• Configuring cloud storage services such as Amazon S3, Google Cloud Storage, or Azure Blob Storage

• Uploading, downloading, and managing data in cloud storage

Lab Exercise 4: Cloud Networking and Security

• Configuring virtual networks and subnets in cloud environments

• Implementing security measures such as firewalls and access control lists (ACLs) in the cloud

Lab Exercise 5: Deploying Applications on Cloud Platforms

• Deploying sample applications on cloud platforms using containerization tools like Docker or Kubernetes

• Understanding the scalability and elasticity features of cloud services

Lab Exercise 6: Monitoring and Logging in the Cloud

• Setting up monitoring and logging services in the cloud to track resource usage and performance metrics

• Analyzing logs and metrics to identify performance bottlenecks and optimize resource utilization

Lab Exercise 7: Cloud Identity and Access Management (IAM)

• Configuring IAM policies and roles to control access to cloud resources

• Managing user identities and permissions in a multi-user cloud environment

Lab Exercise 8: High Availability and Disaster Recovery

• Configuring high availability solutions such as load balancing and auto-scaling

• Implementing disaster recovery strategies using backup and replication techniques

Lab Exercise 9: Serverless Computing and Function as a Service (FaaS)

• Developing and deploying serverless applications using platforms like AWS Lambda or Azure Functions

• Understanding the benefits and limitations of serverless computing architectures

Lab Exercise 10: Big Data and Analytics in the Cloud

• Setting up big data processing frameworks such as Apache Hadoop or Spark on cloud platforms

• Performing data analytics and visualization tasks using cloud-based tools and services

Lab Exercise 11: Internet of Things (IoT) and Cloud Integration

• Integrating IoT devices with cloud platforms to collect and analyze sensor data

• Implementing IoT applications using cloud-based services for data storage and processing

Lab Exercise 12: DevOps Practices in the Cloud

• Implementing continuous integration and continuous deployment (CI/CD) pipelines using cloud-based tools

• Automating infrastructure provisioning and application deployment workflows in the cloud

Lab Exercise 13: Container Orchestration with Kubernetes

• Deploying and managing containerized applications using Kubernetes clusters on cloud platforms

• Scaling and updating applications dynamically using Kubernetes features

Lab Exercise 14: Hybrid and Multi-Cloud Deployments

• Configuring hybrid cloud environments to seamlessly integrate on-premises and cloud resources

• Implementing multi-cloud strategies to leverage the strengths of different cloud providers

Lab Exercise 15: Cost Optimization and Resource Management

• Analyzing cloud usage and optimizing resource allocation to minimize costs

• Implementing cost management strategies such as budgeting and resource tagging

Lab Exercise 16: Case Study

• Demonstrating proficiency in cloud computing concepts and technologies.

ESE 50%

The main objectives of this course are to:

1. To learn the digital image processing techniques and understand the fundamentals of image processing such as image enhancement, restoration, compression and segmentation along with fundamentals of colour images.

1. Demonstration of gray scale conversion using any medical images -MATLAB

2. Analysis of spatial and intensity resolution of images.

3. Contrast stretching of a low contrast image, Histogram, and Histogram Equalization

4. Smoothing and Sharpening using filtering techniques

5. Implementation of Image Smoothening Filters (Mean and Median filtering of an Image)

6. Image Compression by DCT, DPCM, HUFFMAN coding

7. Implementation of image restoring techniques

8. Implementation of Image Intensity slicing technique for image enhancement

9. Edge detection, Thresholding and Region-based segmentation.

10. Analysis of images with different colour models

3. Palm, W.J. “MATLAB for Engineering Applications”, McGraw-Hill Education, ISBN:9781260084719, 2018, https://books.google.co.in/books?id=TG9XuQEACAAJ

ESE 50%

The main objectives of this course are to:

1. To provide practical experience in implementing neural network models.

2. To reinforce theoretical concepts learned in neural network courses.

3. To develop skills in model building, training, and evaluation.

4. To prepare students for real-world applications of neural networks.

Lab Exercise 1: Perceptron Implementation

• Extend the perceptron program to handle multiple output classes.

Lab Exercise 2: Binary Classification with Feedforward Neural Network

• Implement a basic feedforward neural network for binary classification tasks.

Lab Exercise 3: Handwritten Digit Recognition with CNN

• Demonstrate prediction using a CNN Digits Classifier using the MNIST handwritten digits dataset.

Lab Exercise 4: Transfer Learning

• Implement Transfer Learning with a Pretrained Model using a simple dataset.

Lab Exercise 5: Image Generation with Variational Autoencoders

• Implement Variational Autoencoders (VAEs) for Image Generation.

Lab Exercise 6: Sequential Data Processing with RNN

• Implement a basic Recurrent Neural Network (RNN) for sequential data processing.

Lab Exercise 7: Sequence Prediction with LSTM

• Develop a program to train an LSTM network for sequence prediction tasks.

Lab Exercise 8: Hyperparameter Tuning

• Implement Hyperparameter Tuning and Model Optimization techniques.

Lab Exercise 9: Advanced LSTM Training

• Further train an LSTM network for sequence prediction tasks.

Lab Exercise 10: Advanced Model Optimization

• Implement additional Hyperparameter Tuning and Model Optimization techniques.

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (MIT Press, 2016)

2. "Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal (Springer, 2018)

3. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (O'Reilly Media, 2019)

ESE 50%

To explore complex world problems physicists, engineers, financiers and mathematicians require certain methods. These practical problems can rarely be solved analytically. Their solutions can only be approximated through numerical methods. This course deals with the theory and application of numerical approximation techniques.

This course will help the learner to

1. learn about error analysis, solution of nonlinear equations, finite differences, interpolation, numerical integration and differentiation, numerical solution of differential equations, and matrix computation.

2. emphasis the development of numerical algorithms to provide solutions to common problems formulated in science and engineering.

Errors and their analysis, Floating point representation of numbers, solution of algebraic and Transcendental Equations: Bisection method, fixed point Iteration method, the method of False Position, Newton Raphson method and Mullers method. Solution of linear systems, matrix inversion method, Gauss elimination method, Gauss-Seidel and Gauss-Jacobi iterative methods, modification of the Gauss method to compute the inverse, LU decomposition method.

Errors and their analysis, Floating point representation of numbers, solution of algebraic and Transcendental Equations: Bisection method, fixed point Iteration method, the method of False Position, Newton Raphson method and Mullers method. Solution of linear systems, matrix inversion method, Gauss elimination method, Gauss-Seidel and Gauss-Jacobi iterative methods, modification of the Gauss method to compute the inverse, LU decomposition method.

Finite differences: Forward difference, backward difference and shift operators, separation of symbols, Newton’s formulae for interpolation, Lagrange’s interpolation formulae, numerical differentiation. Numerical integration: Trapezoidal rule, Simpson’s one-third rule and Simpson’s three-eighth rule.

Finite differences: Forward difference, backward difference and shift operators, separation of symbols, Newton’s formulae for interpolation, Lagrange’s interpolation formulae, numerical differentiation. Numerical integration: Trapezoidal rule, Simpson’s one-third rule and Simpson’s three-eighth rule.