B.Tech. Computer Science and Engineering (Robotics & Artificial Intelligence)

Curriculum

1ST SEMESTER SUBJECTS

Applied Physics provides fundamental knowledge of mechanics, waves, optics, electricity, magnetism, and modern physics, emphasizing practical applications and problem-solving skills essential for engineering and technological development.

Course Outcome:

At the end of the course, the student will be able to-
CO1: Acquire knowledge about the Maxwell equation and Electromagnetic spectrum
CO2: Understand laser system in industries, laboratories and in communication
CO3: Acquire knowledge about the Crystallography, superconductivity and Magnetic materials.
CO4: Appreciate the need for quantum mechanics, wave particle duality, uncertainty principle etc. and their applications.
CO5: Understand the properties and synthesis of nanomaterials.

Applied Physics Laboratory develops practical skills through experiments in mechanics, optics, electricity, magnetism, and semiconductor physics, enabling students to verify theoretical concepts, analyze experimental data, and understand physical phenomena.

Course Outcome:

At the end of the course, students will be:
CO1: Able to verify some of the theoretical concepts learnt in the theory courses.
CO2: Trained in carrying out precise measurements and handling sensitive equipment.
CO3: Introduced to the methods used for estimating and dealing with experimental uncertainties and systematic errors.
CO4: Learn to draw conclusions from data and develop skills in experimental design.
CO5: Write a technical report which communicates scientific information in a clear and concise manner.

Programming Concepts introduces the fundamentals of computer programming, including problem-solving techniques, algorithms, flowcharts, data types, operators, control structures, functions, and arrays, using a structured programming language.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Describe the procedural and object-oriented paradigm with concepts of streams, classes, functions, data and objects.
CO2: Understand dynamic memory management techniques using pointers, constructors, destructors, etc.
CO3: Describe the concept of function overloading, operator overloading, virtual functions and polymorphism.
CO4: Understanding inheritance in OOP for code reusability and extensible design.
CO5: Demonstrate the use of various OOPs concepts with the help of programs.

Programming Concepts Lab provides hands-on practice in writing, debugging, and executing programs based on the concepts covered in programming Concepts, including control structures, functions, and arrays.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Describe the procedural and object-oriented paradigm with concepts of streams, classes, functions, data and objects.
CO2: Understand dynamic memory management techniques using pointers, constructors, destructors, etc.
CO3: Describe the concept of function overloading, operator overloading, virtual functions and polymorphism.
CO4: Understanding inheritance in OOPs for code reusability and extensible design.
CO5: Demonstrate the use of various OOPs concepts with the help of programs.

This course provides evelops advanced concepts in multivariable calculus, vector calculus, differential equations, Laplace transforms, and numerical methods, equipping students with mathematical techniques for solving engineering problems.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Able to verify some of the theoretical concepts learnt in the theory courses.
CO2: trained to visualize and conceptualize the engineering problems
CO3: Relate matrices and linear transformations, compute Eigen values and Eigen vectors of linear transformations.
CO4: Solve engineering problems by making use of ordinary differential equations.
CO5: Inter-relationship amongst the line integral, double and triple integral formulations.

Workshop Practices provides hands-on exposure to basic manufacturing trades such as carpentry, fitting, welding, sheet metal work, and plumbing, along with workshop safety practices.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Prepare moulds, cores and wooden joints for basic manufacturing applications.
CO2: Perform basic welding operations and fabricate simple welded joints.
CO3: Carry out fitting and machining operations using conventional workshop tools and machines.
CO4: Fabricate simple products using sheet metal and forging processes.
CO5: Demonstrate basic electrical wiring, soldering and electronic circuit assembly practices.

Environmental Studies develops awareness of ecosystems, biodiversity, natural resources, pollution, climate change, environmental management, and sustainable development, encouraging responsible practices and informed solutions to environmental challenges.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Analyse the impact of Biodiversity conservation on species and the environment.
CO2: Apply knowledge of environmental policies and legislations to evaluate and propose solutions for local and global environmental issues.
CO3: Implement environmental management plans to address campus environmental issues such as Waste disposal, water management, and sanitation.
CO4: Students will gain knowledge of the structure and function of ecosystems, biodiversity, and the importance of natural resources.
CO5: Recognize the interdependence between humans and the environment.

Communicative English-I develops foundational language skills in grammar, vocabulary, listening, speaking, reading, and writing for effective communication.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Use standard English aptly in listening, speaking, and communicative situations
CO2: Write error-free sentences and short texts in English Language
CO3: Comprehend reading passages effectively
CO4: Demonstrate use of apt and relevant vocabulary at lower intermediate to intermediate level
CO5: Develop effective oral presentation skills, including audience analysis and body language
CO6: Apply audio-visual aids to enhance communication and improve pronunciation

This course develops an entrepreneurial mindset through self-awareness, creativity, innovation, opportunity recognition, problem-solving, communication, teamwork, leadership, and professional ethics. Students are introduced to the startup ecosystem, design thinking, business models, prototyping, value propositions, and idea pitching to encourage solution-oriented thinking and entrepreneurial initiative.

Course Outcome:

CO1: Demonstrate self-awareness, confidence and growth mindset.
CO2: Apply creativity and innovation tools to generate ideas and solutions.
CO3: Identify opportunities through analysis of real-life and community problems.
CO4: Demonstrate effective communication, teamwork and leadership skills.
CO5: Explain entrepreneurship concepts, startup ecosystem and support systems.
CO6: Develop and present a solution-oriented entrepreneurial project.

Open Source Technology covers the philosophy and practices of open-source software development, licensing models, collaborative development tools, and fundamentals of Linux-based operating systems.

Course Outcome:

CO1: To Gain thorough understanding of the fundamental concepts, history and principles of Open Source Software
CO2: Effectively use and contribute to open source projects
CO3: Apply best practices for managing Linux and deep understanding of Kernel and its processes
CO4: To understand the principles and applications of network security protocols in Linux.
CO5: Fair Understanding of Shell scripting and its fundamentals
CO6: To develop incident response plans and conduct forensic investigations to analyze and respond to security breaches.

Open-Source Technology Lab provides hands-on practice with Linux-based operating systems, open-source tools, and collaborative version-control platforms.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Install and configure open-source virtualization software and Linux operating systems, and demonstrate familiarity with the Linux file system and shell environment.
CO2: Execute and manage Linux file and directory operations, and apply appropriate permission settings using command-line tools.
CO3: Work efficiently with Linux text editors (vi/vim) and understand their modes and operations for file editing and shell scripting.
CO4: Develop and execute basic shell scripts to perform arithmetic, file, string, and system-level operations using Bash scripting.
CO5: Build, compile, and execute C programs in a Linux environment using GCC and the Linux terminal.
CO6: Apply function-based logic in C programming to develop modular programs that perform tasks like swapping values, finding factorials, and reversing numbers.

2ND SEMESTER SUBJECTS

Applied Chemistry introduces fundamental chemical principles and their engineering applications, covering water chemistry, corrosion, materials, polymers, fuels, nanomaterials, spectroscopy, and green chemistry for sustainable technological development.

Course Outcome:

At the end of the course, the students will be able to achieve following outcomes:
CO1: Apply concepts of solution chemistry and water treatment for engineering applications and solve numerical problems related to concentration and hardness of water.
CO2: Describe the principles and applications of spectroscopic techniques (UV-Visible, IR, and NMR) for molecular characterization.
CO3: Explain the principles, classifications, and applications of chemical sensors used in environmental, biomedical, and industrial fields.
CO4: Classify polymers and explain their properties, synthesis, and applications in engineering and industrial sectors.
CO5: Analyze stereo chemical properties of organic molecules, determine molecular configurations, and interpret conformational stability.
CO6: Explain the mechanisms of organic reactions including substitution, addition, and elimination reactions, and apply them in basic organic synthesis.

Applied Chemistry Laboratory develops practical skills through experiments involving water analysis, corrosion, chemical reactions, material characterization, solution preparation, and analytical techniques, emphasizing safety, accuracy, and scientific interpretation.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Demonstrate proficiency in laboratory safety practices, handling of chemical reagents, preparation of standard solutions, and the use of volumetric analysis apparatus.
CO2: Perform acid–base and redox titrations to determine the concentration or strength of unknown solutions and analyze experimental results accurately.
CO3: Determine physicochemical properties of samples, including pH, total hardness, total alkalinity, surface tension, viscosity, redox potential, and partition coefficient using appropriate analytical techniques.
CO4: Carry out organic chemistry experiments, including the preparation of iodoform, differentiation between aldehydes and ketones using qualitative tests, and separation of compounds by thin-layer chromatography.
CO5: Synthesize polymers or drugs and evaluate the properties of oils through saponification and acid value determination using standard laboratory methods.
CO6: Record experimental observations, perform calculations, interpret analytical data, and prepare laboratory reports while adhering to good laboratory practices and safety guidelines.

This course introduces fundamental concepts of electrical and electronic engineering, including circuits, electrical machines, semiconductor devices, digital systems, measurements, and practical applications in engineering and technology.

Course Outcome:

At the end of the course the students will be able to:
CO1: To Understand the fundamental concepts of electricity, such as voltage, current, resistance, power, and energy.
CO2: To understand AC and DC fundamentals and measure power factor in given circuit.
CO3: Describe the characteristics and applications of diodes and transistors.
CO4: To understand Electrical safety and explain the construction, working principle, performance and applications of transformers.
CO5: Understand number systems, logic gates, and basic combinational circuits.
CO6: Apply Boolean algebra and Karnaugh maps to simplify and design digital logic circuits.

This laboratory develops practical skills in electrical circuits, measurements, electronic components, semiconductor devices, digital circuits, and basic electrical systems through experiments, testing, analysis, and troubleshooting techniques.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Understand the fundamental concepts of electricity, such as voltage, current, resistance, power, and energy
CO2: Develop the ability to analyze electrical circuits using techniques such as Ohm's law, Kirchhoff's laws, and network theorems.
CO3: Plot the I-V characteristics of various semiconductor devices.
CO4: Design electrical and electronic circuits by utilizing various components.
CO5: Verify the truth tables of various logic gates

This course provides advanced concepts in multivariable calculus, vector calculus, differential equations, Laplace transforms, and numerical methods, equipping students with mathematical techniques for solving engineering problems.

Course Outcome:

At the end of the course, the students will be able to:
CO1: Apply derivative tests in optimization problems appearing in social sciences, physical sciences, life sciences and a host of other disciplines.
CO2: Understand conceptual variations while advancing from one variable to several variables in calculus.
CO3: Find numerical solutions of system of linear equations and check the accuracy of the solutions.
CO4: Solve initial and boundary value problems in differential equations using numerical methods.
CO5: To find and analyze solution of Laplace equation using different numerical methods.

Engineering Drawing covers principles of orthographic projection, sectional views, isometric and pictorial drawings, and an introduction to computer-aided drafting (CAD).

Course Outcome:

At the end of the course, the students will be able to:
CO1: Identify and use engineering drawing instruments, symbols, and conventions.
CO2: Understand and apply projection methods for points, lines, planes, and solids.
CO3: Create accurate 2D drawings using scales and dimensioning techniques.
CO4: Generate isometric (3D) drawings from 2D orthographic views.
CO5: Use basic CAD tools to create and modify engineering drawings digitally.

C++ Programming introduces object-oriented programming concepts including classes, objects, inheritance, polymorphism, and encapsulation, along with function and operator overloading, templates, exception handling, and an introduction to the Standard Template Library (STL) for developing efficient, modular software solutions.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Describe the procedural and object oriented paradigm with concepts of streams, classes, functions, data and objects.
CO2: Understand dynamic memory management techniques using pointers, constructors, destructors, etc.
CO3: Describe the concept of function overloading, operator overloading, virtual functions and polymorphism.
CO4: Understanding inheritance in OOP for code reusability and extensible design.
CO5: Demonstrate the use of various OOPs concepts with the help of programs.

This lab provides hands-on practice in implementing object-oriented programming concepts using C++, including designing classes and objects, applying inheritance and polymorphism, overloading functions and operators, handling exceptions, and using STL containers and algorithms to solve programming problems.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Describe the procedural and object oriented paradigm with concepts of streams, classes, functions, data and objects.
CO2: Understand dynamic memory management techniques using pointers, constructors, destructors, etc.
CO3: Describe the concept of function overloading, operator overloading, virtual functions and polymorphism.
CO4: Understanding inheritance in OOPs for code reusability and extensible design.
CO5: Demonstrate the use of various OOPs concepts with the help of programs.

Universal Human Values, covering self-awareness, love, compassion, truth, non-violence, righteousness, sacrifice, and inner transformation. Students will develop ethical decision-making, empathy, emotional intelligence, self-reflection, and value-based thinking for personal and professional life.

Course Outcome:

CO1: Define and identify core universal human values and relate them to their own lives.
CO2: Demonstrate self-awareness and initiate personal transformation (Human Revolution)
CO3: Appreciate the importance of Sewa (selfless service), compassion, and empathy.
CO4: Apply principles of truth, non-violence, and moral responsibility in real-life dilemmas.
CO5: Evaluate ethical decisions involving sacrifice and righteousness through case studies.
CO6: Reflect on the relevance of renunciation and simplicity in modern life for inner peace.

Communicative English II builds on foundational language skills with emphasis on advanced communication, presentation skills, group discussions, and technical writing.

Course Outcome:

CO1: Able to apply advanced English language skills for effective academic and professional communication.
CO2: Able to communicate ideas, information and opinions confidently through effective speaking and listening skills.
CO3: Able to comprehend, analyze and interpret various forms of written and spoken English.
CO4: Able to prepare effective professional documents such as emails, reports, presentations and resumes.
CO5: Able to demonstrate effective interpersonal, presentation and communication skills in academic and professional situations.

Entrepreneurship Mindset-II covers business planning, understanding the startup ecosystem, funding avenues, and effective pitching of business ideas.

Course Outcome:

CO1: Able to understand and apply entrepreneurial concepts, principles and practices in identifying business opportunities.
CO2: Able to develop an entrepreneurial mindset through creativity, innovation, critical thinking and effective decision-making.
CO3: Able to identify customer needs and evaluate business ideas based on market opportunities and feasibility.
CO4: Able to develop basic business models and strategies for starting and managing entrepreneurial ventures.
CO5: Able to demonstrate leadership, teamwork, risk-taking and problem-solving skills for successful entrepreneurial development.

Frontend Technologies covers the core building blocks of web development, including HTML for structuring content, CSS for styling and responsive layouts, and JavaScript for adding interactivity, along with an introduction to modern frontend frameworks and best practices for building user-friendly web interfaces.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Understanding the fundamentals and evolution of web technologies.
CO2: Mastery of HTML for structuring web content effectively.
CO3: Skill in using JavaScript for client-side scripting and enhancing user experience.
CO4: Competence in designing and validating web forms for data collection.
CO5: Ability to use Bootstrap for responsive and first web design.

This lab offers practical experience in building and styling responsive web pages using HTML and CSS, adding client-side interactivity with JavaScript, and applying modern frontend development practices and tools to create functional, user-friendly web interfaces.

Course Outcome:

At the end of the course, the students will able to be:
CO1: Understand the fundamentals of web technologies, including the structure and function of websites, and differentiate between client-side and server-side scripting.
CO2: Design and develop static web pages using HTML elements such as text, tables, lists, links, frames, and images.
CO3: Create interactive and user-friendly forms using HTML5 form elements and attributes for structured data input.
CO4: Apply Cascading Style Sheets (CSS) to format and layout web pages effectively, using various selectors and properties.
CO5: Use JavaScript and Bootstrap to add interactivity, validate forms, and develop responsive web pages compatible with different devices.

3RD SEMESTER SUBJECTS

Covers fundamentals of database design, the relational model, SQL, normalization, and transaction management.

Course Outcome:

CO1: Understand basic concepts of database systems and the relational data model.
CO2: Design ER diagrams and convert them into relational schemas.
CO3: Formulate queries using SQL and relational algebra.
CO4: Apply normalization techniques to design efficient, redundancy-free databases.
CO5: Understand transaction management, concurrency control, and recovery techniques.
CO6: Explore concepts of indexing and query optimization.

Hands-on lab for designing databases and implementing SQL queries using RDBMS tools.

Course Outcome:

CO1: Create and manipulate databases using DDL and DML commands.
CO2: Write and execute simple to complex SQL queries.
CO3: Implement joins, subqueries, and views.
CO4: Design and implement PL/SQL procedures, functions, and triggers.
CO5: Apply normalization and ER modeling to real-world case studies.

Introduces linear and non-linear data structures along with algorithm design and analysis techniques.

Course Outcome:

CO1: Understand asymptotic notations and analyze the time/space complexity of algorithms.
CO2: Implement linear data structures such as arrays, stacks, queues, and linked lists.
CO3: Implement non-linear data structures such as trees and graphs.
CO4: Apply searching and sorting algorithms to solve computational problems.
CO5: Understand hashing techniques and their applications.
CO6: Design efficient algorithms using divide-and-conquer, greedy, and dynamic programming approaches.

Lab-based implementation of data structures and algorithmic problem-solving using a programming language.

Course Outcome:

CO1: Implement stacks, queues, and linked lists programmatically.
CO2: Implement tree and graph traversal algorithms.
CO3: Implement and compare various sorting and searching techniques.
CO4: Apply data structures to solve real-world computational problems.
CO5: Analyze the time complexity of implemented algorithms.

Covers advanced mathematical techniques including transforms, numerical methods, and probability essential for engineering applications.

Course Outcome:

CO1: Apply Laplace and Fourier transforms to solve engineering problems.
CO2: Solve differential equations using series solutions and special functions.
CO3: Apply numerical methods for solving algebraic and differential equations.
CO4: Understand concepts of probability and statistical distributions.
CO5: Apply vector calculus concepts to engineering problems.

Covers advanced Python programming concepts including OOP, file handling, and modules for real-world application development.

Course Outcome:

CO1: Understand advanced Python programming constructs and best practices.
CO2: Apply object-oriented programming concepts using Python.
CO3: Implement file handling and exception handling in Python applications.
CO4: Use Python modules, packages, and libraries for application development.
CO5: Apply Python for data manipulation and automation tasks.

Hands-on lab for developing Python applications using advanced programming concepts.

Course Outcome:

CO1: Implement object-oriented Python programs.
CO2: Implement file handling and exception handling programs.
CO3: Develop applications using Python libraries and modules.
CO4: Implement automation scripts using Python.
CO5: Develop a mini-project using advanced Python concepts.

Builds entrepreneurial thinking, business planning, and innovation skills through practical exposure.

Course Outcome:

CO1: Understand the entrepreneurial mindset and identify business opportunities.
CO2: Develop a basic business model and plan for a venture idea.
CO3: Understand fundamentals of innovation, risk-taking, and value creation.
CO4: Apply financial and marketing basics to a proposed venture.
CO5: Present and pitch a business idea effectively.

Introduces sensor technologies, signal conditioning, and instrumentation systems for measurement and automation.

Course Outcome:

CO1: Understand fundamentals of sensors and their working principles.
CO2: Classify sensors based on the physical quantities they measure.
CO3: Understand signal conditioning and data acquisition techniques.
CO4: Apply instrumentation concepts to design measurement systems.
CO5: Explore applications of sensors in automation and robotics.

Hands-on lab for interfacing sensors and building basic instrumentation setups.

Course Outcome:

CO1: Interface various sensors with microcontroller-based systems.
CO2: Perform signal conditioning and calibration of sensor outputs.
CO3: Acquire and analyze sensor data using appropriate tools.
CO4: Build a simple sensor-based measurement or automation setup.
CO5: Troubleshoot common issues in sensor-based systems.

Introduces R programming for statistical computing, data analysis, and visualization.

Course Outcome:

CO1: Understand fundamentals of R programming and its data structures.
CO2: Apply data manipulation techniques using R.
CO3: Perform statistical analysis using R functions and packages.
CO4: Create data visualizations using R graphics packages.
CO5: Apply R for basic data analysis projects.

Hands-on lab for data analysis and visualization using R programming.

Course Outcome:

CO1: Implement basic R programs using core language constructs.
CO2: Perform data cleaning and manipulation using R.
CO3: Apply statistical functions to analyze datasets in R.
CO4: Create visualizations using R packages such as ggplot2.
CO5: Develop a mini data analysis project using R.

4TH SEMESTER SUBJECTS

Covers algorithm design paradigms, complexity analysis, and advanced algorithmic techniques for problem-solving.

Course Outcome:

CO1: Analyze the time and space complexity of algorithms using asymptotic notations.
CO2: Design algorithms using divide-and-conquer, greedy, and dynamic programming strategies.
CO3: Apply graph algorithms for shortest path, spanning tree, and network flow problems.
CO4: Understand backtracking and branch-and-bound techniques.
CO5: Analyze NP-completeness and classify problems based on computational complexity.
CO6: Design and evaluate efficient algorithms for real-world computational problems.

Lab-based implementation and performance analysis of algorithm design techniques.

Course Outcome:

CO1: Implement divide-and-conquer algorithms and analyze their performance.
CO2: Implement greedy and dynamic programming based solutions.
CO3: Implement graph algorithms for traversal, shortest path, and spanning trees.
CO4: Implement backtracking algorithms for constraint satisfaction problems.
CO5: Compare and evaluate algorithm efficiency using empirical analysis.

Introduces networking concepts, protocols, and architectures across the OSI and TCP/IP layers.

Course Outcome:

CO1: Understand network architectures, topologies, and the OSI/TCP-IP reference models.
CO2: Analyze data link layer protocols including error detection and correction techniques.
CO3: Understand network layer concepts including routing algorithms and IP addressing.
CO4: Analyze transport layer protocols and congestion control mechanisms.
CO5: Understand application layer protocols and their real-world implementations.
CO6: Explore network security fundamentals and emerging networking technologies.

Hands-on lab for network configuration, simulation, and protocol analysis.

Course Outcome:

CO1: Configure basic networking devices and IP addressing schemes.
CO2: Simulate network topologies using networking tools/simulators.
CO3: Analyze network traffic and protocols using packet capture tools.
CO4: Implement socket programming for client-server communication.
CO5: Troubleshoot common networking issues in a lab environment.

Covers mathematical foundations including set theory, logic, graph theory, and combinatorics for computer science.

Course Outcome:

CO1: Apply propositional and predicate logic to solve reasoning problems.
CO2: Understand set theory, relations, and functions.
CO3: Apply combinatorics and counting principles to solve problems.
CO4: Understand graph theory concepts including trees, graphs, and their applications.
CO5: Apply algebraic structures such as groups and lattices to computer science problems.

Introduces operating system concepts including process management, memory management, and file systems.

Course Outcome:

CO1: Understand the structure, functions, and types of operating systems.
CO2: Apply process scheduling algorithms and understand process synchronization.
CO3: Understand deadlock detection, prevention, and avoidance techniques.
CO4: Apply memory management techniques including paging and segmentation.
CO5: Understand file system organization and disk scheduling algorithms.
CO6: Explore concepts of virtualization and modern operating systems.

Hands-on lab for implementing OS concepts using shell scripting and system calls.

Course Outcome:

CO1: Implement basic Linux/Unix commands and shell scripts.
CO2: Implement process creation and synchronization using system calls.
CO3: Implement CPU scheduling algorithms programmatically.
CO4: Implement memory management techniques such as paging.
CO5: Implement solutions for classical process synchronization problems.

Covers computer system architecture, instruction sets, memory hierarchy, and processor design.

Course Outcome:

CO1: Understand basic computer organization and functional units.
CO2: Analyze instruction set architectures and addressing modes.
CO3: Understand arithmetic and logic unit design and computer arithmetic.
CO4: Understand memory hierarchy including cache and virtual memory.
CO5: Understand pipelining and instruction-level parallelism.
CO6: Explore I/O organization and multiprocessor architectures.

Introduces fundamental concepts of robotics including kinematics, actuators, and robot control.

Course Outcome:

CO1: Understand the classification and components of robotic systems.
CO2: Apply forward and inverse kinematics concepts to robotic manipulators.
CO3: Understand actuators and sensors used in robotic systems.
CO4: Understand basic robot control and programming concepts.
CO5: Explore applications of robotics across various industries.

Hands-on lab for building and programming basic robotic systems.

Course Outcome:

CO1: Assemble and configure basic robotic hardware components.
CO2: Program simple robotic movements and control sequences.
CO3: Interface sensors and actuators with a robot controller.
CO4: Implement basic kinematic calculations for a robotic arm/mobile robot.
CO5: Build and demonstrate a simple robotics mini-project.

Covers automation systems, industrial robotics, and control techniques for automated processes.

Course Outcome:

CO1: Understand fundamentals of automation and industrial robotics.
CO2: Understand control systems used in automated manufacturing.
CO3: Apply PLC and SCADA concepts to automation systems.
CO4: Understand robot programming for industrial applications.
CO5: Explore integration of robotics in smart manufacturing (Industry 4.0).

Hands-on lab for programming and simulating automated and robotic systems.

Course Outcome:

CO1: Program basic automation logic using PLC/simulation tools.
CO2: Implement robotic control sequences for automation tasks.
CO3: Simulate industrial robotic processes using software tools.
CO4: Integrate sensors and actuators into an automated workflow.
CO5: Develop a mini automation/robotics project.

Builds entrepreneurial thinking and practical business skills through case studies and projects.

Course Outcome:

CO1: Understand advanced concepts of entrepreneurship and business growth.
CO2: Analyze case studies of successful startups and ventures.
CO3: Develop strategies for scaling and sustaining a business.
CO4: Apply leadership and team-building concepts to entrepreneurial ventures.
CO5: Present a refined business plan incorporating market feedback.

5TH SEMESTER SUBJECTS

Covers software development life cycle, requirement analysis, design, and project management principles.

Course Outcome:

CO1: Understand software process models and the software development life cycle.
CO2: Apply requirement elicitation and analysis techniques.
CO3: Apply software design principles including modularity and design patterns.
CO4: Understand software testing techniques and quality assurance practices.
CO5: Apply project management and estimation techniques for software projects.
CO6: Understand software maintenance and configuration management.

Introduces formal languages, automata theory, and computability concepts.

Course Outcome:

CO1: Understand finite automata and regular languages.
CO2: Design context-free grammars and analyze pushdown automata.
CO3: Understand Turing machines and their role in computability.
CO4: Classify problems based on decidability and undecidability.
CO5: Understand complexity classes including P and NP.

Covers object-oriented programming concepts and application development using Java.

Course Outcome:

CO1: Understand the history, features, and fundamental elements of Java programming.
CO2: Apply object-oriented concepts including classes, objects, and inheritance.
CO3: Implement interfaces, packages, and exception handling in Java.
CO4: Understand multithreading and concurrent programming in Java.
CO5: Implement GUI applications using AWT/Swing.
CO6: Handle file I/O and stream operations in Java.

Hands-on lab for developing Java applications using object-oriented concepts.

Course Outcome:

CO1: Implement basic Java programs using core language constructs.
CO2: Implement classes, objects, and inheritance-based programs.
CO3: Implement exception handling and multithreading programs.
CO4: Develop GUI-based applications using Java.
CO5: Implement file handling operations in Java.

Covers information security principles, cryptography basics, and cyber law frameworks.

Course Outcome:

CO1: Understand fundamentals of information security and threat models.
CO2: Apply cryptographic techniques for data confidentiality and integrity.
CO3: Understand network and application security mechanisms.
CO4: Understand cyber laws, IT Act provisions, and compliance requirements.
CO5: Analyze case studies of cybercrimes and security breaches.

Covers PLC architecture, programming languages, and their application in industrial automation and control systems.

Course Outcome:

CO1: Understand the architecture and working principles of Programmable Logic Controllers.
CO2: Understand ladder logic and other IEC 61131-3 programming languages.
CO3: Design PLC programs for digital and analog input/output control.
CO4: Apply timers, counters, and sequential logic in PLC programs.
CO5: Understand interfacing of PLCs with sensors, actuators, and HMI systems.
CO6: Explore industrial applications of PLC-based automation.

Hands-on lab for developing and testing PLC programs for industrial control applications.

Course Outcome:

CO1: Implement basic ladder logic programs on a PLC trainer kit.
CO2: Implement timer and counter based control programs.
CO3: Interface PLCs with sensors and actuators for real-time control.
CO4: Design and test sequential control logic for industrial processes.
CO5: Develop a mini-project using PLC-based automation.

Covers principles of automation system design, control architectures, and integration of automation components.

Course Outcome:

CO1: Understand fundamentals of automation and control system architectures.
CO2: Apply sensor and actuator selection principles in automation system design.
CO3: Design control logic for automated processes and systems.
CO4: Understand SCADA and industrial communication protocols.
CO5: Apply system integration principles for automation projects.
CO6: Explore emerging trends in industrial automation and robotics.

Hands-on lab for designing and implementing automation system projects using industrial components.

Course Outcome:

CO1: Configure sensors and actuators for an automated system.
CO2: Implement control logic for automation system components.
CO3: Integrate SCADA/HMI interfaces with automation hardware.
CO4: Test and troubleshoot automation system designs.
CO5: Develop a mini-project on an integrated automation system.

Evaluation of industrial/summer training undertaken by students to assess practical exposure gained.

Course Outcome:

CO1: Demonstrate understanding of the industrial/organizational environment.
CO2: Apply theoretical knowledge to practical, real-world tasks undertaken during training.
CO3: Present and document the training experience through a report and viva-voce.
CO4: Reflect on skills gained and their relevance to career development.

Develops logical reasoning, quantitative aptitude, and analytical problem-solving skills.

Course Outcome:

CO1: Apply logical reasoning techniques to solve analytical problems.
CO2: Solve quantitative aptitude problems involving numbers and arithmetic.
CO3: Apply data interpretation techniques to analyze given data sets.
CO4: Develop problem-solving strategies for competitive examinations.

Advanced module on entrepreneurship focusing on innovation, funding, and venture execution.

Course Outcome:

CO1: Understand advanced funding options and investment readiness for startups.
CO2: Apply innovation management techniques to venture development.
CO3: Understand legal and regulatory aspects of starting a business.
CO4: Develop a comprehensive venture execution plan.
CO5: Present a pitch incorporating financial and operational planning.

6TH SEMESTER SUBJECTS

Covers cryptographic algorithms, network security protocols, and mechanisms to secure data communication.

Course Outcome:

CO1: Understand fundamentals of network security and cryptographic principles.
CO2: Apply symmetric and asymmetric encryption algorithms for data security.
CO3: Understand hash functions, digital signatures, and message authentication.
CO4: Apply key management and public key infrastructure concepts.
CO5: Understand network security protocols such as SSL/TLS, IPSec, and firewalls.
CO6: Analyze security threats and countermeasures in networked systems.

Hands-on lab for implementing cryptographic algorithms and network security techniques.

Course Outcome:

CO1: Implement classical and modern encryption algorithms.
CO2: Implement hashing and digital signature techniques.
CO3: Configure firewalls and basic network security tools.
CO4: Analyze network traffic for security vulnerabilities.
CO5: Implement secure communication using SSL/TLS concepts.

Covers advanced data structures and their applications in efficient algorithm design.

Course Outcome:

CO1: Understand advanced tree structures such as AVL, B-trees, and Red-Black trees.
CO2: Implement advanced graph algorithms and their applications.
CO3: Understand heap structures and priority queue implementations.
CO4: Apply hashing techniques and collision resolution strategies.
CO5: Understand advanced string matching and pattern searching algorithms.
CO6: Analyze the performance trade-offs of advanced data structures.

Lab-based implementation of advanced data structures and their applications.

Course Outcome:

CO1: Implement balanced tree structures such as AVL and B-trees.
CO2: Implement heap-based priority queues.
CO3: Implement advanced graph algorithms programmatically.
CO4: Implement hashing techniques with collision handling.
CO5: Implement string matching algorithms.

Introduces the phases of compiler construction including lexical analysis, parsing, and code generation.

Course Outcome:

CO1: Understand the phases and structure of a compiler.
CO2: Design lexical analyzers using regular expressions and finite automata.
CO3: Design parsers using context-free grammars and parsing techniques.
CO4: Understand syntax-directed translation and intermediate code generation.
CO5: Understand code optimization and target code generation techniques.
CO6: Explore error detection and recovery mechanisms in compilers.

Introduces core AI concepts, search techniques, knowledge representation, and intelligent agent design.

Course Outcome:

CO1: Understand the fundamentals of AI and intelligent agents.
CO2: Apply uninformed and informed search techniques to solve problems.
CO3: Represent knowledge using logic-based and semantic approaches.
CO4: Understand the basics of machine learning and its role in AI systems.
CO5: Apply AI techniques to design simple intelligent systems.
CO6: Understand ethical and societal implications of AI.

Covers data warehouse architecture, OLAP concepts, and data mining techniques for knowledge discovery.

Course Outcome:

CO1: Understand data warehouse architecture and multidimensional data models.
CO2: Apply OLAP operations for data analysis.
CO3: Understand data preprocessing techniques for data mining.
CO4: Apply classification and clustering algorithms for pattern discovery.
CO5: Understand association rule mining techniques.
CO6: Explore applications of data mining in real-world domains.

Introduces ROS architecture, communication mechanisms, and tools for building robotic applications.

Course Outcome:

CO1: Understand the architecture and core concepts of Robot Operating Systems.
CO2: Apply ROS communication mechanisms including nodes, topics, and services.
CO3: Understand packages, workspaces, and the ROS build system.
CO4: Apply simulation and visualization tools such as Gazebo and RViz.
CO5: Implement sensor and actuator integration using ROS.
CO6: Explore real-world robotic applications built using ROS.

Hands-on lab for developing and testing robotic applications using ROS tools and frameworks.

Course Outcome:

CO1: Set up a ROS workspace and create basic packages.
CO2: Implement publisher-subscriber and service-client communication in ROS.
CO3: Simulate robotic systems using Gazebo and visualize data using RViz.
CO4: Integrate sensors and actuators with a ROS-based robotic system.
CO5: Develop a mini-project using ROS for a robotic application.

Introduces machine learning concepts, algorithms, and model evaluation techniques for predictive analytics.

Course Outcome:

CO1: Understand fundamentals of machine learning and types of learning paradigms.
CO2: Apply supervised learning algorithms for regression and classification tasks.
CO3: Apply unsupervised learning algorithms for clustering and pattern discovery.
CO4: Understand model evaluation, validation, and performance metrics.
CO5: Apply ensemble learning and dimensionality reduction techniques.
CO6: Explore real-world applications of machine learning across domains.

Hands-on lab for implementing machine learning algorithms using Python-based tools and libraries.

Course Outcome:

CO1: Implement data preprocessing and feature engineering techniques.
CO2: Implement supervised learning algorithms programmatically.
CO3: Implement unsupervised learning algorithms programmatically.
CO4: Evaluate machine learning models using standard validation techniques.
CO5: Develop a mini-project applying machine learning to a real-world dataset.

Builds advanced logical reasoning, quantitative aptitude, and analytical problem-solving skills.

Course Outcome:

CO1: Apply advanced logical reasoning techniques to complex problems.
CO2: Solve advanced quantitative aptitude problems.
CO3: Apply data sufficiency and interpretation techniques.
CO4: Develop strategies for competitive and placement examinations.

Provides students hands-on experience in identifying, designing, and initiating a substantial project applying learned concepts.

Course Outcome:

CO1: Identify a real-world problem and formulate project objectives.
CO2: Conduct literature review and requirement analysis for the project.
CO3: Design the system architecture and methodology for the project.
CO4: Develop an initial working prototype of the proposed solution.
CO5: Present and document the project progress through reports and reviews.

Focuses on developing communication, interpersonal, and professional workplace skills.

Course Outcome:

CO1: Develop effective verbal and written communication skills.
CO2: Apply interpersonal and teamwork skills in professional settings.
CO3: Understand workplace etiquette and professional ethics.
CO4: Develop resume writing and interview preparation skills.

7TH SEMESTER SUBJECTS

Introduces image processing fundamentals and computer vision techniques for visual data analysis.

Course Outcome:

CO1: Understand fundamentals of digital image processing and representation.
CO2: Apply image filtering, enhancement, and edge detection techniques.
CO3: Understand feature extraction and object detection techniques.
CO4: Apply image segmentation and classification techniques.
CO5: Understand deep learning approaches for computer vision tasks.
CO6: Explore real-world applications of computer vision.

Covers Internet of Things architecture, protocols, and applications for connected devices.

Course Outcome:

CO1: Understand IoT architecture, components, and protocols.
CO2: Apply sensor and actuator interfacing concepts.
CO3: Understand communication protocols used in IoT systems.
CO4: Apply cloud integration concepts for IoT data management.
CO5: Understand IoT security challenges and solutions.
CO6: Explore real-world IoT application domains.

Hands-on lab for building IoT-based projects using microcontrollers, sensors, and connectivity modules.

Course Outcome:

CO1: Interface sensors and actuators with microcontroller boards.
CO2: Implement basic IoT communication protocols.
CO3: Develop IoT applications with cloud data integration.
CO4: Implement basic IoT security measures.
CO5: Build and demonstrate a working IoT-based mini-project.

Introduces principles of designing scalable, reliable, and maintainable software systems.

Course Outcome:

CO1: Understand fundamentals of system design and design trade-offs.
CO2: Apply scalability concepts including load balancing and caching.
CO3: Design database and storage solutions for large-scale systems.
CO4: Understand microservices architecture and distributed system design.
CO5: Design fault-tolerant and highly available systems.
CO6: Apply system design principles to real-world case studies.

Introduces blockchain concepts, architecture, and applications in decentralized systems.

Course Outcome:

CO1: Understand fundamentals of blockchain technology and distributed ledgers.
CO2: Understand consensus mechanisms used in blockchain networks.
CO3: Apply smart contract concepts for decentralized applications.
CO4: Understand cryptocurrency and blockchain-based financial systems.
CO5: Explore blockchain applications beyond cryptocurrency.

Hands-on lab for developing and deploying blockchain-based applications and smart contracts.

Course Outcome:

CO1: Set up a basic blockchain network environment.
CO2: Develop and deploy smart contracts.
CO3: Build a simple decentralized application (DApp).
CO4: Implement basic cryptocurrency transaction simulations.
CO5: Evaluate blockchain applications for real-world use cases.

Introduces research methods, ethics, and technical writing skills for academic and applied research.

Course Outcome:

CO1: Understand fundamentals of research design and methodology.
CO2: Apply literature review and research problem formulation techniques.
CO3: Understand data collection and analysis methods for research.
CO4: Apply research ethics and plagiarism-avoidance practices.
CO5: Develop skills for writing and presenting research papers.

Covers machine vision systems, image acquisition, and inspection techniques used in industrial and robotic applications.

Course Outcome:

CO1: Understand fundamentals of machine vision systems and image acquisition hardware.
CO2: Apply image preprocessing and feature extraction techniques for vision tasks.
CO3: Understand object detection and recognition techniques in machine vision.
CO4: Apply machine vision techniques for industrial inspection and quality control.
CO5: Understand calibration and 3D vision concepts.
CO6: Explore real-world applications of machine vision in automation and robotics.

Hands-on lab for implementing machine vision techniques for inspection and object recognition tasks.

Course Outcome:

CO1: Set up and calibrate a machine vision camera system.
CO2: Implement image preprocessing and feature extraction techniques.
CO3: Implement object detection and recognition using vision libraries.
CO4: Develop an automated inspection application using machine vision.
CO5: Develop a mini-project using machine vision for an industrial/robotic task.

Introduces the design, navigation, and control principles of autonomous mobile robots.

Course Outcome:

CO1: Understand fundamentals of autonomous mobile robot architectures.
CO2: Apply localization and mapping techniques such as SLAM.
CO3: Understand path planning and obstacle avoidance algorithms.
CO4: Apply sensor fusion techniques for robot perception.
CO5: Understand motion control and navigation strategies for mobile robots.
CO6: Explore real-world applications of autonomous mobile robots.

Hands-on lab for implementing navigation, mapping, and control algorithms on mobile robot platforms.

Course Outcome:

CO1: Set up and configure a mobile robot platform for autonomous operation.
CO2: Implement localization and mapping techniques on a mobile robot.
CO3: Implement path planning and obstacle avoidance algorithms.
CO4: Integrate sensor fusion for improved robot perception.
CO5: Develop a mini-project demonstrating autonomous navigation.

Continuation of the capstone project focusing on implementation, testing, and final deployment.

Course Outcome:

CO1: Implement the complete proposed system based on the earlier design.
CO2: Conduct testing and validation of the developed system.
CO3: Refine the solution based on evaluation and feedback.
CO4: Document the complete project methodology, results, and outcomes.
CO5: Present and defend the final project through a viva-voce/demonstration.

Focuses on advanced professional skills including leadership, career readiness, and workplace communication.

Course Outcome:

CO1: Develop advanced presentation and public speaking skills.
CO2: Apply leadership and team management concepts in professional settings.
CO3: Prepare for competitive job interviews and group discussions.
CO4: Understand corporate work culture and professional networking practices.

8TH SEMESTER SUBJECTS

Provides students with industry exposure through a structured training/internship period to apply academic knowledge in a professional environment.

Course Outcome:

CO1: Demonstrate understanding of industry practices, tools, and work culture.
CO2: Apply academic knowledge and skills to real-world industrial tasks.
CO3: Develop professional and technical skills relevant to the chosen domain.
CO4: Present the outcomes of industrial training through a report and viva-voce.

Details	Amount
Programme Fees (per Semester)	₹75000
Examination Fees	₹3000
International Fees (per Year)	$4500

Admissions
Open 2026-27

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B.Tech Computer Science and Engineering with Specialisation in Robotics and Artificial intelligence (AI)

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1ST SEMESTER SUBJECTS

2ND SEMESTER SUBJECTS

3RD SEMESTER SUBJECTS

4TH SEMESTER SUBJECTS

5TH SEMESTER SUBJECTS

6TH SEMESTER SUBJECTS

7TH SEMESTER SUBJECTS

8TH SEMESTER SUBJECTS

fees

Fee Slab

Programme Outcomes

Programme Specific Outcomes

Salient Features

Infrastructure

Admissions Open 2026-27

Know About Us

Entrepreneurship

Development & Skill Enhancement

Experience @ Campus

Campus

about university

Academic Info

Schools

Programs

Admission

Student Life

University Fest

Student Welfare

Placements

IQAC

Research and Development Cell

International

Important-Links

B.Tech Computer Science and Engineering with Specialisation in Robotics and Artificial intelligence (AI)

program-details

Industry Immersion

eligibility criteria

Admission criteria

Duration

related program

Curriculum

1ST SEMESTER SUBJECTS

Applied Physics

Applied Physics Lab

Programming Concepts

Programming Concepts Lab

Engineering Mathematics-I

Workshop Practices

Environmental Studies

Communicative English- I

Entrepreneurship Mindset-I

Open Source Technology

Open Source Technology Lab

2ND SEMESTER SUBJECTS

Applied Chemistry

Applied Chemistry Lab

Basics of Electrical and Electronics Engineering

Basics of Electrical and Electronics Engineering Lab

Engineering Mathematics- II

Engineering Drawing

C++ Programming

C++ Programming Lab

Universal Human Value

Communicative English II

Entrepreneurship Mindset-II

Frontend Technologies

Frontend Technologies Lab

3RD SEMESTER SUBJECTS

Database Management System

Database Management System Lab

Data Structures and Algorithms

Data Structures and Algorithms Lab

Engineering Mathematics-III

Advanced Python Programming

Advanced Python Programming Lab

Entrepreneurship Mindset-III

Sensors and Instrumentation

Sensors and Instrumentation Lab

R Language

R Language Lab

4TH SEMESTER SUBJECTS

Analysis & Design of Algorithms

Analysis & Design of Algorithms Lab

Computer Networks

Computer Networks Lab

Discrete Structures

Operating System

Operating System Lab

Computer Architecture and Organization

Principles of Robotics

Principles of Robotics Lab

Robotics and Automation

Robotics and Automation Lab

Entrepreneurship Mindset-IV

Admissions
Open 2026-27