Course Overview

The Linear Algebra and Univariate Calculus course offers a comprehensive introduction to two fundamental areas of mathematics. It covers topics in Linear Algebra, such as vector spaces, matrices, determinants, linear transformations, eigenvalues, and eigenvectors, which are essential for solving systems of equations and modeling various phenomena. In Univariate Calculus, the course delves into differentiation and integration of single-variable functions, exploring concepts like limits, continuity, and their practical applications in optimization and area under curves. Together, these topics build a strong mathematical foundation for advanced studies in fields like engineering, physics, and data science.

Course Outcomes

• Students will demonstrate a solid understanding of key linear algebra concepts, including vector spaces, matrices, determinants, linear transformations, eigenvalues, and eigenvectors, enabling them to solve systems of equations and analyze data.
• Participants will gain proficiency in differentiating and integrating single-variable functions, understanding limits and continuity, and applying these concepts to real-world problems in optimization and calculating areas under curves.
• Learners will be able to apply linear algebra and calculus techniques to model and solve practical problems in engineering, physics, and data science, showcasing their ability to connect theory with real-world applications.
• Students will enhance their critical thinking and analytical skills, enabling them to approach complex mathematical problems methodically and develop effective solutions.
• Graduates of the course will be well-prepared for further studies in mathematics and related fields, possessing a strong foundational knowledge that supports advanced coursework in engineering, physics, data science, and other technical disciplines.

Course Overview

1. To formulate simple algorithms for arithmetic and logical problems. 2. To test and execute the programs and correct syntax and logical errors. 3. To design code on the basis of logic to solve problems in C programming

Course Outcomes

• Understand computer system elements and a foundational comprehension of algorithms and programming.
• Utilize branching and looping statements to address decision-making programming problems.
• Apply homogeneous derived data types, heterogeneous data types, strings, and functions effectively for programming tasks.
• Demonstrate understanding of pointers and applying their concepts skillfully in programming scenarios.
• Apply file handling concepts in C programming with competence, ensuring effective data management and storage solutions.

Course Overview

This course explores fundamental principles of electrical engineering, including circuit analysis, electromagnetism, and electronic devices, through theoretical study and practical laboratory experiments.

Course Outcomes

• Students will demonstrate a thorough understanding of circuit analysis techniques, including Ohm’s Law, Kirchhoff’s laws, and the analysis of both AC and DC circuits, enabling them to design and troubleshoot electrical circuits effectively.
• Participants will grasp the fundamental principles of electromagnetism, including electric fields, magnetic fields, and electromagnetic induction, and will be able to apply these concepts to real-world engineering problems.
• Learners will gain knowledge of various electronic components, such as resistors, capacitors, diodes, and transistors, and will understand their functions and applications within electrical circuits.
• Students will develop practical laboratory skills through experiments that reinforce theoretical concepts, including the use of laboratory equipment, measurement techniques, and data analysis to evaluate circuit performance.
• Graduates will enhance their critical thinking and problem-solving abilities, enabling them to approach complex electrical engineering challenges methodically and implement effective solutions based on foundational principles.

Course Overview

The "Practical Workshop for Engineers" course provides a hands-on learning experience designed to equip participants with essential skills and knowledge in various engineering practices. This course covers a range of topics, including machining, welding, electronics, and materials testing, allowing participants to gain practical experience with tools, equipment, and techniques used in the engineering field. Through a combination of theoretical instruction and practical exercises, students will learn to apply engineering principles in real-world scenarios, fostering problem-solving skills and teamwork. The course aims to bridge the gap between theoretical knowledge and practical application, preparing engineers for the challenges of their professions.

Course Outcomes

• Students will demonstrate practical skills in using various engineering tools and equipment, including machining tools, welding apparatus, and electronic testing instruments.
• Participants will gain a comprehensive understanding of fundamental engineering processes, including fabrication, assembly, and testing of engineering components.
• Learners will develop the ability to identify and troubleshoot common engineering problems encountered in workshop settings, applying critical thinking to devise effective solutions.
• Students will enhance their teamwork and communication skills through collaborative projects, learning to work effectively with peers in a workshop environment.
• Graduates will understand and implement safety protocols and best practices in a workshop setting, ensuring a safe working environment while conducting engineering tasks.

Course Overview

Course Objectives (Minimum 3) 1. Actively engage in diverse club activities (dance, music, photography, drama, literacy) to foster personal development. 2. Participate enthusiastically in workshops and competitions, enhancing practical skills and competitive spirit. 3. Develop proficiency to represent ADTU effectively in inter-university and national competitions, showcasing leadership and teamwork. 4. Gain insights and skills from industry experts through workshops, enhancing professional competence and career readiness.

Course Outcomes

• Engage actively in diverse club activities such as dance, music, photography, drama, and literacy, fostering personal interests and skills development.
• Participate enthusiastically in workshops and competitions aligned with individual hobbies and interests, enhancing practical learning and competitive spirit.
• Gain proficiency to represent ADTU effectively in inter-university, state, and national level competitions, demonstrating leadership and teamwork.
• Benefit from workshops conducted by industry experts, gaining valuable insights and skills applicable to their respective fields of interest.
• Experience a 360-degree learning approach that integrates academic growth with holistic development, nurturing well-rounded personalities capable of thriving in various professional and social contexts.

Course Overview

Introduction to Java is going to offer various knowledge on Java object Oriented Programming with Javac and Java Command

Course Outcomes

• Variables concepts on data variable
• Class and Objects Concepts
• Loop statements in Java and Polymorphism concepts
• Inheritance Concepts
• Abstraction concepts

Course Overview

This course provides a comprehensive exploration of the ethical considerations and social implications associated with the rapid advancements in technology and engineering. Students will engage with theoretical frameworks, case studies, and practical applications to develop a nuanced understanding of the ethical responsibilities inherent in designing, developing, and deploying technology. The course emphasizes critical thinking, responsible decision-making, and a holistic approach to addressing the ethical challenges posed by emerging technologies.

Course Outcomes

• Students should be able to articulate and apply major ethical frameworks to analyze and evaluate the moral implications of technological and engineering decisions.
• Develop the ability to critically assess the ethical dimensions of technology and engineering projects, and make informed, ethically sound decisions in complex situations
• Gain expertise in designing and implementing technologies with a strong focus on privacy, security, and protection of user data.
• Understand the importance of diversity in technology development, and appreciate how biases can impact the design and implementation of technology.
• Acquire knowledge and skills in sustainable design practices, and understand the environmental impact of technology, with the ability to propose eco-friendly solutions.
• Students should be able to articulate and apply major ethical frameworks to analyze and evaluate the moral implications of technological and engineering decisions.
• Develop the ability to critically assess the ethical dimensions of technology and engineering projects and make informed, ethically sound decisions in complex situations.
• Gain expertise in designing and implementing technologies with a strong focus on privacy, security, and the protection of user data.
• Understand the importance of diversity in technology development, and appreciate how biases can impact the design and implementation of technology.
• Acquire knowledge and skills in sustainable design practices, and understand the environmental impact of technology, with the ability to propose eco-friendly solutions.

Course Overview

This extracurricular course offers students an opportunity to delve into the fascinating world of emerging technologies and their societal impact. Participants will explore cutting-edge advancements in various fields, from artificial intelligence to biotechnology, and engage in thought-provoking discussions on the ethical, social, and economic implications of these technologies. The course aims to inspire curiosity, critical thinking, and informed perspectives on the role of innovation in shaping our future.

Course Outcomes

• Develop a comprehensive understanding of various emerging technologies, including their underlying principles, applications, and potential impact on society.
• Cultivate critical thinking skills to analyze the ethical, social, and economic implications of emerging technologies, enabling informed perspectives and discussions.
• Gain a heightened awareness of ethical considerations associated with innovative technologies and develop the ability to make ethically sound decisions in a rapidly changing technological landscape.
• Assess the societal impact of emerging technologies, exploring how they shape cultural norms, influence interpersonal relationships, and contribute to broader social changes.
• Acquire insights into the entrepreneurial opportunities and challenges within the tech landscape, fostering an entrepreneurial mindset and an understanding of innovation in a startup context.

Course Overview

This course is mainly about the methods for the solution of such equations. The focus of attention is on the methods applicable to a class of equations, and also on those specialized for an individual equation with important applications applications.

Course Outcomes

• solve first order differential equations arising in various engineering fields
• evaluate linear differential equations of higher order and use the knowledge to study certain problems that arise in engineering
• illustrate the techniques of Laplace transform to solve problems that arise engineering
• summarize various concepts of vector differentiation
• use calculus to vector functions and interpret vector integral theorems

Course Overview

To impart a sound knowledge on the principles of chemistry involving the different application oriented topics required for all engineering branches.

Course Outcomes

• Understand the principles of water characterization and treatment for portable and industrial purposes.
• To impart knowledge on the essential aspects of Principles of polymer chemistry and engineering applications of polymers.
• Having a sound knowledge in the Field of the Conventional and non-Conventional energy.
• To impart knowledge on the essential aspects of electrochemical cells, emf and applications of emf measurements.
• To make the students understand the Principles of corrosion and corrosion control.

Course Overview

This course is intended to develop anunderstanding of electrical and Electronics models and its analysis at Basic Electrical components, Basic Circuit Designing; correction elements. The course also indulges semiconductor fabrication, and different types of communication systems.

Course Outcomes

• Apply the laws in the analysis of DC and AC circuits.
• Explain the construction and operation of transformers, DC generators and motors.
• Understand the construction and operation of induction motors, and synchronous generators.
• Overview of the semiconductor fabrication.
• Outline the principle of an electronic instrumentation system and explains about principle of radio and cellular communication system.

Course Overview

This course offers designing and developing applications involving Object Oriented Programming concepts such as inheritance, association, aggregation, composition, polymorphism, abstract classes and interfaces using Java. It also covers designing and building multi-threaded Java Applications etc.

Course Outcomes

• Students will be able to understand object-oriented programming concepts and implement in java.
• Students will be able to comprehend building blocks of OOPs language, inheritance, package and interfaces, and analyse real-world problems in terms of these
• Students will be able to identify exception handling methods.
• Students will be able to develop interactive as well as GUI-based java applications in project-based learning
• Illustrate among students the inheritance concepts for reusing the program

Course Overview

This immersive and experiential course offers students a unique opportunity to delve into the realm of computer networking fundamentals through a fully practical and outreach-oriented approach. Grounded in real-world applications and scenarios, participants will engage in hands-on activities, field exercises, and community outreach initiatives to develop a comprehensive understanding of the core concepts and practices in computer networking.

Course Outcomes

• Demonstrate proficiency in configuring, managing, and troubleshooting network devices and protocols.
• Apply theoretical knowledge to real-world networking scenarios, including network design, implementation, and optimization.
• Collaborate effectively in team-based projects and community outreach initiatives related to computer networking.
• Apply technical concepts and solutions clearly and effectively to diverse problems.
• Analyze and evaluate the impact of networking technologies on individuals, communities, and society at large.

Course Overview

The objective of the Digital Electronics course is to provide students with a fundamental understanding of digital circuits, logic design, and their applications in modern electronic systems

Course Outcomes

• students will be able to explain the basic concepts of digital electronics, including binary systems, logic gates, and number systems.
• Students will demonstrate the ability to design, analyze, and implement combinational and sequential logic circuits using standard digital components.
• Students will use simulation software to model and verify the functionality of digital circuits before physical implementation.
• Students will gain the skills to integrate microcontrollers into digital systems, understanding their role in control and automation.
• Students will develop techniques for troubleshooting and testing digital circuits, ensuring proper functionality and performance in practical applications.

Course Overview

Functional Programming in Python explores core concepts like immutability, first-class functions, and recursion. Learn to write clean, efficient, and reusable code using Python's functional programming features and libraries.

Course Outcomes

• Explain the fundamental concepts of functional programming and how they differ from other programming paradigms.
• Use Python's functional programming constructs (e.g., higher-order functions, lambda expressions, and recursion) to solve real-world problems.
• Identify and analyze the advantages and limitations of functional programming in Python compared to imperative programming.
• Critically evaluate the efficiency and readability of functional code by applying functional programming techniques to optimize solutions.
• Design and implement complex Python applications using functional programming principles, demonstrating proficiency in leveraging Python's functional libraries and built-in functions.

Course Overview

Data Structure and Algorithm explores fundamental concepts such as arrays, linked lists, stacks, queues, trees, graphs, sorting, and searching algorithms, providing a strong foundation for efficient problem-solving and programming.

Course Outcomes

• Understand and describe the fundamental data structures (arrays, linked lists, stacks, queues, trees, graphs) and their applications.
• Apply appropriate data structures to solve real-world problems effectively and efficiently.
• Analyze the time and space complexities of various algorithms to assess their efficiency.
• Evaluate different algorithmic approaches for problem-solving, demonstrating the ability to select the optimal solution.
• Design and implement data structures and algorithms to address complex computational problems.

Course Overview

The course explores global leadership strategies, cultural dynamics, and organizational behavior, equipping students with skills to navigate diverse work environments and enhance team effectiveness in an international context.

Course Outcomes

• Students will be able to explain various leadership styles and organizational behaviors in a global context.
• Students will be able to assess how cultural dimensions influence team dynamics and leadership effectiveness.
• Students will demonstrate the ability to implement appropriate leadership strategies in diverse organizational settings.
• Students will critique leadership practices based on case studies and recommend improvements.
• Students will design an actionable leadership plan that addresses the unique challenges faced by global organizations, incorporating best practices and theoretical frameworks.

Course Overview

Computer Organization and Architecture" course explores the fundamental concepts of computer systems, covering hardware components, data representation, instruction sets, memory hierarchy, CPU design, input/output mechanisms, and performance optimization techniques.

Course Outcomes

• Understand the fundamental concepts of computer organization and architecture, including data representation, machine instructions, and addressing modes.
• Analyze the functional units of a computer system, such as the CPU, memory, and input/output devices, to comprehend their roles in computer operation
• Apply principles of digital logic to design and evaluate basic combinational and sequential circuits used in computer architecture.
• Evaluate different types of memory hierarchies and cache organizations, assessing their impact on system performance.
• Design simple CPU models by incorporating control unit, ALU, and data paths, demonstrating knowledge of instruction execution and pipelining concepts.

Course Overview

This course introduces the fundamental concepts of probability and statistics, covering topics such as probability theory, distributions, hypothesis testing, estimation, and regression analysis, with practical applications in various fields.

Course Outcomes

• Explain the fundamental concepts of probability, random variables, and probability distributions to establish a foundational understanding of statistical principles.
• Apply probability laws and theorems to solve real-world problems involving uncertainty and random phenomena.
• Analyze data sets using various statistical techniques, including measures of central tendency, dispersion, and correlation, to draw meaningful insights.
• Evaluate hypotheses using statistical inference methods such as hypothesis testing, confidence intervals, and regression analysis to make informed decisions.
• Design and develop probabilistic models to simulate complex systems and predict outcomes, demonstrating the ability to synthesize statistical knowledge for problem-solving.

Course Overview

Field-based training offers hands-on experience in a real-world environment, bridging theory with practice. It enhances practical skills, problem-solving, and adaptability, providing learners with valuable, industry-relevant knowledge and experience.

Course Outcomes

• Explain the fundamental principles and concepts applied in real-world field-based activities to demonstrate an understanding of practical applications.
• Apply theoretical knowledge to solve practical problems encountered during field training sessions.
• Analyze the effectiveness of various techniques and methods used during fieldwork, identifying strengths, weaknesses, and areas for improvement.
• Evaluate the outcomes of field-based projects, comparing expected results with actual outcomes, and justify any deviations.
• Design and implement a field-based project or activity that integrates learned concepts

Course Overview

This course is designed to develop skills and interests through participation in diverse extracurricular and co-curricular activities, learn about teamwork and leadership abilities by engaging students in club-led events and competitions, and to provide opportunities for personal growth and practical learning beyond the academic curriculum.

Course Outcomes

• Explore different activities organized by various clubs, such as dance, music, photography, drama, and literacy.
• Develop confidence to participate in regular club activities, including workshops and competitions, according to individual interests.
• Apply knowledge and skills to represent ADTU in inter-university, state, and national level competitions.
• Explore new platform to learn from invited experts in their respective fields.
• Evaluate overall growth alongside academic development.

Course Overview

This course provides a comprehensive introduction to the principles, processes, and practices of software engineering. Students will explore the Software Development Life Cycle (SDLC), covering phases from requirement engineering to design, development, testing, and maintenance. The curriculum emphasizes scalable, maintainable software design principles, object-oriented design, and software architecture styles.

Course Outcomes

• Demonstrate an understanding of the importance of software engineering and its impact on software development projects.
• Evaluate software development life cycle models to identify their strengths and weaknesses and make informed decisions when choosing a model for a project.
• Develop software requirements specifications that accurately capture the functional and non-functional requirements of a software application.
• Design software applications using object-oriented design principles and software architecture styles to create efficient, maintainable, and scalable applications.
• Analyze software testing techniques and apply them effectively to ensure the quality of software applications.

Course Overview

This course provides an in-depth exploration of Artificial Intelligence (AI), its principles, methodologies, and applications. It covers fundamental topics such as machine learning, natural language processing, computer vision, robotics, and knowledge representation. Students will gain hands-on experience with AI algorithms and tools, learning to develop intelligent systems capable of solving complex real-world problems.

Course Outcomes

• Have basic knowledge representation, problem solving, and learning methods of artificial intelligence
• Provide the apt agent strategy to solve a given problem
• Represent a problem using first order and predicate logic
• Design applications like expert systems and chat-bot
• Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem

Course Overview

The Operating System (OS) course provides a comprehensive introduction to the principles, design, and implementation of modern operating systems. Students explore the critical role of the OS as an interface between hardware and software, enabling efficient and secure resource management. Key topics include process management, memory management, file systems, I/O systems, and synchronization mechanisms.

Course Outcomes

• Analyze operating system evolution, types, services, and structures including UNIX and WINDOWS.
• Analyze process concepts, relationships, and states, examine thread advantages, and appraise CPU scheduling objectives, algorithms, and criteria. .
• Evaluate IPC mechanisms and solutions for classical problems; assess deadlock conditions and use prevention, avoidance, detection, and recovery techniques.
• Evaluate memory management concepts, including mapping, allocation strategies, paging, virtual memory principles, and analyse page fault/replacement algorithms.
• Analyze I/O hardware, device controllers, DMA principles, and I/O software, secondary-storage structures, disk scheduling algorithms, file management concepts, allocation methods, and directory implementations.

Course Overview

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Course Outcomes

Course Overview

-This course provides a comprehensive understanding of Database Management Systems (DBMS). It covers the fundamental concepts, design principles, and practical applications of databases. Students will gain skills to design, implement, and manage databases effectively.

Course Outcomes

• -Understand fundamental concepts of database management systems, utilizing DBMS languages and data models effectively.
• -Implement query languages to design relational databases for practical application of database design principles.
• -Execute transaction processing with database recovery, applying ACID properties and concurrency control schemes proficiently.
• -Ensure database security through authentication and authorization processes, demonstrating competence in securing database systems.
• -Apply advanced concepts in data warehousing, data mining, distributed databases, and web databases for handling complex database scenarios.

Course Overview

This course presents several basic ideas in automata theory and formal languages, such as grammar, finite automaton, regular expression, formal language, pushdown automaton, and Turing machine. This course not only contains the fundamental models of computation but also the building blocks of numerous subfields of computer science, such as concurrent systems, software engineering, compilers, etc.

Course Outcomes

• Able to understand the concept of abstract machines and their power to recognize the languages.
• Able to employ finite state machines for modelling and solving computing problems
• Able to design context free grammars for formal languages
• Able to distinguish between decidability and undecidability
• Able to gain proficiency with mathematical tools and formal methods.

Course Overview

This course offers designing and developing applications involving Object Oriented Programming concepts such as inheritance, association, aggregation, composition, polymorphism, abstract classes and interfaces using Java. It also covers designing and building multi-threaded Java Applications etc.

Course Outcomes

• Students will be able to understand object-oriented programming concepts and implement in java.
• Students will be able to comprehend building blocks of OOPs language, inheritance, package and interfaces, and analyze real-world problems in terms of these.
• Students will be able to realize and relate concepts like multithreading, exception handling etc. in object-oriented programs.
• Students will be able to identify exception handling methods.
• Students will be able to develop interactive as well as GUI-based java applications in project-based learning.

Course Overview

1. Introduce the field of artificial neural networks and deep learning algorithms, the problem settings, and their applications to solve real world problems. 2. Understand the motivation for different neural network architectures and select the appropriate architecture for a given problem 3. Promote further deep learning on the topics of artificial neural networks and deep learning

Course Outcomes

• Learn about basic concept of artificial neuron and human brain.
• Understand the concepts different classes of neural network.
• Learn the algorithm feed forward network.
• To become familiar with the concepts of deep learning.
• Understand the concept of associative memory.

Course Overview

-The objective of this course is to familiarize the students with the basic concept of data and to work with various kinds of data and statistical data. It aims to presents a perfect blend of machine learning, big data analytics, and statistics, the programme helps you gain experience in solving problems with real-world data.

Course Outcomes

• -Understanding the data and data science
• -Analyzing the data and data life cycle
• --Methods of preparing the data for processing
• -Algorithms of Machine learning methods and using
• -Introduction to python programming

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Course Outcomes

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Course Overview

Compiler Design is essential for anyone involved in the development of programming languages, compilers, or related tools. It provides a deep understanding of how programming languages are translated into executable code and how optimization techniques can be applied to enhance program performance. This knowledge is valuable for compiler engineers, language designers, and anyone interested in low-level programming and system-level development.

Course Outcomes

• Understand the different phases of a compiler and explain lexical analysis principles.
• Apply parsing techniques to build a language-specific syntax analyser.
• Analyse syntax-directed translations, evaluation order, and type checking.
• Understand the association of runtime storage allocation with control flow and procedure calls.
• Apply code optimization on intermediate code for target code generation.

Course Overview

This course provides an in-depth exploration of Artificial Intelligence (AI), its principles, methodologies, and applications. It covers fundamental topics such as machine learning, natural language processing, computer vision, robotics, and knowledge representation. Students will gain hands-on experience with AI algorithms and tools, learning to develop intelligent systems capable of solving complex real-world problems.

Course Outcomes

• . Have basic knowledge representation, problem solving, and learning methods of artificial intelligence
• Provide the apt agent strategy to solve a given problem
• Represent a problem using first order and predicate logic
• Design applications like expert systems and chat-bot
• Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem

Course Overview

Machine Learning is a field of artificial intelligence that focuses on developing algorithms that can learn from and make predictions on data. The course covers fundamental concepts of machine learning, including supervised and unsupervised learning techniques, model evaluation, and practical implementation using Python. Students will learn to develop and deploy machine learning models for real-world applications while understanding the theoretical foundations and mathematical concepts behind these algorithms.

Course Outcomes

• Understand the basic concepts and types of machine learning algorithms
• Apply appropriate machine learning techniques to solve real-world problems.
• Apply unsupervised learning techniques for clustering and dimensionality reduction
• Develop skills in data preprocessing and feature engineering.
• Design and deploy machine learning solutions for real-world problems.

Course Overview

This course provides a comprehensive introduction to the MATLAB® technical computing environment. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

Course Outcomes

• Learn to summarize the basics of MATLAB.
• Learn to divide a complex task up into smaller, simpler tasks.
• Learn to apply basic flow controls (if-else, for, while).
• Learn to analyze program scripts and functions using the MATLAB development environment.
• Learn to generate results and analyze.

Course Overview

Digital image processing deals with processing of images which are digital in nature. Study of the subject is motivated by three major applications. The first application is in improvement of pictorial information for human perception i.e. enhancing the quality of the image so that the image will have a better look. The second is for autonomous machine applications which have wider applications in industries, particularly for quality control in assembly automation and many similar applications. This course will introduce various image processing techniques, algorithms and their applications.

Course Outcomes

• Learn to describe the theoretical foundation for image processing methods using various image transforms.
• Learn to apply various categories of filters to enhance and restore images in various applications.
• Learn to discuss various color transformation models for different processing techniques on color images.
• Learn to analyze an image by detecting the isolated points, edge and boundary parameters for segmenting images.
• Learn to demonstrate feature extraction of images and pattern classification using boundary, Region and Principal Component descriptors.

Course Overview

.Nanotechnology is an interdisciplinary field that deals with the manipulation and control of matter on an atomic and molecular scale, typically below 100 nanometers. This course introduces the fundamental concepts, principles, and applications of nanotechnology across various scientific and engineering domains. Students will explore the unique properties of nanomaterials, fabrication methods, characterization techniques, and their applications in industries such as electronics, healthcare, energy, and the environment.

Course Outcomes

• Demonstrate a comprehensive understanding of the fundamental principles and history of nanotechnology, emphasizing its interdisciplinary nature.
• Analyze and explain the unique physical, chemical, and mechanical properties of nanomaterials and their implications for real-world applications.
• Apply various synthesis and characterization techniques to create and study nanostructures, demonstrating proficiency in laboratory methodologies.
• Evaluate the applications of nanotechnology across industries such as electronics, healthcare, energy, and the environment, proposing innovative solutions to contemporary challenges.
• Assess the ethical, environmental, and societal impacts of nanotechnology, ensuring responsible research and development practices in the field.

Course Overview

Data Mining is the process of discovering patterns, trends, and useful information from large datasets. This course provides a comprehensive introduction to the concepts, techniques, and tools used in data mining. Students will learn how to preprocess data, identify patterns, and apply data mining methods to real-world problems in domains like business, healthcare, and social sciences. The course also explores recent advances in the field and ethical considerations surrounding data mining practices.

Course Outcomes

• Demonstrate knowledge of the fundamental concepts and methodologies of data mining and knowledge discovery.
• Apply data preprocessing techniques to clean, transform, and prepare datasets for mining tasks.
• Implement and evaluate data mining algorithms such as classification, clustering, and association rule mining using appropriate tools.
• Analyze and interpret data mining results to provide actionable insights in real-world scenarios.
• Address ethical issues, including privacy and bias, while adhering to legal and regulatory standards in data mining practices.

Course Overview

The course on Intellectual Property Rights (IPR) and Cyber Law provides an understanding of the legal frameworks that govern intellectual property and cyberspace. It equips students with knowledge of IPR concepts, the processes of patenting and copyright, and the implications of trademark protection. Additionally, the course covers the fundamentals of cyber law, focusing on legal issues related to internet use, data protection, cybercrimes, and electronic transactions. Students will gain insights into the role of law in fostering innovation, protecting rights, and maintaining cybersecurity in a digital world.

Course Outcomes

• Demonstrate an understanding of Intellectual Property Rights (IPR) and their importance in fostering innovation and protecting creativity.
• Apply legal frameworks and processes for patents, copyrights, trademarks, and trade secrets effectively.
• Identify and address legal issues related to cybercrimes, data protection, and electronic transactions under cyber law frameworks.
• Analyze real-world cases of IPR infringement and cyber law violations to recommend appropriate legal actions.
• Evaluate the ethical and societal implications of IPR and cyber law, staying informed about global regulations and emerging technologies.

Course Overview

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.

Course Outcomes

• Learn about basic concept of artificial neuron and human brain.
• Understand the concepts different classes of neural network.
• Learn the algorithm feed forward network.
• To become familiar with the concepts of deep learning.
• Understand the concept of associative memory.
• Learn about basic concept of artificial neuron and human brain.
• Understand the concepts different classes of neural network.
• Learn the algorithm feed forward network.
• To become familiar with the concepts of deep learning.
• Understand the concept of associative memory.