B. Tech. CSE (AI & DS)
| III SEM | BS-CS-AIDS-201A :Mathematics for Big Data & Optimization |
| CO1 | Demonstrate understanding of basic mathematical concepts in data science, relating to linear algebra, probability, and calculus |
| CO2 | Employ methods related to these concepts in a variety of data science applications |
| CO3 | Apply logical thinking to problem-solving in context. |
| CO4 | Use appropriate technology to aid problem-solving and data analysis |
| III SEM | PC-CS-AIDS- 203A: Object-Oriented Programming |
| CO1 | To introduce the basic concepts of object oriented programming language and the its representation. |
| CO2 | To allocate dynamic memory, access private members of class and the behavior of inheritance and its implementation. |
| CO3 | To introduce polymorphism, interface design and overloading of operator. |
| CO4 | To handle backup system using file, general purpose template and handling of raised exception during programming. |
| III SEM | PC-CS-AIDS- 205A: Data Structures & Algorithms |
| CO1 | To introduce the basic concepts of Data structure , basic data types ,searching and sorting based on array data types. |
| CO2 | To introduce the structured data types like Stacks and Queue and its basic operations’ implementation. |
| CO3 | To introduce dynamic implementation of linked list. |
| CO4 | To introduce the concepts of Tree and graph and implementation of traversal algorithms. |
| III SEM | PC-CS-AIDS- 207A: Introduction to Artificial Intelligence |
| CO1 | Demonstrate fundamental understanding of Artificial Intelligence (AI) and its foundation |
| CO2 | Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning |
| CO3 | Demonstrate proficiency in applying scientific method to models of machine learning |
| CO4 | Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications |
| III SEM | PC-CS-AIDS- 209A: Programming Languages |
| CO1 | To introduce the basic concepts of programming language, the general problems and methods related to syntax and semantics. |
| CO2 | To introduce the structured data objects, subprograms and programmer defined data types. |
| CO3 | To outline the sequence control and data control. |
| CO4 | To introduce the concepts of storage management using programming languages. |
| III SEM | HM-902: Business Intelligence and Entrepreneurship |
| CO1 | Students will be able understand who the entrepreneurs are and what competences needed to become an Entrepreneur. |
| CO2 | Students will be able understand insights into the management, opportunity search, identification of a Product; market feasibility studies; project finalization etc. required for small business enterprises. |
| CO3 | Students can be able to write a report and do oral presentation on the topics such as product identification, business idea, export marketing etc. |
| CO4 | Students will be able to know the different financial and other assistance available for the small industrial units. |
| III SEM | PC-CS-AIDS- 213LA: Data Structure & Algorithms Lab |
| CO1 | Implement linear and non linear data structures using linked list. |
| CO2 | Apply various data structures such as stack, queue and tree to solve the problems. |
| CO3 | Implement various searching and sorting techniques. |
| CO4 | Choose appropriate data structure while designing the applications and analyze the complexity of the algorithms. |
| III SEM | PC-CS-AIDS- 215LA: Artificial Intelligence Lab |
| CO1 | To understand the basic concepts of Artificial Intelligence. |
| CO2 | To apply various AI Search algorithms. |
| CO3 | To understand the fundamentals of knowledge representation and theorem proving using AI tools. |
| CO4 | Ability to apply knowledge representation and machine learning techniques to real life problems. |
| III SEM | PC-CS-AIDS- 217LA: Object Oriented Programming Lab |
| CO1 | Implement object oriented concepts such as objects, classes abstraction and message passing. |
| CO2 | Implement the friend function, function overloading and virtual function |
| CO3 | Implement Operator overloading, Inheritance and method overriding. |
| CO4 | Implement the various functions on String and apply I/O operation to handle file system |
| IV SEM | BS-AIDS- 202A: Bayesian Data Analysis |
| CO1 | Demonstrate fundamental understanding of Bayesian Inference and models |
| CO2 | Understand and apply Bayesian statistics, posterior inference and decision analysis for making Bayesian models. |
| CO3 | Demonstrate Computation, approximation and simulating from probability distributions in Bayesian analysis |
| CO4 | Understand Bayesian forms of the standard statistical models |
| IV SEM | PC-CS-AIDS- 204A: Data Science and R Programming |
| CO1 | Basics of Data Science, Explain basic Statistics. Identify probability distributions commonly used as foundations for statistical modeling. Fit a model to data. |
| CO2 | Using R to carry out basic statistical modeling and analysis. |
| CO3 | Explain the significance of exploratory data analysis (EDA) in data science. Apply basic tools (plots, graphs, summary statistics) to carry out EDA. |
| CO4 | Describe the Data Science Process and how its components interact via machine learning models. |
| IV SEM | ES-CS-AIDS-206A: Intelligent Communication Systems |
| CO1 | To be able to understand the theoretical basis of communication system process |
| CO2 | Demonstrate the concept of Information theory and describe various sources available to transfer the information. |
| CO3 | To have the in depth knowledge of the Communication Network Structure and study various protocols. |
| CO4 | To deal with the practical aspect of intelligent communication system. |
| IV SEM | PC-CS-AIDS- 208A: Internet & Web technology |
| CO1 | Learn the basic concepts of information and web architecture. |
| CO2 | Learn about the skills that will enable to design and build high level web enabled applications. |
| CO3 | Understand the applicability of Java Script as per current software industry standards. |
| CO4 | Acquaint the latest programming language for the implementation of object based and procedure based applications using Python. |
| IV SEM | PC-CS-AIDS- 210A: Data Base Management Systems |
| CO1 | To provide introduction to relational model. |
| CO2 | To learn about ER diagrams and SQL. |
| CO3 | To understand about the concept of functional dependencies. |
| CO4 | To understand about Query Processing and Transaction Processing. |
| IV SEM | PC-CS-AIDS- 212A: OPERATING SYSTEMS |
| CO1 | To understand the structure and functions of Operating system. |
| CO2 | To learn about processes, threads and scheduling algorithms. |
| CO3 | To understand the principle of concurrency. |
| CO4 | To understand the concept of deadlocks. |
| CO5 | To learn various memory management schemes. |
| CO6 | To study I/O management and file systems. |
| CO7 | To study the concept of protection and security. |
| IV SEM | PC-CS-AIDS- 214LA: R Lab |
| CO1 | Install and use R for simple programming tasks. Extend the functionality of R by using add-on packages. |
| CO2 | Extract data from files and other sources and perform various data manipulation tasks on them. 4. Code statistical functions in R. |
| CO3 | Use R Graphics and Tables to visualize results of various statistical operations on data . |
| CO4 | Apply the knowledge of R gained to data Analytics for real life applications. |
| IV SEM | ES-CS-AIDS- 216LA: Internet & Web Technology Lab |
| CO1 | Design webpages using HTML, JavaScript and CSS. |
| CO2 | Design and test simple function/program to implement Searching and sorting techniques using Python. |
| CO3 | Develop program in Java Script for pattern matching using regular expressions and errors in scripts. |
| CO4 | Design client-server based web applications. |
| IV SEM | PC-CS-AIDS- 218LA: Database Management Systems Lab |
| CO1 | To understand& Implement basic DDL commands. |
| CO2 | To learn & Implement DML and DCL commands. |
| CO3 | To understand the SQL queries using SQL operators. |
| CO4 | To understand the concept of relational algebra and implement using examples. |
| IV SEM | MC-901A: Environmental Sciences |
| CO1 | The students will be able to learn the importance of natural resources. |
| CO2 | To learn the theoretical and practical aspects of eco system. |
| CO3 | Will be able to learn the basic concepts of conservation of biodiversity. |
| CO4 | The students will be able to understand the basic concept of sustainable development. |
| V SEM | PC-CS-AIDS-301A:Theory of Computation |
| CO1 | Students are able to explain and manipulate the different fundamental concepts in automata theory and formal languages. |
| CO2 | Simplify automata and context-free grammars; Prove properties of languages, grammars and automata with rigorously formal mathematical methods, minimization. |
| CO3 | Differentiate and manipulate formal descriptions of push down automata, its applications and transducer machines. |
| CO4 | To understand basic properties of Turing machines and computing with Turing machine, the concepts of tractability and decidability. |
| V SEM | PC-CS-AIDS-303A: Design and Analysis of Algorithms |
| CO1 | To introduce the basic concepts of Data Structures and their analysis. |
| CO2 | To study the concept of Dynamic Programming and various advanced Data Structures. |
| CO3 | To introduce various Graph algorithms and concepts of Computational complexities. |
| CO4 | To study various Flow and Sorting Networks |
| V SEM | PC-CS-AIDS-313LA: Artificial Neural Networks Lab |
| CO1 | Implement cognitive tasks and processing of sensorial data such as vision, image- And speech recognition, control, robotics, expert systems. |
| CO2 | Design single and multi-layer feed-forward neural networks |
| CO3 | Understand and implement supervised and unsupervised learning concepts & Understand unsupervised learning using Kohonen networks |
| CO4 | Implement training of recurrent Hop field networks and associative memory concepts. |
| V SEM | ES-CS-AIDS-305A: Computer Network |
| CO1 | To understand the basic concept of networking, types, networking topologies and layered architecture. |
| CO2 | To understand datalink layer and MAC sub-layer` |
| CO3 | To understand the network Layer functioning |
| CO4 | To understand the transport layer and application layer operation |
| V SEM | PC-CS-AIDS-307A: Machine Learning with using Python |
| CO1 | Understand basics of Python programming language. |
| CO2 | Explain the operation of different supervised and unsupervised algorithms and their implementation in Python. |
| CO3 | Implement several clustering, classification and regression algorithms, and apply a suitable learning algorithm to arrange of basic problems. |
| CO4 | Work on Recommender Systems: Content-Based and Collaborative Filtering |
| CO5 | Use and Analyze Popular models: Train/Test Split, Gradient Descent, and Mean Squared Error and perform custom analysis |
| CO6 | Apply predictions and segmentation on real-world datasets. Interpret the output and validity of a learning algorithm. |
| V SEM | ES-CS-AIDS-309A: Computer Architecture |
| CO1 | Be familiar with the internal organization and operations of a computer. |
| CO2 | Be familiar with the design trade‐offs in designing and constructing a computer processor. |
| CO3 | Be aware with the CPU design including the RISC/CISC architectures. |
| CO4 | Be acquainted with the basic knowledge of I/O devices and select the appropriate interfacing standards for I/O devices. |
| V SEM | PC-CS-AIDS-311A: Artificial Neural Networks |
| CO1 | Understand basic principles of neuron structure. |
| CO2 | Understand and explain the mathematical foundations of neural network models |
| CO3 | Understand and apply the methods of training neural networks; |
| CO4 | Implement and analyze different algorithms for learning. |
| CO5 | Formalize the problem to solve it by using a neural network. Via implementation of these techniques in MATLAB. |
| V SEM | PC-CS- AIDS- 317LA: Design and Analysis of Algorithms Lab |
| CO1 | The student should be able to Design algorithms for various computing problems. |
| CO2 | The student should be able to Analyse the time and space complexity of algorithms. |
| CO3 | The student should be able to Critically analyse the different algorithm design techniques for a given problem. |
| CO4 | The student should be able to Modify existing algorithms to improve efficiency. |
| V SEM | PC-CS-AIDS-315LA: Python Lab |
| CO1 | Implement Python programming basics and paradigm. |
| CO2 | Implement python looping, control statements, string manipulations and functions. |
| CO3 | Implement Data Analysis & visualization–using NumPy, panda matplot lib etc. |
| CO4 | Implement Object Oriented Skills in Python. |
| V SEM | MC-904A: Energy Resources &Management |
| CO1 | An overview about Energy Resources, Conventional and Non-conventional sources. |
| CO2 | Understand the Layout and working of Conventional Power Plants. |
| CO3 | Understand the Layout and working of Non-Conventional Power Plants. |
| CO4 | To understand the Energy Management, Audit and tariffs, Role of Energy in Economic development and Energy Scenario in India. |
| VI SEM | PC-CS-AIDS-302A : Compiler Design |
| CO1 | To understand the role and designing of a lexical analyzer. |
| CO2 | To analyze the role and designing of syntax analyzer or parser. |
| CO3 | To identify the role of semantic analyzer and intermediate code generation. |
| CO4 | To explore the design importance of optimization of codes and error detection. |
| VI SEM | ES-CS-AIDS-304A: Applied Statistical Analysis for AI |
| CO1 | Explore the Statistical Analysis concepts with the irrelationships and process. |
| CO2 | Explain the concept of describing, transforming and summarizing data using various statistical methods and apply them to solve real world problems. |
| CO3 | Understand and apply testing hypothesis with real life datasets. |
| CO4 | Examine and analyze the relationships to find the correlation and regression and their applications in real life. |
| CO5 | Explore the advanced techniques with applications of decision trees, neural networks. |
| VI SEM | PC-CS-AIDS-306A: Big Data Analytics |
| CO1 | Understand Big Data and its analytics in the real world. |
| CO2 | Analyze the Big Data framework like Hadoop and NOSQL to efficiently store and process Big Data to generate analytics. |
| CO3 | Design of Algorithms to solve Data Intensive Problems using Map Reduce Paradigm 3 4 |
| CO4 | Design and Implementation of Big Data Analytics using pig and spark to solve data intensive problems and to generate analytics. |
| CO5 | Implement Big Data Activities using Hive. |
| VI SEM | PC-CS-AIDS-308A: Applied Machine Learning |
| CO1 | Identify over fit regression models. |
| CO2 | Compare different regularized regression algorithms and decision tree ensemble algorithms. |
| CO3 | Explain the confusion matrix and its relation to the ROC curve. |
| CO4 | Construct training datasets, testing datasets, and model pipelines. |
| VI SEM | OE-CS-AIDS-302: Soft Skills and Interpersonal Communication |
| CO1 | Develop effective communication skills (spoken and written). |
| CO2 | Develop effective presentation skills. |
| CO3 | Conduct effective business correspondence and prepare business reports which produce results. |
| CO4 | Become self-confident individuals by mastering inter-personal skills, team management skills, and leadership skills. |
| VI SEM | OE-CS- AIDS-304: Management Information System |
| CO1 | To provide introduction to relational model. |
| CO2 | To learn about ER diagrams and SQL. |
| CO3 | To understand about the concept of functional dependencies. |
| CO4 | To understand about Query Processing and Transaction Processing. |
| VI SEM | OE-CS-AIDS-306: Enterprise Resource Planning |
| CO1 | Design model for ERP for large projects and to design model for E-commerce architecture for any application |
| CO2 | Describe the advantages, strategic value, and organizational impact of utilizing an ERP system for the management of information across the functional areas of a business: sales and marketing, accounting and finance, human resource management, and supply chain. |
| CO3 | Demonstrate a working knowledge of how data and transactions are integrated in an ERP system to manage the sales order process, production process, and procurement process. |
| CO4 | Evaluate organizational opportunities and challenges in the design system within a business scenario. |
| VI SEM | PC-CS-AIDS-310A: Soft Computing |
| CO1 | The main objective of the Soft Computing Techniques to Improve Data Analysis |
| CO2 | To strengthen the dialogue between the statistics and soft computing research communities in order to cross-pollinate both fields |
| CO3 | To develop Solutions and generate mutual improvement activities |
| CO4 | To develop practical data analysis skills, which can be applied to practical problems |
| VI SEM | PC-CS-AIDS-312LA: Applied Machine Learning Lab |
| CO1 | Perform advanced data cleaning, exploration, and visualization |
| CO2 | Engineer features based on conditional relationships between existing features |
| CO3 | Build and finalize a machine learning classifier |
| CO4 | Build machine learning applications in different domains |
| VI SEM | PC-CS-AIDS-314LA: Big Data Analytics Lab |
| CO1 | Demonstrate the knowledge of big data analytics and implement different file management task in Hadoop. |
| CO2 | Understand Map Reduce Paradigm and develop data applications using variety of systems. |
| CO3 | Analyze and perform different operations on data using Pig Latin scripts. |
| CO4 | Illustrate and apply different operations on relations and databases using Hive. |
| VI SEM | ES-CS-AIDS-316LA: Applied Statistical Analysis for AI Lab |
| CO1 | Implement basic Statistical operations in R language. |
| CO2 | Implement regression techniques. |
| CO3 | Implement hypothesis testing with real time applications. |
| CO4 | Implement and evaluate various probability distributions for real world problems. |
| VII SEM | HM-CS-AIDS-401A: Business Intelligence and Data Visualization |
| CO1 | Students will learn the principles and best practices for how to use data in order to support fact-based decision making. |
| CO2 | Emphasis will be given to applications in marketing, where BI helps in the Businesses. |
| CO3 | BI helps performing for sales analysis and in application domains |
| CO4 | Practical experience will be gained by developing a BI project (case-study) with leading BI software. |
| VII SEM | HSS-403A :Universal Human Values II: Understanding Harmony |
| CO1 | Development of a holistic perspective based on self-exploration about themselves(humanbeing),family,societyandnature/existence. |
| CO2 | Understanding (or developing clarity) of the harmony in the human being, family, society and nature/existence. |
| CO3 | Strengthening of self-reflection. |
| CO4 | Development of commitment and courage to act. |
| VII SEM | OE-CS-AIDS-401: Cyber Law and Ethics |
| CO1 | To facilitate the basic knowledge of cyber Law. |
| CO2 | To learn about how to maintain the Confidentiality, Integrity and Availability of information technology act. |
| CO3 | To get enable to fix the various Cyber Law and Related Legislation. |
| CO4 | To deal with the Cyber Ethics. |
| VII SEM | OE-CS-AIDS-403: Probability for Data Science |
| CO1 | Understand the mathematical framework for probability theory |
| CO2 | Understand various kinds of Random Variables that are fundamental to probabilistic modeling. |
| CO3 | To Learn Statistical Concept in Data Analytics |
| CO4 | Explore some introductory concepts from statistics that are helpful in analyzing data and machine learning. |
| VII SEM | OE-CS-AIDS-405:Cluster Computing |
| CO1 | Remember and understand the basic concepts/Principles of distributed Systems |
| CO2 | Analyze the Various Concepts of Cluster Computing |
| CO3 | Able to describe different parallel processing platforms involved in achieving high performance computing |
| CO4 | Develop efficient and high-performance parallel programming. |
| VII SEM | OE-CS- AIDS-407: Microprocessor |
| CO1 | To study the Architecture of 8086 microprocessors |
| CO2 | To implement the interfacing of memories to 8086 Microprocessor |
| CO3 | To learn and analyze the instruction set of 8086 Microprocessor and implementation of assembly language programming of 8086 Microprocessor. |
| CO4 | To design and implement the interfacing of interrupts, basic I/O and DMA with 8086 Microprocessor |
| VII SEM | PE-CS-AIDS-415A: ANN and Deep Learning |
| CO 1 | To learn the basics of artificial neural networks concepts, various neural networks architecture |
| CO 2 | To explore knowledge of special types of Artificial neural networks |
| CO 3 | To understand the basics of Deep learning and its applications |
| CO 4 | To imprise about the different deep learning algorithms and their applications to solve real world problems. |
| VII SEM | PE-CS-AIDS-417A:Data Mining & Predictive Modelling |
| CO1 | Understand the fundamental concept of Data Mining. |
| CO2 | Learn Data Mining techniques for Prediction and Forecasting. |
| CO3 | Compare the underlying Predictive Modelling techniques. |
| CO4 | Select appropriate Predictive Modelling approaches to identify cases and apply using a suitable package such as SPSS modeler . |
| VII SEM | PE-CS-AIDS-419A: Predictive Analysis |
| CO1 | Understand how to use predictive analytics tools to analyze real-life business problems. |
| CO2 | Demonstrate case-based practical problems using predictive analytics techniques to interpret model outputs. |
| CO 3 | Learn regression, logistic regression, and forecasting using software tools such as MS Excel, SPSS, and SAS. |
| CO4 | Understand to Forecasting, Time Series Analysis and develop the Model. |
| VII SEM | PE-CS-AIDS-421A: Advance Computer Architecture |
| CO1 | Classify and interpret various paradigms, models and micro-architectural design of advanced computer architecture as well as identify the parallel processing types and levels for achieving optimum scheduling |
| CO2 | Identify the roles of VLIW & superscalar processors and branch handling techniques for performance improvement |
| CO3 | Analyze and interpret the basic usage of various MIMD architectures and relative importance of various types of static and dynamic connection networks for realizing efficient networks. |
| CO4 | Examine the various types of processors and memory hierarchy levels and cache coherence problem including software and hardware based protocols to achieve better speed and uniformity. |
| VII SEM | PE-CS-AIDS-423A: High Performance Computing |
| CO1 | To study the need for HPC and parallelism |
| CO2 | To study parallel models of computation such as dataflow, and demand-driven computation. |
| CO3 | To study state of the art processor architectures |
| CO4 | To program and accelerate applications on the new high performance computing devices, we must understand both the computational architecture and the principles of program optimization |
| VII SEM | PE-CS-AIDS-425A: Human AI Interaction |
| CO1 | To have a broad foundational understanding of types and techniques in AI/ML |
| CO2 | To be able to demonstrate good understanding of the potential use cases and benefits of artificial intelligence (AI) technologies |
| CO3 | To have a critical understanding of the ethical, social and legal implications of AI applications on human life and work |
| CO4 | To be able to understand appropriate design, development and research methods for human-AI interaction |
| VII SEM | PE-CS-AIDS- 427A: Software Testing |
| CO 1 | Expose the criteria and parameters for the generation of test cases. |
| CO 2 | Learn the design of test cases and generating test cases. |
| CO 3 | Be familiar with test management and software testing activities and V&V activities. |
| CO 4 | Be exposed to the significance of software testing in web and Object orient techniques. |
| VII SEM | PE-CS-AIDS-429A: Natural Language Processing |
| CO1 | Be familiar with syntax and semantics in NLP. |
| CO2 | To implement various concepts of knowledge representation using Prolog. |
| CO3 | To classify different parsing techniques and understand semantic networks. |
| CO4 | To identify/explain various applications of NLP. |
| VII SEM | HM-CS-AIDS-405A: Data Visualization Lab |
| CO1 | Understand and describe the main concepts of data visualization |
| CO2 | Create ad-hoc reports, data visualizations, and dashboards using Tableau Desktop |
| CO3 | Publish the created visualizations to Tableau Server and Tableau Public |
| CO4 | Create Dashboard for real problems in Industry |
| VII SEM | PC-CS-AIDS- 415 LA: ANN and Deep Learning Lab |
| CO1 | To learn the basics of artificial neural networks concepts, various neural networks architecture |
| CO2 | To explore knowledge of special types of Artificial neural networks |
| CO3 | To understand the basics of Deep learning and its applications |
| CO4 | To imprise about the different deep learning algorithms and their applications to solve real world problems. |
| VII SEM | PC-CS-AIDS- 417LA:Data Mining & Predictive Modelling LAB |
| CO1 | Understand the fundamental concept of Data Mining. |
| CO2 | Learn Data Mining techniques for Prediction and Forecasting. |
| CO3 | Compare the underlying Predictive Modelling techniques. |
| CO4 | Select appropriate Predictive Modelling approaches to identify cases and apply using a suitable package such as SPSS modeller. |
| VII SEM | PE-CS-AIDS- 419 LA: Predictive Analysis Lab |
| CO1 | Understand how to use predictive analytics tools to analyze real-life business problems. |
| CO2 | Demonstrate case-based practical problems using predictive analytics techniques to interpret model outputs. |
| CO3 | Learn regression, logistic regression, and forecasting using software tools such as MS Excel, SPSS, and SAS. |
| CO4 | Understand to Forecasting, Time Series Analysis and develop the Model. |
| VII SEM | PE-CS-AIDS- 421 LA: Advance Computer Architecture Lab |
| CO1 | To implement adder circuits using basic gates |
| CO2 | To understand the converter circuits using basic gates. |
| CO3 | To understand the working of Multiplexer |
| CO4 | To understand the various circuits for ALU, Datapath and control units. |
| VIII SEM | PC-CS-AIDS- 402A: Reinforcement Learning |
| CO1 | To learn the basics of Reinforcement Learning concepts, various Reinforcement Learning architecture |
| CO2 | To explore knowledge of various process of Reinforcement Learning |
| CO3 | To understand the basics of Reinforcement Learning models |
| CO4 | To implies about the different Reinforcement Learning algorithms and their applications to solve real world problems. |
| VIII SEM | HSS-404A: Entrepreneurship and Start-ups |
| CO1 | To understand the basics of Entrepreneurship . |
| CO2 | To learn the basics of Creative and Design Thinking . |
| CO3 | To apply the Business Enterprises . |
| CO4 | To know about business models . |
| VIII SEM | OE-CS- AIDS-402: Cyber Security |
| CO1 | Understand the cyber security threat landscape. |
| CO2 | Develop a deeper understanding and familiarity with various types of cyber-attacks, cyber crimes, vulnerabilities and remedies thereto. |
| CO3 | Increase awareness about cyber-attack vectors and safety against cyber-frauds |
| CO4 | Analyze and evaluate existing legal framework and laws on cyber security. |
| VIII SEM | OE-CS-AIDS-404: Information Retrieval |
| CO1 | Ability to apply information retrieval principles and retrieval models to locate relevant information from large collections of data |
| CO2 | Apply various indexing technique and understanding of different data structures. |
| CO3 | Implementation of various clustering and searching techniques. |
| CO4 | Understanding of information visualization and various advance topics. |
| VIII SEM | OE-CS- AIDS-406: Robotics and Intelligent Systems |
| CO1 | Understand the basic terminologies in Robotics to develop intelligent systems |
| CO2 | Apply the random search and heuristic search for intelligent systems. |
| CO3 | Understand the abstractions and reasoning for intelligent systems |
| CO4 | Apply the rule based methods in intelligent systems |
| VIII SEM | OE-CS-AIDS-408: Agile Software Engineering |
| CO1 | Analyze existing problems with the team, development process and wider organization |
| CO2 | Apply a thorough understanding of Agile principles and specific practices |
| CO3 | Select the most appropriate way to improve results for a specific circumstance or need |
| CO4 | Judge and craft appropriate adaptations to existing practices or processes depending upon analysis of typical problems and risk analysis. |
| VIII SEM | OE-CS-AIDS-410: Image Processing and Recognition |
| CO1 | To Understand Basics of Image formation and transformation using sampling and quantization |
| CO2 | To Understand different types signal processing techniques used for image sharpening and smoothing |
| CO3 | To understand the nature and inherent difficulties of the pattern recognition problems. |
| CO4 | Understand concepts, trade-offs, and appropriateness of the different feature types and classification techniques such as Bayesian, maximum likelihood, etc |
| VIII SEM | PE-CS-AIDS- 414A: Social Networks |
| CO1 | Demonstrate proficiency in the use of social networks for business and personal use |
| CO2 | Demonstrate proficiency in the use of social network analysis concepts and techniques. |
| CO3 | Demonstrate proficiency in the use of social network developer tools. |
| CO4 | Examine the various types of processors and demonstrate proficiency in the use of social network concepts for solving real world issues. |
| VIII SEM | PE-CS-AIDS-416A: Application of Data Science in Industry |
| CO1 | Describe a flow process for data science problems |
| CO2 | Classify data science problems into standard typology |
| CO3 | Develop R codes for data science solutions |
| CO4 | Correlate results to the solution approach followed and Construct use cases to validate approach and identify modifications required |
| VIII SEM | PE-CS-AIDS-420A: Neural Network and Fuzzy Logic |
| CO1 | Understand the concept of Artificial Intelligence, search techniques and a knowledge Representation issues |
| CO2 | Understanding reasoning and fuzzy logic for artificial intelligence |
| CO3 | Students will be able to learn defuzzification and fuzzy measures |
| CO4 | Studentswillbeabletolearntheapplicationsoffuzzylogicandhybridsoftcomputingtechniques |
| VIII SEM | PE-CS-AIDS-422A:Internet of Things |
| CO1 | Understanding of basic concepts of Internet of things. |
| CO2 | Implementation of programming fundamentals on Arduino. |
| CO3 | Understanding of various sensors and IoT protocols. |
| CO4 | Importance of cryptographic fundamentals in Internet of things. |
| VIII SEM | PE-CS-AIDS-424A:Block Chain |
| CO1 | Understanding of distributed systems and importance of security in networks. |
| CO2 | Basic Concept of blockchain and application of blockchain in various domains. |
| CO3 | Knowledge of various hash functions and consensus algorithms. |
| CO4 | Understanding the concepts of Ethereum blockchain and tools used for implementation. |
| VIII SEM | PE-CS-AIDS-426A: Next Generation Databases |
| CO1 | Implement and evaluate complex, scalable database systems, with emphasis on providing experimental evidence for design decisions. |
| CO2 | Demonstrate the management of structured and unstructured data management with recent tools and technologies. |
| CO3 | Demonstrate competency in designing No SQL database management systems. |
| CO4 | Demonstrate competency in designing XML Databases. |
| VIII SEM | PE-CS-AIDS-416LA: Application of Data Science in Industry Lab |
| CO1 | Describe a flow process for data science problems |
| CO2 | Classify data science problems into standard typology |
| CO3 | Develop R codes for data science solutions |
| CO4 | Correlate results to the solution approach followed and Construct use cases to validate approach and identify modifications required |
| VIII SEM | PC-CS-AIDS- 404LA: Reinforcement Learning Lab |
| CO1 | Implement Python programming advance and paradigm. |
| CO2 | Implement various process of Reinforcement Learning |
| CO3 | Implement various Reinforcement Learning models |
| CO4 | Implement various Reinforcement Learning algorithms. |
| VIII SEM | PE-CS-AIDS-414LA:Social Networks Lab |
| CO1 | Demonstrate proficiency in the use of social networks for business and personal use |
| CO2 | Demonstrate proficiency in the use of social network analysis concepts and techniques. |
| CO3 | Demonstrate proficiency in the use of social network developer tools. |
| CO4 | Examine the various types of processors and demonstrate proficiency in the use of social network concepts for solving real world issues. |
| VIII SEM | PE- CS-AIDS-420 LA: Neural Network and Fuzzy Logic Lab |
| CO1 | To give students an understanding of foundational concepts of fuzzy control primarily based on fuzzy set theory. To know operations on fuzzy sets, fuzzy relations. |
| CO2 | To understand basic building blocks of Mamdani Fuzzy Logic |
| Controllers (FLCs). | |
| CO3 | To get an insight into Fuzzification, Fuzzy Inferencing, Defuzzification. |
| CO4 | To understand the nonlinearity of different blocks of FLC and to analyze adaptive issues in the stability issues of FLCs. |
| Industrial Training/Internship (Semester: 3rd, 5th and 7th) | |
| CO1 | Identify the problem in the relevant engineering field and gather information through independent or collaborative study |
| CO2 | Develop and maintain a product based on the learning with the ability to work as an individual or in group with the capacity to be a team member or leader or manager. |
| CO3 | Apply the acquired skills in communication and document writing. |
| CO4 | Demonstrate the professional, societal and ethical responsibilities of an engineer. |
| Project – I, II (Semester: 7th and 8th) | |
| CO1 | Independently carry out literature survey in identified domain, and consolidate it to formulate a problem statement |
| CO2 | Apply identified knowledge to solve a complex engineering problem and design a solution, implement and test the proposed solution |
| CO3 | Use synthesis/modeling to simulate and solve a problem or apply appropriate method of analysis to draw valid conclusions and present, demonstrate, execute final version of project |
| CO4 | Incorporate the social, environmental and ethical issues effectively into solution of an engineering problem |
| CO5 | Contribute effectively as a team member or leader to manage the project timeline |
| CO6 | Write pertinent project reports and make effective Project Presentations |
(a) PROJECT EVALUATION
(i) Internal assessment of project work
| Review | Assessment | AssessmentTool (Rubric) | AssessmentWeightage | CO(s)Covered |
| 1 | Project Synopsis Evaluation | PR1 | 20% | 1 |
| 2 | 1st Mid-Term Project Evaluation | PR2 & PR6 | 10% & 5% | 2, 5 |
| 3 | 2nd Mid-Term Project Evaluation | PR3 & PR6 | 10% & 5% | 2, 5 |
| 4 | End Term Project Evaluation | PR4, PR5 & PR6 | 20%, 10% & 5% | 3, 4, 5 |
| 5 | Project Report Evaluation | PR7 | 15% | 6 |
(ii) Rubric-PR1: Project Synopsis Evaluation (Maximum Marks: 20)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Topic selection | Complete innovative and useful for society | Somewhat innovative and useful for society | Useful for society but not innovative | Useful for limited group and not innovative |
| b | Problem Definition | Exceeds expectation. | Extends beyond expectation in some manner. | Meets expectation. | Nearly meet expectations |
| The social, ethical and environmental issues of the project problem also identified. | Problem and its implications well understood and described. | Problem and its implications understood but not well described. | Steps to be followed to solve the defined problem are not specified properly. | ||
| c | Literature Survey Purpose and need of the project | Outstanding investigation in all aspects. | Well-researched project, good depth and thoroughness, sensible planning of research and well referenced throughout. | Research is clear and structured. | Minimal research or cursory coverage |
| Detailed and extensive explanation of the purpose and need of the project. | Collects a great deal of information and good study of the existing systems. | Appropriate coverage is present and referenced. | Minimal referencing, | ||
| . | . | Moderate study of the existing systems. | Minimal explanation of the purpose and need of the project. | ||
| d | Justification of Project Objectives and Planning | All objectives of the proposed work are well defined. | Good justification to the objectives. | Incomplete justification to the objectives proposed. | Limited information |
| Steps to be followed to solve the defined problem are clearly specified. | Methodology to be followed is specified but detailing is not done. | Steps are mentioned but unclear and without justification to objectives. | Only some objectives of the proposed work are defined. | ||
| e | Project Scheduling & Distribution of Work among Team members | Detailed and extensive scheduling with timelines provided for each phase of project. | Good Scheduling of project. | Moderate scheduling of project. | Poor / No project scheduling done |
| Work breakdown structure well defined. | Work breakdown structure properly defined. | Work breakdown insufficient. | No Work breakdown structure provided. | ||
| TOTAL MARKS = (a + b + c + d + e)/2.5 | |||||
(iii) Rubric-PR2:1st Mid-term Project Evaluation (Maximum Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality of Software Requirements/ Specifications | Outstanding clarity of thought and documentation in the development of design from the specification using and adapting models appropriately. | Focus is on specification and the design follows from it, using most appropriate elements of chosen design technique | Design techniques used minimally though correctly on specification | Very minimal analysis |
| Excellent incisive analysis leading to well defined model/ requirements specification of high quality that is fully accurate. | Analysis is well presented and leads to a sound well documented model/ requirements specification. | Minimal model/ requirements specification is created | Very Minimal model/ requirements specification is created | ||
| b | Quality Appropriateness and Accuracy of Design | Excellent design covering all aspects of the specification, fully appropriate to the project, shoeing clear thinking | Appropriate design, clear and accurate, satisfactory for the implementation of the project. | Limited design, or design not well related to specification or model | Very minimal design |
| TOTAL MARKS = (a + b)/2 | |||||
(iv) Rubric-PR3: 2nd mid Term Project Evaluation (Maximum Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality, appropriateness and accuracy of Project Implementation | Excellent use of software engineering principles and models both at higher and lower levels in implementation from design cycle. | Very well engineered solution, with evidence that the student has used proven method in transforming design into implementation. | Appropriately engineered implementation which follows from design Language/package facilities exploited to suggest a functional implementation. | In sufficient implementation to show competent use of any problem solving methods. |
| Documented use of complex features in the language /package which show quantitatively and qualitatively the improvements gained. | Appropriate use of facilities to make implementation more efficient or effective. | Project with some limitations, mostly technically sound. | Minimal implementation. | ||
| An excellent fully operating technically outstanding project. | Effective and efficient implementation technically with only minor limitations. | Project essentially works but with some severe functional limitations. | Poor technical quality with little use of development skills or knowledge in evidence. | ||
| Project fulfils functional requirements specification exactly with no limitations or failures of any type. | Project works well with only some minor functional limitations. | Project does not work in most parts to requirements/ specification. | |||
| b | Quality, appropriateness and accuracy of Testing | A quality piece of work giving full coverage of the solution and full program of testing/ evaluation undertaken. | Extensive and well organized implementation and testing/evaluation documentation. | Sufficient implementation documentation and testing/ evaluation documentation. | Minimal implementation documentation or testing/evaluation documentation. |
| TOTAL MARKS = (a + b)/2 | |||||
(v) Rubric–PR4: End Semester Internal Project Evaluation (Maximum Marks: 20)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Quality and accuracy of Software System/Model | Excellent design covering all aspects of the specification, fully appropriate to the project, shoeing clear thinking. | Appropriate design, clear and accurate, satisfactory for the implementation of the project. | Design not well related to specification or model | Very minimal design |
| An excellent fully operating technically outstanding project | Very well engineered solution, with evidence that the student has used proven method in transforming design into implementation | Language/package facilities exploited to suggest a functional implementation | In sufficient implementation to show competent use of any problem solving methods | ||
| Outstanding clarity of thought and documentation in the development of design from the specification using and adapting models appropriately | Effective and efficient implementation with only minor limitations | Project with some limitations, mostly technically sound | Poor technical quality with little use of development skills or knowledge in evidence | ||
| A quality piece of work giving full coverage of the solution and full programme of testing/ evaluation undertaken | Extensive and well organized implementation and testing/ evaluation documentation | Project essentially works but with some severe limitations | Project does not work in most parts to requirements specification | ||
| Sufficient implementation documentation and testing/ evaluation documentation | Minimal implementation documentation or testing/ evaluation documentation | ||||
| b | Demonstration of software system /Module working and Functioning | All defined objectives are achieved | All defined objectives are achieved | All defined objectives are achieved | Only some of the defined objectives are achieved |
| Each module working well and properly demonstrated | Each module working well and properly demonstrated | Modules are working well in isolation and properly demonstrated | Modules are not in proper working form that further leads to failure of integrated system | ||
| All modules of project are well integrated and system working is accurate | Integration of all modules not done and system working is not Very satisfactory | Modules of project are not properly integrated | |||
| TOTAL MARKS = (a + b) | |||||
(vi) Rubric–PR5: Identification of the social, environmental and ethical issues (Max. Marks: 10)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Identification of the social, environmental and ethical issues of the project problem | Identifying and solving social, environmental and ethical issues | Identifying and solving social, environmental or ethical issues | Identifying social, environmental or ethical issues | Not able to Identify any issues |
(vii) Rubric– PR6: Individual Contribution Evaluation (Maximum Marks: 05)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Individual Presentation | Excellently planned and executed presentation and demo leaving the listeners in no doubt of the value of the product | Quality presentation and demo. Clear and concise description leaving listeners with sound understanding of project and its problems | Timed and prepared presentation, demo with student describing what has been learnt | No presentation or no demo or student unable to articulate project development |
| Contents of presentations are appropriate and well delivered | Contents of presentations are appropriate and well delivered | Contents of presentations are appropriate but not well delivered | Contents of presentations are not appropriate and not well delivered | ||
| Proper eye contact with audience and clear voice with good spoken language | Clear voice with good spoken language but | Eye contact with only few people and unclear voice | Poor eye contact with audience and unclear voice | ||
| Less eye contact with audience | |||||
| b | Individual Contribution | Excellent Contribution showing his/her dependency in project | Good contribution as reflected in overall work | Some contribution as reflected in overall work. | No Contribution |
| c | To observe the completion of work referring to the original set plan | Ahead of the proposed plan | In pace with the plan | Delayed but can cope up with the lag at their own | Interventional help is needed |
| TOTAL MARKS = (a + b+c)/6 | |||||
(viii) Rubric– PR7: Project Report Evaluation (Maximum Marks: 15)
| No. | Criteria | Excellent (10-9) | Good (8-7) | Average (6-5) | Poor (4-0) |
| a | Style, structure, form and the perceived clarity with readability of report | Outstanding, comprehensive and clear report, Fully referenced | Effective report using academic language accurately referenced. | Acceptable report structure, some referencing, no missing parts, clarity of language | Report is unbalanced or unclear, or it is difficult to follow ideas |
| Major sections missing, or no referencing | |||||
| b | Effectiveness of the project report | Accurately referenced, very high standard of presentation aimed at the right level throughout. | Effective technical/business report fully structured, accurately referenced | Adequate report presentation references included. | Referencing is poor or inconsistent, or lack of illustrative content. |
| Fully referenced Complete explanation of the key concepts and strong description of the technical requirements of the project | Incomplete explanation of the key concepts and in- sufficient description of the technical requirements of the project | Report is unreadable as an English report | |||
| Complete explanation of the key concepts but in-sufficient description of the technical requirements of the project | Inappropriate explanation of the key concepts and poor description of the technical requirements of the project. | ||||
| c | Results, Conclusion and Discussions | Results are presented in very appropriate manner | Results are presented in good manner | Results presented are not much satisfactory | Results are not presented properly |
| Project work is well summarized and concluded | Project work summary and conclusion not very appropriate Future extensions in the project are specified | Project work summary and conclusion not very appropriate Future extensions in the project are not specified | Project work is not summarized and concluded | ||
| Future extensions in the project are well specified | Future extensions in the project are not specified | ||||
| TOTAL MARKS = (a+b+c)/2 | |||||
(ix) Evaluation weightages of each CO through the rubrics
| CO Number | CO1 | CO2 | CO3 | CO4 | CO5 | CO6 |
| Marks allotted to the COs through the rubrics (Max. 100) | 20 | 20 | 20 | 10 | 15 | 15 |
(b) INTERNSHIP EVALUATION
(i) Internal assessment of internships
| Rubric | Parameters | Weightage (Assessment Marks) |
| R1 | Objective of Training | 20% (10) |
| R2 | Domain Knowledge | 20% (10) |
| R3 | Practical Implementation | 20% (10) |
| R4 | Q and A during Presentation | 20% (10) |
| R5 | Training Report | 20% (10) |
| Total | 100% (50) | |
(ii) The rubrics for assessing internships:
| Rubric | Parameter | Level of Achievement | |||
| Excellent (10) | Good (8) | Average (6) | Poor (4) | ||
| R1 | Objective of Training | Objective of training is clearly and well defined. | Objective of training is defined with good justifications. | Objective of training is defined with little justifications. | Objective of training is unclear. |
| R2 | Domain Knowledge | Extensive knowledge of technology implemented | Fair knowledge of technology implemented | Lacks sufficient knowledge of technology implemented | No knowledge of technology implemented |
| R3 | Practical Implementation | Practical Implementation is completed in very systematic manner. | Practical Implementation is completed in appropriate manner. | Practical Implementation is completed but not systematically. | Practical Implementation is not completed. |
| R4 | Q and A during Presentation | Answers effectively in a satisfied manner to queries by the examiner | Answers appropriately to queries by the examiner | Non satisfactory answers to the queries by the examiner | Does not answer to queries by the examiner |
| R5 | Training Report | Report as per specified format and completed. | Report completed with very few contents not as per format. | Report completed but formatting not done properly | Report not prepared as per format. |
(iii) Evaluation weightages of each CO through the rubrics
| CO Number | CO1 | CO2 | CO3 | CO4 |
| Marks allotted to the COs through the rubrics (Max. 50) | 10 | 20 | 10 | 10 |
