Computer Science & Engineering (AI & ML)

Student Evaluation and OBE

Evaluation Scheme for B. Tech.
    • Theory (100 Marks)
      • Continuous Internal Assessment (25 Marks)
        • Sessional Tests (15 Marks)
        • Attendance (5 Marks)
        • Assignments (5 Marks)
      • End Semester University Examination (75 Marks)
    • Practicals (100 Marks)
      • Continuous Internal Assessment (40 Marks)
        • Lab Performance (16 Marks)
        • Lab Records/Attendance (12 Marks)
        • Viva (12 Marks)
      • End Semester University Examination (60 Marks)
    • Project Evaluation
    • Seminar on Internship Evaluation
Course Outcomes
III SEM BS-CS-AIML-201A:Applied Statistical Analysis for AI
CO1 To study  the Statistical Analysis concepts with their relationships and process.
CO2 To familiarize with describing data, transforming and summarizing.
CO3 To understand testing hypothesis with real time applications.
CO4 To apply the examining relationships to find the correlation and regression.
CO5 To demonstrate and analyse using basic statistical techniques with different use cases.
CO6 To understand the advanced techniques with applications of decision trees, neural networks.
III SEM ES-CS-AIML-203A:Data Structure
CO 1 To introduce the basic concepts of Data structure , basic data types ,searching  and sorting based on array data types.
CO 2 To introduce the structured data types like Stacks and Queue and its basic operations’ implementation.
CO 3 To introduce dynamic implementation of linked list.
CO 4 To introduce the concepts of Tree and graph and implementation of traversal algorithms.
III SEM PS-CS-AIML-205A: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-AIML-207A :Introduction to AI
CO1 Demonstrate fundamental understanding  of Artificial Intelligence (AI) and its foundation
CO2 Demonstrate basic concepts of problem solving, searching, inference, perception
CO3 Demonstrate proficiency in applying AI techniques in various domains
CO4 Apply basic principles of AI in solutions that require real world knowledge representation and learning
CO5 Demonstrate the real life examples of Artificial Intelligence
CO6 Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications
III SEM ES-CS-AIML-209A:Programming Language
CO 1 To introduce the basic concepts of programming language, the general problems and methods related to syntax and semantics.
CO 2 To introduce the structured data objects, subprograms and programmer defined data types.
CO 3 To outline the sequence control and data control.
CO 4 To introduce the concepts of storage management using programming languages.
III SEM HM-902 A: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-AIML-  213LA:Data Structure 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-AIML-  215LA:Object Oriented Programming Lab
CO1 To familiarize with the class and objects
CO2 To implement the concept of constructors
CO3 To familiarize the concept of operator overloading
CO4 To implement the concepts of Inheritance
III SEM PC-CS-AIML-217LA:Python Lab-I
CO1 To understand the basic concepts of programming in Python.
CO2 To implement data types, logical and mathematical operators.
CO3 To implement list, tuples, dictionaries, arrays, strings
CO4 To understand and implement the fundamentals of functions, recursion
CO5 To learn and apply the concepts of looping and conditional statements
CO6 To learn and implement the fundamentals of searching and sorting
IV SEM BS-CS-AIML 202M:Mathematics for Machine Learning
CO1 To understand the basic concepts of data science & machine learning Concepts and their application in modern context
CO2 To apply the basic statistical concepts for solving various problems
CO3 To distinguish between various probability distributions and apply the concepts for the solution of related problems
CO4 To learn the essential tools of matrices and linear algebra including linear transformations, eigen values, diagonalisation, orthogonalization and factorization
CO5 To learn mathematical modelling, types of matrices
CO6 To Implement mathematical concepts using real-world data
IV SEM PC-CS-AIML-204A:Intelligent Systems
CO1 Understand the basic terminologies in artificial intelligence 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
CO5 Identify the characteristics and architectures of algorithms of multi agent systems
CO6 Identify different application areas of Intelligent Systems
IV SEM PC-CS-AIML-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-AIML- 210A:Operating System
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 and the concept of deadlocks.
CO4 To understand various memory management scheme and to study I/O management and file systems.
IV SEM PC-CS-AIML-212A:Software Engineering
CO1 To understand the basic concepts of Software Engineering.
CO2 To understand the fundamental concept of requirements engineering and Analysis Modelling.
CO3 To understand the different design techniques and their implementation.
CO4 To learn about software testing and maintenance measures.
IV SEM PC-CS-AIML- 216A: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 PC-CS-AIML- 218A:Internet and 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-AIML-220A:Python Lab-II
CO1 To understand the basic concepts of Python libraries
CO2 To learn and apply concepts of data manipulation in machine Learning .
CO3 To learn and apply descriptive analysis concepts.
CO4 To understand the fundamentals of knowledge representation.
CO5 To learn and apply concepts of distribution and hypothesis.
CO6 To understand and implement various data visualization concepts.
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-AIML- 301A:Automata
CO 1 Students are able to explain and manipulate the different fundamental concepts in automata theory and formal languages.
CO 2 Simplify automata and context-free grammars; Prove properties of languages, grammars and automata with rigorously formal mathematical methods, minimization.
CO 3 Differentiate and manipulate formal descriptions of push down automata, its applications and transducer machines.
CO 4 To understand basic properties of Turing machines and computing with Turing machine, the concepts of tractability and decidability.
V SEM PC- CS- AIML- 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 ES- CS- AIML- 305A:Computer Network
CO1 To understand the basic concept of networking, types, networking topologies and layered architecture.
CO2 To understand data link 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- AIML- 307A:Artificial Neural Networks
CO1 To learn the basics of artificial neural networks concepts.
CO2 Expose detailed explanation of various neural networks architecture.
CO3 To explore knowledge of special types of Artificial neural networks.
CO4 To explore deep neural networks and fuzzy logic techniques.
V SEM ES- CS- AIML- 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- AIML- 311A:Artificial Intelligence and Machine Learning
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 Apply basic principles of ML Algorithms and Models; regression, classification, and clustering.
V SEM PC- CS- AIML- 313A:Artificial Intelligence and Machine Learning Lab
CO1 To implement the search space problems.
CO2 To formulate and implement the game problems.
CO3 To implement the various classifiers on different dataset
CO4 To implement the clustering algorithms
V SEM PC-CS-AIML- 317A :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 Analyze the time and space complexity of algorithms.
CO3 The student should be able to Critically analyze the different algorithm design techniques for a given problem.
CO4 The student should be able to Modify existing algorithms to improve efficiency.
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
V SEM PC- CS- AIML- 315A:Artificial Neural Networks Lab
CO1 To implement the basic mathematical operations using neural network.
CO2 To design single and multi-layer feed-forward neural network
CO3 To understand supervised and unsupervised learning concepts & understand unsupervised learning using Neural networks.
CO4 To understand the training of recurrent Hopfield networks and associative memory concepts.
VI SEM PC CS- -AIML-  302A:Human Computer Interaction
CO1 To develop the foundations of Human Computer Interaction
CO2 To learn and apply the design technologies for individuals and persons with disabilities
CO3 To Understand the structure of models and theories of human computer interaction and vision
CO4 To Design an interactive web interface on the basis of models studied.
VI SEM PC- CS- AIML- 304A:Applied Machine Learning
CO1 To develop an understanding of where and how Machine Learning can be used.
CO2 To learn and apply supervised learning techniques to regression and classification problems
CO3  To understand and apply the concept of KNN and SVM.
CO4 To learn and apply unsupervised Machine Learning techniques.
VI SEM PC- CS- AIML-  306A:Expert Systems
CO1 Examining the fundamentals and terminologies of expert system.
CO2 To explore knowledge of expert system.
CO3 To facilitate students to implement various knowledge representation techniques for acquisition and validate various structures in experts system domain.
CO4 Signifying AI techniques to solve social, industrial, and environmental problems.
CO5 Application of professional aspects in multi-disciplinary approach to meet global standards towards design, realizing and manufacturing.
VI SEM PC-CS-AIML- 308A: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.
VI SEM PC-CS- AIML- 310A:Computer Vision
CO 1 To develop the foundation of image formation, measurement, and analysis
CO 2 To developed the practical skills necessary to build computer vision applications
CO 3 the geometric relationships between 2D images and the 3D world.
CO 4 To have gained exposure to object and scene recognition and categorization from images
VI SEM OE-CS-AIML-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- AIML -304:Project Management
CO 1 To Understand Project Management principles while developing software.
CO 2 To manage software projects and control software deliverables.
CO 3 To Obtain adequate knowledge about software process models and software effort estimation techniques.
CO 4 To Learn staff selection process and the issues related to people management.
VI SEM OE-CS- AIML -306:Enterprise Resource Planning
CO 1 To Develop model for ERP for large projects
CO 2 To Develop model for E-commerce architecture for any application
CO 3 To 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.
CO 4 To Evaluate organizational opportunities and challenges in the design system within a business scenario.
VI SEM OE-CS- AIML -308:Stochastic Processes and Applications
CO 1 To demonstrate clear understanding of random variable and distribution.
CO 2 To demonstrate operations on single random variable
CO 3 To demonstrate operations on multiple random variable
CO 4 To demonstrate random processes with its characteristics.
VI SEM PC-CS-AIML-312A:Applied Machine Learning Lab
CO1 To formulate a machine learning problem anddevelop a solution.
CO2 To select an appropriate pattern analysis method for analyzing data.
CO3 To apply machine learning techniques such as classification and feature selection to practical applications and detect patterns in the data.
CO4 To develop an ANN network and analyze the data.
CO5 To implement recent machine learning techniques, train models, conduct experiments, and develop real-world ML-based applications and products
VI SEM PC- CS- AIML-  314A:Expert Systems Lab
CO1 To implement about representing knowledge.
CO2 To study the reasoning and decision making of some real life problems
CO3 To construct plans and methods for generating knowledge.
CO4 To study the concepts of expert systems.
VI SEM PC- CS- AIML-  318A:Software Testing Lab
CO1 To design and implement the test cases..
CO2 Generating test cases for real life problems.
CO3 To implement test management and software testing activities and V&V activities.
CO4 To implement software testing in web and Object orient techniques.
VI SEM Data Science with R Programming PC-CS- AIML- 401A
CO1 To understand Basics of Data Science statistics, Identify probability distributions.
CO2 To perform basics statistical analysis Using R.
CO3 To Apply basic tools to carry out Exploratory data analysis.
CO4 To explore the components data science Process to interact via machine learning models.
VI SEM Universal Human Values II: Understanding Harmony  HSS- 403A
CO 1 Development of a holistic perspective based on self-exploration about
CO 2 Understanding (or developing clarity) of the harmony in the human being,
CO 3 Strengthening of self-reflection.
CO 4 Development of commitment and courage to act.
VII Robotics and Intelligent Systems OE-CS- AIML- 401
CO1 To Understand the basic terminologies in Robotics to develop intelligent systems
CO2 To Apply the random search and heuristic search for intelligent systems.
CO3 To Understand the abstractions and reasoning for intelligent systems, Apply the rule based methods in intelligent systems
CO4 To Identify the characteristics and architectures of algorithms of multi agent systems, Identify different application areas of Intelligent Systems
VII Probability for Data Science OE-CS-AIML- 403
CO1 To Understand the mathematical framework for probability theory
CO2 To Understand various kinds of Random Variables that are fundamental to probabilistic modeling.
CO3 To Learn statistical concepts that are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.
CO4 To Explore some introductory concepts from statistics that are helpful in analyzing data and machine learning.
VII Cluster Computing OE-CS-AIML- 405
CO1 To Remember and understand the basic concepts/Principles of distributed Systems
CO2 To Analyze the Various Concepts of Cluster Computing
CO3 To Able to describe different parallel processing platforms involved  in achieving high performance computing
CO4 To Develop efficient and high performance parallel programming.
VII Microprocessor OE-CS-AIML-407
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 Advance Computer Architecture PE-CS-AIML-415A
CO1 To 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 To Identify the roles of VLIW & superscalar processors and branch handling techniques for performance improvement
CO3 To 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 To 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 Soft Computing PC-CS- AIML-417A
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
VII Data Mining and Predictive Modelling PE-CS- AIML-419A
CO1 To learn, how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines and Bayesian network models.
CO2 To know the use of the binary classifier and numeric predictor nodes to automate model selection.
CO3 To advice on when and how to use each model. Also learn how to combine two or more models to improve prediction.
CO4 To Apply predictive modeling approaches using a suitable package such as SPSS Modeler
VII Big Data Analytics for Internet of Things PE-CS-AIML-421A
CO1 To Understand Big Data and its analytics in the real world.
CO2 To Analyze the Big Data framework like Hadoop and NOSQL to efficiently store and process Big Data to generate analytics.
CO3 To Design of Algorithms to solve Data Intensive Problems using MapReduce Paradigm
CO4 To Design and Implementation of Big Data Analytics using pig and spark to solve data intensive problems and to generate analytics.
VII Deep Learning PE-CS-AIML- 423A
CO1 To Recognize the characteristics of deep learning models that are useful to solve real-world problems.
CO2 To Understand different methodologies to create application using deep nets.
CO3 To Identify and apply appropriate deep learning algorithms for analyzing the data for variety of problems.
CO4 To Implement different deep learning algorithms. Design the test procedures to assess the efficacy of the developed model. Combine several models in to gain better results
VII Working with Raspberry pi & Arduino platform PE-CS- AIML-425A
CO1 To learn the embedded system and their working and IOT fundamentals.
CO2 To know the use of Arduino and its basic concepts. Also understand the various Arduino based projects.
VII Big Data Analytics for Internet of Things Lab PE-CS- AIML- 421LA
CO1 Install and use R for simple programming tasks. Extend the functionality of R by using add-on packages.
CO2 To perform basics statistical analysis Using R.
CO3 To Apply basic tools to carry out Exploratory data analysis.
CO4 To explore the components data science Process to interact via machine learning models.
VII Deep learning Lab  PC-CS- AIML- 423LA
CO1 To understand the deep learning.
CO2 To perform basics deep learning networks
CO3 To apply various deep learning networks in real world life..
CO4 To implement deep learning modules.
VII Working with Raspberry pi & Arduino platform Lab PE-CS- AIML- 425LA
CO1 To understand the Raspberry Pi.
CO2 To perform basics practicals using Arduino platform
CO3 To apply various Raspberry pi & Arduino platform in real world life..
CO4 To implement and connect with MySQL database
VII R Programming lab PC-CS- AIML- 407LA
CO1 Install and use R for simple programming tasks. Extend the functionality of R by using add-on packages.
CO2 To perform basics statistical analysis Using R.
CO3 To Apply basic tools to carry out Exploratory data analysis.
VIII Optimization Method in ML PC-CS-AIML-402A
CO1 To understand the basics of convex optimization.
CO2 To learn the basics of gradient based methods.
CO3 To apply the operator’s splitting methods.
VIII Cyber Law and Ethics OE-CS-AIML-402
CO 1 To give overview of Cyber Ethics, Intellectual Property Right and Trademark Related laws with respect to Cyber Space.
CO 2 To analyze and evaluate existing legal framework and laws on cyber security.
CO 3 To analyze and evaluate the Intellectual rights and copyrights.
CO 4 To understand cyber ethics.
VIII Entrepreneurship and Start-ups  HSS-404A
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 Cryptographic Fundamentals OE-CS-AIML- 404
CO1 To Student will be able to understand basic cryptographic algorithms.
CO2 To Able to understand the fundamental ideas of public-key cryptography.
CO3 To Analyze and compare symmetric-key encryption public-key encryption schemes based on different security models
CO4 To Able to understand the PKI infrastructure.
VIII Network Operating System OE-CS-AIML-406
CO1 To Identify the features of modern Microsoft operating systems including UNIX and UNIX-like operating systems.
CO2 To Explain the fundamentals of operating system and its use in network communication.
CO3 To Analyze how to manage user accounts, group accounts, and shared resources.
CO4 To Devise a security policy for your client and server computers.
VIII Reasoning, Problem Solving and Robotics  OE-CS-AIML-408
CO1 To list and explain the basic elements of robots
CO2 To analyze robot kinematics and its control methods
CO3 To Classify the various sensors used in robots for better performance
CO4 To summarize various industrial and non-industrial applications of robots
VIII Image Processing and Recognition OE-CS- AIML- 410
CO 1 To Understand Basics of Image formation and transformation using sampling and quantization
CO 2 To Understand different types signal processing techniques used for image sharpening and smoothing
CO 3 To understand the nature and inherent difficulties of the pattern recognition problems.
CO 4 Understand concepts, trade-offs, and appropriateness of the different feature types and classification techniques such as Bayesian, maximum likelihood, etc
VIII Social Networks PE-CS-AIML-416A
CO1 To Demonstrate proficiency in the use of social networks for business and personal use
CO2 To Demonstrate proficiency in the use of social network analysis concepts and techniques.
CO3 To Demonstrate proficiency in the use of social network developer tools.
CO4 To Examine the various types of processors and demonstrate proficiency in the use of social network concepts for solving real world issues.
VIII Neural Network and Fuzzy Logic systems PE-CS-AIML- 420A
CO1 To The course is designed to give a solid grounding of fundamental concepts of fuzzy logic and its applications. The level of the course is chosen to be such that all students aspiring to be a part of computational intelligence directly or indirectly in near future should get a foundation of these concepts through this course.
CO2 To Understanding reasoning and fuzzy logic for artificial intelligence
CO3 To learn defuzzification and fuzzy measures
CO4 To students will be able to learn the applications of fuzzy logic and hybrid soft computing techniques
VIII Augmented Reality PE-CS-AIML- 422A
CO1 The course is designed to describe how VR systems work and list the applications of VR.
CO2 Understand the design and implementation of the hardware that enables VR systems to be built
CO3 Understand the system of human vision and its implication on perception and rendering
CO4 Explain the concepts of motion and tracking in VR systems and Describe the importance of interaction and audio in VR systems
VIII Advance Machine Learning PE- CS- AIML-424A
CO1 To understand advanced methods of machine learning.
CO2 To Emphasis on approaches of deep learning with practical relevance.
CO3 To Analyze recent applications of advanced machine learning.
CO4 To Understand implementation of advanced machine learning.
VIII Natural Language Processing PE-CS-AIML-426A
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.
VIII Optimization Lab PC-CS-AIML-406LA
CO1 Apply mathematical and computational skills needed for the practical utility operation research.
CO2 Implement various linear programming problems
CO3 Implement various optimization methods in machine learning
CO4 Understand and implement genetic algorithms
VIII Advance AI Application Lab PC- CS- AIML- 408LA
CO1 Implementation of various type of algorithm in AI applications for better use of application
CO2 In-depth learning of machine learning, Deep learning and neural networks
CO3 Implement various artificial intelligence technique
CO4 Understand artificial intelligence and its analytics in real world
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