COURSE OUTCOMES (COs)

ME Computer Science and Engineering

At the end of the course, the student should be able to:

Machine Learning

CO1:Students will be able to Design hypothesis model for any real-life problem.
CO2:Apply linear regression, logistic regression and regularization to any machine learning problem.
CO3:Apply learning techniques like decision tress, bayesian theory, clustering, SVM, ANN, etc., to solve a real-life problem.
CO4:Evaluate and perform diagnoses of any machine learning system.


Computer Algorithms

CO1:To know problem solving techniques.
CO2:To understand advanced data structures: Red-Black Trees, B-Trees, Binomial Heap, Fibonacci Heap Data Structures for Disjoint Sets.
CO3:To understand Graph algorithm: Search algorithms, computation of strongly connected components, shortest distance algorithms, minimum spanning tree algorithms.
CO4:To understand geometric algorithm,string matching,matrix algorithms,Polynomial computation algorithms.


Advanced Computer Networks

CO1: To Understand Concepts of Computer N/Ws, TCP/IP reference model.
CO2:To study Interior and Exterior Gateways routing application layered protocols such as DHCP, BOOTP OSI, TCP/IP, ATMX.25, frame relay, switching techniques in communication system.
CO3:Introduction of SONET/SDH optical networks.
CO4:Study evoluation of optical system.
CO5:Brief introduction of TDM & WDM Network elements.
CO6:The students will learn TCP/IP , Interior & Exterior routing protocols ,TDM &WDM SONET/SDH associated with the computer networks.


Cloud Computing

CO1:To know the Distributed Computing &Cluster computing.
CO2: To understand Cloud Computing, Basics of the emerging cloud computing paradigm, Cloud Benefits Characteristics .
CO3: To understand the Service models.
CO4: Study KVM,VM Scheduling.
CO5: Study disaster recovery, cloud security .
CO6:The student will get an understanding of cloud computing and virtualization concepts.


Natural Language Processing

CO1:Introduction To Machine Learning & NLP.
CO2:The field of Natural Language Processing (NLP), also called Computational Linguistics or Human Language Technology, studies the processing of human languages, from both a linguistic and a computational / technological perspective.
CO3:Students will gain an in-depth understanding of the computational properties of natural languages and the commonly used algorithms for processing linguistic information. The course examines NLP models and algorithms using both the traditional symbolic and the more recent statistical approaches.
CO4:By the end of the course, Student should have a sense of the capabilities and limitations of current natural language technologies, and some of the algorithms and techniques that underlie these technologies. They should also understand the theoretical underpinnings of natural language processing in linguistics and formal language theory.


SOCIAL NETWORK ANALYSIS

CO1:Classify social networks .
CO2: Analyze social media and networking data.
CO3: Apply Social networks Visualization tools.
CO4: Identify application driven virtual communities from social networks.
CO5:Analyze the social data using graph theoretic computing approach .
CO6:Apply sentiment mining.


Software Testing

CO1:Understand the basic concepts of software testing.
CO2:After studying this unit, you will be able to: State the importance of testing in the software development life cycle.
CO3:Describe the evolution and types of software testing.
CO4:Define a bug and illustrate the reason of occurrence of a bug.
CO5:Describe the software development models.
CO6: Perform effective and efficient structural testing of software .


Algorithms For Big Data

CO1:The objectives of this subject are to: Introduce students the concept and challenge of big data (3 V’s: volume, velocity, and variety).
CO2: Teach students in applying skills and tools to manage and analyze the big data.
CO3:Upon completion of the subject, students will be able to: understand the concept and challenge of big data and why existing technology is inadequate to analyze the big data.
CO4: collect, manage, store, query, and analyze various form of big data.
CO5: gain hands-on experience on large-scale analytics tools to solve some open big data problems; and
CO6: understand the impact of big data for business decisions and strategy.


Real Time System

CO1:Introduction of the real-time systems.
CO2:Computing required for the real-time embedded systems.
CO3:Communication required for the real-time embedded systems.
CO4: Present an overview of the real-time embedded systems in practice.
CO5:After succefully completing these course students shall be able:To present the mathematical model of the system.
CO6: To develop real-time algorithm for task scheduling.


Cryptography & Network Security

CO1:To discuss on various types of attacks and their characteristics.
CO2: To illustrate the basic concept of encryption and decryption for secure data transmission.
CO3: To Analyse and compare various cryptography techniques.
CO4:To explain the concept of digital signature and its applications.


Intrusion Detection System

CO1:Explain The Fundamental Concepts Of Network Protocol Analysis And Demonstrate The Skill To Capture And Analyse Network Packets.
CO2:Use various protocol analysers and Network Intrusion Detection Systems as security tools to detect network attacks and troubleshoot network problems.
CO3:Intrusion Detection System (IDS) are software or hardware systems that automate the process of monitoring and analyzing the events that occur in a computer network, to detect malicious activity.
CO4:Since the severity of attacks occurring in the network has increased drastically, Intrusion detection system have become a necessary addition to security infrastructure of most organizations.
CO5:Intrusion detection systems (IDS) take either network or host based approach for recognizing and deflecting attacks.
CO6:Intrusion detection allows organization to protect their systems from the threats that come with increasing network connectivity and reliance on information systems.


MODEL CHECKING (Elective-2)

CO1:Understand the concept of verification of a concurrent system and the main issues related to the model checking problem.
CO2:Define the formalism of transitions systems for modelling concurrent systems using the concept of state variables.
CO3:Illustrate the concepts of parallel composition, interleaving, synchronous communication, synchronized product between transition systems.
CO4:Have knowledge of the type of properties that can be expressed on a trace of a transition system:safety and liveness properties; explain the concept of fairness.
CO5:Have knowledge of the main concepts of automata theory applied to system verification, in particular the algorithms for verifying regular safety properties and omega-regular properties.
CO6:Define the Linear Time Logic (LTL) and illustrate the automata-based algorithms for the model checking of an LTL formula.


Artificial Intelligence-: representation and reasoning

CO1:A student completing this AI course will be able to : Understand and explain the basic knowledge representation, problem solving and reasoning methods in artificial intelligence Assess the applicability, strength, and weaknesses of the basic knowledge representation, problem solving and reasoning in solving particular engineering problems.
CO2:Develop intelligent systems by assembling solutions to concrete computational problems.
CO3:Understand the role of knowledge representation, problem solving and reasoning in intelligent-system engineering.
CO4:At the end of this course student should be able to solve particular problem using algorithmic techniques.


High Performance Computing

CO1:At the end of the module, a student will have an understanding of The role of HPC in science and engineering.
CO2:The most commonly used HPC platforms and parallel programming models.
CO3:The means by which to measure, analyse and assess the performance of HPC applications and their supporting hardware.
CO4:Mechanisms for evaluating the suitability of different HPC solutions to common problems found in Computational Science.
CO5:The role of administration, scheduling, code portability and data management in an HPC environment, with particular reference to Grid Computing.
CO6:The potential benefits and pitfalls of Grid Computing.


Introduction to Cognitive Science

CO1:Identify cognitive science as an interdisciplnary paradigm of study of cross-cutting areas such as Philosophy, Psychology, Neuroscience, Linguistics, Anthropology, and Artificial Intelligence.
CO2:Explain various processes of the mind such as memory and attention, as well asrepresentational and modeling techniques that are used to build computational models of mental processes.
CO3:Illustrate cognitive models such as IAM and modeling techniques such as logic programs and neural networks.
CO4:Design simple automata and develop logical deductions for problem solving that serve as the foundation of intelligent computer programs such as natural language processing applications (e.g. ALICE and ELIZA).
CO5:Read, understand, and summarize scientific articles in the different related areas of Cognitive Science.
CO6:Collaborate, communicate, and negotiate with peers as a team to produce a joint outcome.


Virtual Reality

CO1:The use of virtual reality (VR) in education can be considered as one of the natural evolutions of computer-assisted instruction (CAI) or computer-based training (CBT).
CO2:Studies show that a virtual environment can “stimulate learning and comprehension, because it provides a tight coupling between symbolic and experiential information.
CO3:At every level of education, virtual reality has the potential to make a difference, to lead learners to new discoveries, to motivate and encourage and excite.
CO4:Student Schould Understand Of Virtual Reality.


Mobile Computing

CO1:Objective of this course is: To provide guidelines, design principles and experience in developing applications for small, mobile devices, including an appreciation of context and location aware services.
CO2:To develop an appreciation of interaction modalities with small, mobile devices (including interface design for non-standard display surfaces) through the implementation of simple applications and use cases.
CO3:To introduce wireless communication and networking principles, that support connectivity to cellular networks, wireless internet and sensor devices.
CO4:To understand the use of transaction and e-commerce principles over such devices to support mobile business concepts.
CO5: To appreciate the social and ethical issues of mobile computing, including privacy.
CO6:At the end of the module, the student will be able to demonstrate: A working understanding of the characteristics and limitations of mobile hardware devices including their user-interface modalities.


Stoarage Systems

CO1:Student should be able to understand the concept of Information retrieval.
CO2: Student should be able to deal with storage and retrieval process of text and multimedia data.
CO3:Student should be able to evaluate performance of any information retrieval system.
CO4:Student should be able to understand importance of recommender system.


Functional Programing

CO1:At the end of the course the student will be able to:Write programs in a functional style.
CO2:students will be able to:Write simple programs involving elementary Haskell techniques, including pure function definitions;
CO3:Use standard combinators for operating on lists.
CO4: Define new algebraic data types and use recursion to define functions that traverse recursive types.
CO5:Demonstrate understanding of how to express data structures and function interfaces using types, and how to infer types.
CO6: Demonstrate understanding of how to structure programs using monads, how to use the most common standard monads (including IO, Maybe, and State), and how to use a monad transformer.


Object Oriented System

CO1:Master the fundamental principles of OO programming,
CO2: Master key principles in OO analysis, design, and development.
CO3:Be familiar with the application of the Unified Modeling Language (UML) towards analysis and design.
CO4:Master common patterns in OO design and implement them.
CO5:Be familiar with alternative development processes.
CO6:Be familiar with group/team projects and presentations.


Pattern Recognition

CO1:To explain the concept of pattern recognition and its different phases.
CO2: To discuss on the idea of feature extraction and different approaches towards prototype selection.
CO3:To illustrate the Support Vector Machine and its application in real life problem solving.
CO4:At the end of course student will be able to:‘Read’ facial expressions .
CO5:Recognising Speech.
CO6:Reading a Document.


Reinforcement Learning

CO1:Objective of this course is The first is to prepare student for conducting research in reinforcement learning.
CO2:The second is to provide you with the required knowledge to apply reinforcement learning techniques to novel applications.
CO3:Reinforcement learning is a framework for modeling the an autonomous agent’s interaction with an unknown world.
CO4:The agent’s objective is to learn the effects of it’s actions, and modify its policy in order to maximize future reward.
CO5:The study of Reinforcement learning emphasizes a learning approach to artificial intelligence.
CO6:Unlike supervised learning, the agent is not explicitly told the correct answers (labels), rather an RL agent must learn only from reward and trial and error interaction with the world.


Data Science

CO1:has broad insight, understanding and intuition of the whole process line of extracting knowledge from data.
CO2:has a deep insight in one of the specialisations within the program, depending on the study and the choice of the master project.
CO3:has solid knowledge in a broad range of methods based on statistics and informatics and can use these for data management, analysis and problem solving.
CO4:has experience in deriving theoretical properties of methods involved in Data Science.
CO5:has experience in implementation/modification of methods involved in Data Science.
CO6:In this master's programme you will get a solid basis in the informatics and statistical methodology necessary for working within Data Science. Depending on your interest you can specialise into different topics.


Software Architecture

CO1: Argue the importance and role of software architecture in large-scale software systems.
CO2: Design and motivate software architecture for large-scale software systems.
CO3:Recognise major software architectural styles, design patterns, and frameworks.
CO4:Describe a software architecture using various documentation approaches and architectural description languages.
CO5:Generate architectural alternatives for a problem and selection among them.
CO6:Use well-understood paradigms for designing new systems.


Seminar-I

CO1:To survey selected topics addressing issues of science in society today.
CO2:To assimilate, synthesize and integrate information related to a topic.
CO3:Collect, Organize &Analyze information about emerging technologies /market demands/current trends.
CO4:Exhibit effective communication skills, stage courage, and confidence.
CO5: Demonstrate intrapersonal skills.
CO6:Prepare a well organized report employing elements of technical writing and critical thinking.


Project Phase –I

CO1:To become familiar with the process of undertaking literature survey /performing industrial visit and identifying the problem statement.
CO2: To apply algorithmic strategies while solving problems.
CO3:After completing this course, students will be able to: Identify and finalize problem statement by surveying variety of domains.
CO4: Perform requirement analysis and identify design methodologies.
CO5: Design innovative idea for solving the problem.
CO6:Apply advanced programming techniques.


Project Phase – II

CO1:After successful completion of this course, students will be able to: Develop solutions for framed problem statement.
CO2: Test and analyze different modules of planned project and integrate them into a single module.
CO3: Implement hardware and/or software techniques for identified problem.
CO4:Prepare project report and deliver presentation.