B. Tech Fourth Year Computer Science Subjects and Syllabus

Syllabus of B Tech in Computer Science Fourth  year  contains mostly the elective subjects. Which is not much important for gate perspective as well as Interview Preparation.  And Because of All Universities are following AICTE curriculum So each have similar syllabus.

Some of Fourth year Elective Subjects are Distributed System, Artificial Intelligence, Embedded Systems, Application Of Soft Computing, Cloud Computing etc.

List of Third Year Computer Science Subjects​

Subjects         Topics
Distributed System
  • Introduction and Characterization of Distributed Systems
  • Distributed Mutual Exclusion
  • Agreement Protocol
  • Failure Recovery in Distributed Systems
  • Transactions and Concurrency Control
Artificial Intelligence
  • Introduction to Artificial Intelligence
  • Introduction to Search
  • Knowledge Representation & Reasoning
  • Machine Learning
  • Pattern Recognition
Embedded Systems
  • Introduction to Embedded Systems
  • Embedded Networking
  • Embedded Firmware Development Environment
  • RTOS Based Embedded System Design
  • Embedded System Application Development
Application of Soft Computing
  • Neural Networks-I (Introduction & Architecture)
  • Neural Networks-II (Back propogation networks)
  • Fuzzy Logic-I (Introduction)
  • Fuzzy Logic –II (Fuzzy Membership, Rules)
  • Genetic Algorithm(GA)
Cloud Computing
  • Introduction to Cloud Computing
  • Cloud Enabling Technologies
  • Cloud Architecture, Services AND Storage
  • Resource Management And Security In Cloud
  • Cloud Technologies And Advancement
Agile Software Development
  • Agile Methodology
  • Agile Processes
  • Agility And Knowledge Management
  • Agility And Requirements Engineering
  • Agility And Quality Assurance
Machine Learning
  • Introduction to Machine Learning
  • Decision Tree Learning and Artificial Neural Networks
  • Evaluating Hypotheses And Bayesian Learning
  • Computational Learning Theory
  • Genetic Algorithms
Image Processesing
  • Digital Image Fundamentals
  • Image Enhancement
  • Image Restoration
  • Image Segmentation
  • Image Compression And Recognition

Syllabus of Third Year Computer Science​


Unit/Module 1 : Introduction and Characterization of Distributed Systems

Characterization of Distributed Systems: Introduction, Examples of distributed Systems, Resource sharing and the Web Challenges. Architectural models, Fundamental Models. Theoretical
Foundation for Distributed System: Limitation of Distributed system, absence of global clock, shared memory, Logical clocks ,Lamport’s & vectors logical clocks. Concepts in Message Passing
Systems: causal order, total order, total causal order, Techniques for Message Ordering, Causal ordering of messages, global state, termination detection.

Unit/Module 2 : Distributed Mutual Exclusion
Distributed Mutual Exclusion: Classification of  distributed mutual exclusion, requirement of mutual exclusion theorem, Token based and non token based algorithms, performance metric for
distributed mutual exclusion algorithms. Distributed Deadlock Detection: system model, resource Vs communication deadlocks, deadlock prevention, avoidance, detection & resolution, centralized
dead lock detection, distributed dead lock detection, path pushing algorithms, edge chasing algorithms.

Unit/Module 3 : Agreement Protocol
Introduction, System models, classification of Agreement Problem,
Byzantine agreement problem, Consensus problem, Interactive consistency Problem, Solution to Byzantine Agreement problem, Application of Agreement problem, Atomic Commit in Distributed
Database system. Distributed Resource Management: Issues in distributed File Systems, Mechanism for building distributed file systems, Design issues in Distributed Shared Memory,
Algorithm for Implementation of Distributed Shared Memory.

Unit/Module 4 : Failure Recovery in Distributed Systems
Failure Recovery in Distributed Systems: Concepts in Backward and Forward recovery, Recovery in Concurrent systems, Obtaining consistent Checkpoints, Recovery in Distributed Database Systems.
Fault Tolerance: Issues in Fault Tolerance, Commit Protocols, Voting protocols, Dynamic voting protocols.


Unit/Module 5 : Transactions and Concurrency Control
Transactions and Concurrency Control: Transactions, Nested transactions, Locks, Optimistic Concurrency control, Timestamp ordering, Comparison of methods for concurrency control.
Distributed Transactions: Flat and nested distributed transactions, Atomic Commit protocols, Concurrency control in distributed transactions, Distributed deadlocks, Transaction recovery.
Replication: System model and group communication, Fault – tolerant services, highly available services, Transactions with replicated data.


Unit/Module 1 : Introduction to Artificial Intelligence Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing.


Unit/Module 2 : Introduction to Search
Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha – Beta pruning 


Unit/Module 3 : Knowledge Representation & Reasoning
Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility
theory, Hidden Markov Models (HMM), Bayesian Networks.


Unit/Module 4 : Machine Learning 

Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm, Reinforcement learning


Unit/Module 5 : Pattern Recognition :Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parameter estimation methods – Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbor (NN) Rule, Bayes
Classifier, Support Vector Machine (SVM), K – means clustering.


Unit/Module 1 : Introduction to Embedded Systems

Introduction to Embedded Systems – The build process for
embedded systems- Structural units in Embedded processor , selection of processor & memory devices- DMA – Memory
management methods- Timer and Counting devices, Watchdog Timer, Real Time Clock, In circuit emulator, Target Hardware Debugging.


Unit/Module 2 : Embedded Networking

Embedded Networking: Introduction, I/O Device Ports & Buses– Serial Bus communication protocols – RS232 standard – RS422 – RS485 – CAN Bus -Serial Peripheral Interface (SPI) – Inter Integrated Circuits (I2C) –need for device drivers.

Unit/Module 3 : Embedded Firmware Development Environment

Embedded Product Development Life Cycleobjectives, different phases of EDLC, Modelling of EDLC; issues in Hardware-software Co-design, Data Flow Graph, state machine model, Sequential Program Model, concurrent Model, object oriented Model.

Unit/Module 4 : RTOS Based Embedded System Design Introduction to basic concepts of RTOS- Task, process
& threads, interrupt routines in RTOS, Multiprocessing and Multitasking, Preemptive and non preemptive scheduling, Task communication shared memory, message passing-, Inter process
Communication – synchronization between processes-semaphores, Mailbox, pipes, priority inversion, priority inheritance, comparison of Real time Operating systems: Vx Works, чC/OS-II, RT Linux.


Unit/Module 5 : Embedded System Application Development

Design issues and techniques Case Study of Washing Machine- Automotive Application- Smart card System Application.


Unit/Module 1 : Neural Networks-I (Introduction & Architecture)

Neuron, Nerve structure and synapse, Artificial Neuron and its model, activation functions, Neural network architecture: single layer and multilayer feed forward networks, recurrent networks. Various learning techniques; perception and convergence rule, Auto-associative and hetro-associative memory.

Unit/Module 2 : Neural Networks-II (Back propogation networks)
Architecture: perceptron model, solution, single layer artificial neural network, multilayer perception model; back propogation learning methods, effect of learning rule co-efficient ;back propagation algorithm, factors affecting backpropagation training, applications.


Unit/Module 3 : Fuzzy Logic-I (Introduction)

Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set
theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp


Unit/Module 4 : Fuzzy Logic –II (Fuzzy Membership, Rules)

Membership functions, interference in fuzzy logic,
fuzzy if-then rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications & Defuzzificataions,
Fuzzy Controller, Industrial applications


Unit/Module 5 : Genetic Algorithm(GA)

Basic concepts, working principle, procedures of GA, flow chart of GA, Genetic representations, (encoding) Initialization and selection, Genetic operators, Mutation, Generational Cycle, applications.


Unit/Module 1 : Introduction to Cloud Computing

Definition of Cloud – Evolution of Cloud Computing – Underlying Principles of Parallel and Distributed Computing – Cloud Characteristics – Elasticity in Cloud – On-demand Provisioning.


Unit/Module 2 : Cloud Enabling Technologies
Service Oriented Architecture – REST and Systems of Systems – Web Services – PublishSubscribe Model – Basics of Virtualization – Types of Virtualization – Implementation Levels of Virtualization – Virtualization Structures – Tools and Mechanisms – Virtualization of CPU – Memory – I/O Devices –Virtualization Support and Disaster Recovery.


Unit/Module 3 : Cloud Architecture, Services AND Storage

Layered Cloud Architecture Design – NIST Cloud Computing Reference Architecture – Public, Private and Hybrid Clouds – laaS – PaaS – SaaS – Architectural Design Challenges – Cloud Storage – Storage-as-a-Service – Advantages of Cloud Storage – Cloud Storage Providers – S3.


Unit/Module 4 : Resource Management And Security In Cloud
Inter Cloud Resource Management – Resource Provisioning and Resource Provisioning Methods – Global Exchange of Cloud Resources – Security Overview – Cloud Security Challenges –
Software-as-a-Service Security – Security Governance – Virtual Machine Security – IAM – Security Standards.


Unit/Module 5 : Cloud Technologies And Advancement
Hadoop – MapReduce – Virtual Box — Google App Engine – Programming Environment for Google App Engine –– Open Stack – Federation in the Cloud – Four Levels of Federation – Federated Services and Applications – Future of Federation


Unit/Module 1 : Agile Methodology

Theories for Agile Management – Agile Software Development – Traditional Model vs. Agile Model – Classification of Agile Methods – Agile Manifesto and Principles – Agile Project Management – Agile Team Interactions – Ethics in Agile Teams – Agility in Design, Testing – Agile Documentations – Agile Drivers, Capabilities and Values


Unit/Module 2 : Agile Processes
Lean Production – SCRUM, Crystal, Feature Driven Development- Adaptive Software Development – Extreme Programming: Method Overview – Lifecycle – Work Products, Roles and Practices.


Unit/Module 3 : Agility And Knowledge Management
Agile Information Systems – Agile Decision Making – Earl‗S Schools of KM – Institutional Knowledge Evolution Cycle – Development, Acquisition, Refinement, Distribution, Deployment ,
Leveraging – KM in Software Engineering – Managing Software Knowledge – Challenges of Migrating to Agile Methodologies – Agile Knowledge Sharing – Role of Story-Cards – Story-Card Maturity Model (SMM).


Unit/Module 4 : Agility And Requirements Engineering
Impact of Agile Processes in RE–Current Agile Practices – Variance – Overview of RE Using Agile – Managing Unstable Requirements – Requirements Elicitation – Agile Requirements Abstraction Model – Requirements Management in Agile Environment, Agile Requirements Prioritization – Agile Requirements Modeling and Generation – Concurrency in Agile Requirements Generation.


Unit/Module 5 : Agility And Quality Assurance
Agile Product Development – Agile Metrics – Feature Driven Development (FDD) – Financial and Production Metrics in FDD – Agile Approach to Quality Assurance – Test Driven Development –
Agile Approach in Global Software Development.


Unit/Module 1 : Introduction to Machine Learning

Well defined learning problems, Designing a Learning System, Issues in Machine Learning; THE CONCEPT LEARNING TASK – General-to-specific ordering of hypotheses, Find-S, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias


Unit/Module 2 : Decision Tree Learning and Artificial Neural Networks

Decision tree learning algorithm-Inductive bias- Issues in
Decision tree learning;
ARTIFICIAL NEURAL NETWORKS – Perceptrons, Gradient descent and the Delta rule, Adaline, Multilayer networks, Derivation of backpropagation rule Backpropagation AlgorithmConvergence,


Unit/module 3 : Evaluating Hypotheses And Bayesian Learning

Evaluating Hypotheses: Estimating Hypotheses Accuracy, Basics of sampling Theory, Comparing Learning Algorithms; Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm


Unit/Module4 : Computational Learning Theory
Sample Complexity for Finite Hypothesis spaces, Sample
Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning; INSTANCE-BASED LEARNING – k-Nearest Neighbour Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning


Unit/Module 5 : Genetic Algorithms
An illustrative example, Hypothesis space search, Genetic Programming, Models of Evolution and Learning; Learning first order rules-sequential covering algorithmsGeneral to specific beam search-FOIL; REINFORCEMENT LEARNING – The Learning Task, Q


Unit/Module 1 : Digital Image Fundamentals

Steps in Digital Image Processing – Components –
Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels – Color image fundamentals – RGB, HSI models, Two-dimensional mathematical preliminaries, 2D transforms – DFT, DCT.



Unit/Module 2 : Image Enhancement
Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–Smoothing and Sharpening Spatial Filtering, Frequency Domain: Introduction to Fourier Transform– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters, Homomorphic filtering, Color image enhancement.


Unit/Module 3 : Image Restoration
Image Restoration – degradation model, Properties, Noise models – Mean Filters – Order Statistics– Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch
Filtering – Inverse Filtering – Wiener filtering.



Unit/Module 4 : Image Segmentation
Edge detection, Edge linking via Hough transform – Thresholding – Region based segmentation – Region growing – Region splitting and merging – Morphological processing- erosion and dilation,
Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed segmentation algorithm.


Unit/Module 5 : Image Compression And Recognition

Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, JPEG standard, MPEG. Boundary representation, Boundary description, Fourier Descriptor, Regional
Descriptors – Topological feature, Texture – Patterns and Pattern classes – Recognition based on

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