Pre-Ph.D. Coursework Notes - Computer Science & Engineering

Chaudhary Charan Singh University, Meerut

Pre-Ph.D. Course-Work Programme

  • Curriculum & Syllabus
  • Session 2023-2024
  • Department of Computer Science & Engineering

Semester-wise Paper Details

  • Core Paper-1: Research Methodology (Credits: 04, Duration: 60 hrs)
  • Core Paper-2: [Subject Area] (Credits: 04, Duration: 60 hrs)
  • Survey/research project (Credits: 04, Duration: 60 hrs)

CORE PAPER-I: COMPUTER SCIENCE & ENGINEERING

Course Objectives:
  1. Discuss Digital Logic & Computer Organization and Architecture.
  2. Discuss Software Engineering.
  3. Discuss the principles of computer networks.
  4. Discuss Database Management System and Data Structures.
  5. Discuss Operating System concepts and procedures.
Course Outcomes:
  • CO1: Apply concepts of Digital Binary System and Computer Organization and Architecture.
  • CO2: Compare and contrast various methods for software design.
  • CO3: Describe the functions of Network Layer.
  • CO4: Apply knowledge of databases for real-life applications and describe arrays, linked lists, stacks, queues, trees.
  • CO5: Learn various memory management schemes.
Unit I: Digital Logic
  • Topics:
    • Information representation
    • Computer arithmetic on fixed & floating-point numbers
    • Boolean algebra
    • Combinational circuits
    • Sequential circuits
    • Memory system
    • Processor organization
    • Input-output organization
    • Pipeline processing
    • Static & dynamic interconnection networks
  • Number of Lectures: 12
Unit II: Software Engineering
  • Topics:
    • Development models
    • Metrics
    • Software Project Management
    • Analysis
    • Design: System design, detailed design, function-oriented, object-oriented analysis & design, user interface design
    • Coding & Testing
    • Software quality & reliability
    • Object Modeling Technique (OMT) methodology
  • Number of Lectures: 12
Unit III: Computer Networks
  • Topics:
    • Reference Models
    • Data Communication
    • Internetworking: Components and issues
    • Media access controls
    • Virtual circuits & datagram's
    • Routing algorithms
    • Congestion control
    • Network Security
    • Firewalls
    • Internet architecture and protocols
  • Number of Lectures: 12
Unit IV: Database & Data Structures
  • Database Topics:
    • Three-schema Architecture and Data Independence
    • Data Models
    • E-R Model
    • Relational Data Model
    • SQL Programming Techniques
    • Relational Database Design
    • Functional Dependencies
    • Normalization
    • Query Processing and Optimization
    • Transaction Processing Concepts
    • Concurrency Control Techniques
    • Recovery Techniques
  • Data Structure Topics:
    • Arrays
    • String
    • Linked Lists
    • Stacks
    • Queues
    • Trees: Binary & Threaded Trees, traversal, Binary Search Tree, Huffman & AVL Trees, B Trees
    • Graphs: Adjacency Matrix, Path Matrix, Linked Representation, traversal
    • Searching & Sorting techniques
  • Number of Lectures: 12
Unit V: Operating System
  • Topics:
    • Multiprogramming, Multiprocessing & Multitasking
    • Memory Management
    • Virtual memory
    • Paging
    • Fragmentation
    • Concurrent Processing
    • CPU scheduling
    • I/O scheduling
    • Deadlock
    • System Software
    • Interpreter, compilers, Assemblers, Linkers
    • Information Retrieval Systems - public and deep web, web crawlers
  • Number of Lectures: 12
Teaching Learning Process:
  • Class discussions/demonstrations
  • PowerPoint presentations
  • Class activities/assignments
  • Field visits
  • Internship, etc.
Suggested Readings:
  1. M. Morris Mano and M. D. Ciletti, "Digital Design", Pearson Education.
  2. Silberschatz, Galvin, and Gagne, "Operating Systems Concepts", Wiley
  3. Sibsankar Halder and Alex A Aravind, "Operating Systems", Pearson Education
  4. Aaron M. Tenenbaum, Yedidyah Langsam and Moshe J. Augenstein, "Data Structures Using C and C++", PHI Learning Private Limited, Delhi India
  5. Horowitz and Sahani, "Fundamentals of Data Structures", Galgotia Publications Pvt. Ltd Delhi India.
  6. RS Pressman, Software Engineering: A Practitioners Approach, McGraw Hill.
  7. Pankaj Jalote, Software Engineering, Wiley.
  8. Korth, Silbertz, Sudarshan," Database Concepts", McGraw Hill.
  9. Leon & Leon,"Database Management Systems", Vikas Publishing House.
  10. Behrouz Forouzan, "Data Communication and Networking", McGraw Hill.
  11. Andrew Tanenbaum "Computer Networks", Prentice Hall.
  12. John.R.Larne, "Linkers and Loaders", Marfan Kaufmann Pub.

CORE PAPER-II: COMPUTER SCIENCE & ENGINEERING

Course Objectives:
  1. Identify and discuss the role and importance of AI.
  2. Identify and discuss the issues and concepts Natural language process.
  3. Identify and discuss basic of IoT and Challenges in IoT Design challenges.
  4. Identify and discuss the concepts Deep learning.
  5. Identify & understands Cloud Computing Services etc.
Course Outcomes:
  • CO1. Understand the basics of the theory and practice of Artificial Intelligence as a discipline and about intelligent agents
  • CO2. To learn the fundamentals of natural language processing and explain the challenges of NLP.
  • CO3. Demonstrate basic concepts, principles and challenges in IoT.
  • CO4. To design appropriate machine learning algorithms and apply the algorithms to real-world problems.
  • CO5. Describe architecture and underlying principles of cloud computing and analyze advanced cloud technologies.
Unit I: Artificial Intelligence
  • Topics:
    • AI: Characteristics of Intelligent Agents- Typical Intelligent Agents
    • Problem-Solving Approach to Typical AI problems.
    • SOFTWARE AGENTS: Architecture for Intelligent Agents-Agent communication-Negotiation and Bargaining - Argumentation among Agents - Trust and Reputation in Multi-agent systems.
  • Number of Lectures: 12
Unit II: Problem Solving Methods
  • Topics:
    • Problem-solving Methods - Search Strategies- Uninformed-Informed - Heuristics - Local Search Algorithms and Optimization Problems Searching with Partial Observations
    • Constraint Satisfaction Problems Constraint Propagation Backtracking Search - Game Playing - Optimal Decisions in Games - Alpha - Beta Pruning - Stochastic Games.
  • Number of Lectures: 12
Unit III: Natural Language Processing (NLP)
  • Topics:
    • NLP: Origins and challenges of NLP
    • Language Modeling: Grammar-based LM, Statistical LM - Regular Expressions, Finite-State Automata - English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance.
  • Number of Lectures: 12
Unit IV: Word Level Analysis & Internet of Things (IoT)
  • Word Level Analysis Topics:
    • Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Back off- Word Classes, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues i PoS tagging - Hidden Markov and Maximum Entropy models.
  • IoT Topics:
    • Internet of Things (IoT): Vision, Definition, Conceptual Framework, Architectural view, technology behind IoT, Sources of the IoT, M2M Communication, IoT Examples.
    • Design Principles for Connected Devices: IoT/M2M systems layers and design standardization, communication technologies, data enrichment and consolidation, ease of designing and affordability.
    • Challenges in IoT Design challenges: Development Challenges, Security Challenges, Other challenges IoT Applications: Smart Metering, E-health, City Automation, Automotive Applications, home automation, smart cards, communicating data with H/W units, mobiles, tablets, Designing of smart street lights in the smart city.
  • Number of Lectures: 12
Unit V: Machine Learning & Deep Learning and Cloud Computing
  • Machine Learning & Deep Learning Topics:
    • DEEP NETWORKS: History of Deep Learning- A Probabilistic Theory of Deep Learning, Backpropagation and regularization, batch normalization- VC Dimension and Neural Nets-Deep Vs Shallow Networks-Convolutional Networks- Generative Adversarial Networks (GAN), Semi-supervised Learn.
  • Cloud Computing Topics:
    • Introduction To Cloud Computing, Cloud Enabling Technologies Service Oriented Architecture, Cloud Architecture, Resource Management and Security
    • Cloud Technologies and Advancements Hadoop And Storage, Services in Cloud,
  • Number of Lectures: 12
Teaching Learning Process:
  • Class discussions/ demonstrations
  • Power Point presentations
  • Class activities/ assignments
  • Field visits
  • Internship, etc.
Suggested Readings:
  1. David L. Poole and Alan K. Mackworth, -Artificial Intelligence: Foundations of Computational Agentsl, Cambridge University Press, 2010.
  2. Nils J. Nilsson, -The Quest for Artificial Intelligencel, Cambridge University Press, 2009.
  3. M. Tim Jones, -Artificial Intelligence: A Systems Approach (Computer Science) I, Jones and Bartlett Publishers, Inc.First Edition, 2008
  4. S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approachl, Prentice Hall, Third Edition, 2009.
  5. Daniel Jurafsky, James H. Martin-Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication, 2014.
  6. Steven Bird, Ewan Klein and Edward Loper, -Natural Language Processing with Python, First Edition, OReilly Media, 2009.
  7. Lawrence Rabiner And Biing-Hwang Juang, "Fundamentals of Speech Recognition", Pearson Education, 2003
  8. Olivier Hersent, DavidBoswarthick, Omar Elloumi"The Internet of Things key applications and protocols", willey
  9. Jeeva Jose, Internet of Things, Khanna Publishing House.
  10. Tom M. Mitchell, -Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
  11. Adrian McEwen, HakinCassimally "Designing the Internet of Things" Wiley India
  12. Ethem Alpaydin, -Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press 2004.
  13. Stephen Marsland, -Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
  14. Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag..
  15. Deng & Yu, Deep Learning: Methods and Applications, Now Publishers, 2013.
  16. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.
  17. Michael Nielsen. Neural Networks and Deep Learning. Determination Press. 2015.
  18. Kai Hwang, Geoffrey C. Fox, Jack G. Dongarra, “Distributed and Cloud Computing, From Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
  19. Rittinghouse, John W., and James F. Ransome, -Cloud Computing: Implementation, Management and Security. CRC Press, 2017.
  20. George Reese, "Cloud Application Architectures: Building Applications and Infrastructure in the Cloud: Transactional Systems for EC2 and Beyond (Theory Practice), O'Reilly, 2009