Syllabus for
Master of Technology (Computer Science and Engineering)
Academic Year  (2024)

The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2011 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: Develop and design real time projects more efficiently using math, statistics and analytics tools to deliver quality software solutions.

PO5: Analyze and apply the needs of computing in the society to promote novel and sustainable research ideas.

Assessments(Theory & Practical)

·         Continuous Internal Assessment (CIA) for Theory = 100 Marks

•   Continuous Internal Assessment (CIA) for Practical  = 50 Marks

This course is aimed at providing a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments.

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security. Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities.

Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse. Vulnerabilities -  Phishing -  Online Attacks – Pharming - Phoarging  –  Cyber Attacks  -  Cyber Threats -  Zombie- stuxnet - Denial of Service Vulnerabilities  - Server Hardening-TCP/IP attack-SYN Flood.

Cyber Security Management Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle. Security Policy Management - Security Policies - Coverage Matrix. Business Continuity Planning - Disaster Types - Disaster Recovery Plan - Business Continuity Planning Process.

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box Architectural Integration:  Security Zones - Devices viz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security.

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services. Securing Communications:  Securing Services - Transport – Wireless - Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems:   Intrusion - Defense in Depth - IDS/IPS -IDS/IPS Weakness and Forensic Analysis. Cyber Evolution: Cyber Organization - Cyber Future

R2. AtulKahate, “Cryptography and Network Security”, 2nd Edition, Tata McGrawHill.

R3. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1st Edition 2011.

R4. .Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, “Cyber Security Policy Guidebook”, Wiley; 1 edition , 2012

R5. Dan Shoemaker and Wm. Arthur Conklin, “Cybersecurity: The Essential Body Of Knowledge”,   Delmar Cengage Learning; 1 edition (May 17, 2011) .

R6.William Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3^rdEdition2002.

Course Description: 

In almost every field of computer science, efficient algorithms are required. Nonetheless, there might be significant regional differences in the type of problem to be solved, the idea of what algorithms are "efficient," and even the model of computation. Many of the techniques that are broadly applicable in the creation of efficient algorithms are examined in this course, and their use in a variety of application areas and computational models is investigated.

Course Objectives:

• To learn the systematic way of solving problems.
• To understand the different methods of organizing large amounts of data.
• To efficiently implement the different data structures.
• To efficiently implement solutions for specific problems.

Review of Analysis Techniques: Growth of Functions: Asymptotic notations; Standard notations and common functions; Recurrences and Solution of Recurrence equations- The substitution method, The recurrence – tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods.

Graph Algorithms: Bellman - Ford Algorithm; Single source shortest paths in a DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford -Fulkerson method; Maximum bipartite matching.

Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT.

Number -Theoretic Algorithms: Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization.

String-Matching Algorithms: Naïve string Matching; Rabin - Karp algorithm; String matching with finite automata; Knuth-Morris-Pratt algorithm; Boyer – Moore algorithms.

Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms.

Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages.

1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.

1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
2. Tanenbaum A.S., Langram Y, Augestien M.J., ”Data Structures using Java”, Prentice Hall of India, 2009
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition,Pearson Education, 2012.
4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

1.      Students will be able to identify security breaches in a computer network

2.      Students will be able to discuss major issues concerning computer security.

3.      Students will know how to use standard security tools to locate and fix security leaks in a computer network.

4.      Students will have an understanding of a variety of cryptographic algorithms and protocols underlying network security applications.

5.      Students will have an understanding of system-level security issues.

OWASP Top 10 –2021, Injection and Inclusion, Cross Site Scripting, Injection in stored procedures, Denial of Service, Buffer Overflows and Input Validation, Access Control, DevOps Security, API Security, OWASP top 10 Cloud security Risks, Secure CodeReview, SAST and DAST tools, Case Study on Web Application Framework, use browser-jsguard Firefox add-on also to detect Malicious and Suspicious Webpages. Web Application Security Risks, Identifying the Application Security Risks, Threat Risk Modelling, Other HTTP fields, Data Extraction, Advanced Identification/Exploitation

Practical:

Experiment on the web application security.

Introduction to Android Architecture, Android File Structure, Android Build Process, Android App fundamentals, Android Security Model, Device Rooting, Android Debug bridge, Penetration Testing Tools, OWASP Top 10 Mobile App vulnerabilities, Attacks on Android Apps, Web based attacks on Android devices, Networks based attacks, Social Engineering attacks, Overview of Mobile Malware, Android App Analysis

Practical:

Experiment on the mobile security.

Introduction to Ethical Hacking, Identifying Different Types of Hacking  Technologies, Understanding the Different Phase Involved in Ethical Hacking, Types of Hacker Classes, Goals of Attackers, Functionality and Ease of Use Triangle, Ethical Hacking procedure, Creating a Security Evaluation Plan, Foot-printing and Social Engineering, Tracerouting, Network Scanning and Vulnerability Scanning, SYN, Stealth, XMAS, NULL, IDLE and FIN Scans, TCP Communication Flag Types, Banner Grabbing and OS Finger printing Techniques, Using Proxy servers in launching an Attack, Http tunneling Techniques, IP Spoofing Techniques, Enumeration, Password-cracking Techniques, Redirecting the SMB Logon to the attackers, SMB Redirection, SMB Relay MITM Attacks and Countermeasures, NetBIOS DOS Attacks, DDos Attack, Password-Cracking Countermeasures, Active/Passive online Attacks, Offline Attacks, Keyloggers and other Spyware Technologies, Trojans and Backdoors, Overt and Covert Channels, Reverse-connecting Trojans, Netcat Trojan, Indications of a Trojan Attacks, Wrapping, Trojan Construction Kit and Trojan Makers, The countermeasure Techniques in Preventing Trojans, Trojan Evading techniques, System File Verification, Virus and a Worm, Antivirus Evasion Techniques, Virus Detection Methods, Protocols Susceptible to Sniffing, Active and Passive Sniffing, ARP Poisoning, Ethereal Capture and Display Filters, MAC Flooding, DNS Hacking, DNS Spoofing Techniques, Sniffing Countermeasures, Types of DOS Attacks, Smurf Attacks, SYN Flooding, Spoofing vs Hijacking, Types of Session Hijacking, Steps to perform session Hijacking, Prevention of session Hijacking, Hacking Web Servers, Web Application Vulnerabilities, Web- Based Password Cracking Techniques, Wireless Hacking, WEP, WPA Authentication Mechanisms and Cracking Techniques, Wireless Sniffers and Locating SSIDS, Wireless hacking Techniques, Methods used to secure Wireless Networks, IDSs, Honeypots and Firewalls.

Practical:

Experiment on the ethical hacking.

R2: Maximum Linux Security, SAMS Books by Anonymous, 2000, ISBN: 0-672-31670-6.

R3: The Cuckoo's Egg : Tracking a Spy Through the Maze of Computer Espionage; by Clifford Stoll; Pocket Books; ISBN 0671726889

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

To correctly define and cite appropriate instances for the application of computer forensics Correctlycollect and analyze computer forensic evidence

Identify the essential and up-to-date concepts, algorithms, protocols, tools, and methodology of Computer Forensics

Introduction to Cyber Crime and Cyber Forensics, Basic Forensic Principles, Computer Forensics, Types of Cyber Forensics Techniques, Cyber Forensics Procedures, Detecting Incidents, Handling Evidence, Encoding and Encryption, Cyber Forensics Tools: Sysinternals Suite, FTK Forensics Tool kit, FTK Imager, OSF, Hex, Cyber check Suite, Live system forensics, Linux Forensics, An introduction to Mobile forensics.

Practical:

Experiment on the cyber forensics.

R2:  Justice Yatindra Singh: Cyber Laws, Universal Law Publishing Co., New Delhi

R3: CYBER LAW-The Indian Perspective, PawanDuggal (2009)

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

Data-driven decision making is becoming more common in organizations and businesses. In fact, database systems are at the center of the information systems strategies of most organizations. Users at any level of an organization can expect to work with and use database systems often. So, the ability to use these systems, which includes knowing what they can do and what they can't do, figuring out whether to access data directly or through technical experts, and knowing how to find and use the information well, became essential in every industry. Also, being able to design new systems and applications for them is a clear advantage and a necessity in the modern world. One type of database system that is widely used and the main focus of this course is the Relational Database Management System (RDBMS).

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables.

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL.

1. Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

Course Description:

This course aims to introduce advanced techniques for the design and analysis of algorithms that arise in a variety of applications.

Course Objectives:

1. To increase the knowledge of advanced paradigms of algorithm design.
2. To make the students learn an object-oriented way of solving problems.
3. To Enhance students’ capability of selecting the best and efficient way for encoding problems.

1. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and Compare their performances.
2. Implementation of Linear Search and Binary Search Algorithms and Compare their performances. 
3. Mini Project Details: Selection of Topic and Research about the Selected Topic.

1. Implementation of Shortest Path Algorithm (Bellman Ford).
2. Implementation of Single Source Shortest Path in a DAG using Dijkstra's Algorithm.
3. Mini Project Details: Choosing the right Dataset for the chosen Title/Topic and Exploratory Data Analysis (EDA).

1. Implement Chinese Remainder Theorem Algorithm.
2. Implement RSA Public-Key Cryptosystem.
3. Mini Project Details: Implementation (2 Phases) - Phase 1  

1. Design, develop, and write a program to solve String Matching Problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.
2. Mini Project Details: Implementation (2 Phases) - Phase 2

1. Design, develop, and write a program to implement a Monte Carlo algorithm to test the primality of a given integer and determine its performance.
2. Implement any Las Vegas Algorithm used for Randomization.
3. Mini Project Details: Performance Analysis and Report Submission.

1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd Edition, The MIT Press, 2014.
2. Kenneth A. Berman, Jerome L. Paul: “Algorithms”, Cengage Learning, 2013.

1. Horowitz, Sahni, Rajasekaran, “Computer Algorithms”, University press 2008
2. Tanenbaum A.S., Langram Y, Augestien M.J.,” Data Structures using Java”, Prentice Hall of India, 2009
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition, Pearson Education, 2012.
4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

CIA 50 %

ESE 50 %

Total Marks= 50

Course will give students a chance to use what they learn in the lectures, homework, SQL assignments, and a database implementation project.

CIA 50 %

ESE 50 %

Total Marks= 50

To introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research

 Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan

 Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis.

 Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external.

Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools

 Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables

2.      Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

3.      Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

This course helps to understand computer-based vulnerabilities and explore different footprinting, reconnaissance, and scanning methods. The course also exposes the enumeration and vulnerability analysis methods and explores options for network protection.

1. To understand the basics of computer-based vulnerabilities.

2. To understand hacking options available in Web and wireless applications.

3. To practice tools to perform ethical hacking to expose the vulnerabilities.

Ethical Hacking Overview - Role of Security and Penetration Testers - Penetration-Testing Methodologies- Laws of the Land - Overview of TCP/IP- The Application Layer - The Transport Layer - The Internet Layer - IP Addressing. - Network and Computer Attacks - Malware - Protecting Against Malware Attacks.- Intruder Attacks - Addressing Physical Security

 Experiment 1: Install Kali or Backtrack Linux / Metasploitable/ Windows XP and basic operations.

Footprinting Concepts - Footprinting through Search Engines, Web Services, Social Networking Sites, Websites, and Email - Competitive Intelligence - Footprinting through Social Engineering - Footprinting Tools - Network Scanning Concepts - Port-Scanning Tools - Scanning Techniques - Scanning Beyond IDS and Firewall.

Experiment 2: Practice the basics of reconnaissance.

Experiment 3: Using FOCA / SearchDiggity tools, extract metadata and expand the target list.

Experiment 4: Aggregates information from public databases using online free tools like Paterva’s Maltego.

Experiment 5: Scan the target using tools like Nessus.

Enumeration Concepts - NetBIOS Enumeration – SNMP, LDAP, NTP, SMTP, and DNS Enumeration - Vulnerability Assessment Concepts - Desktop and Server OS Vulnerabilities - Windows OS Vulnerabilities - Tools for Identifying Vulnerabilities in Windows- Linux OS Vulnerabilities- Vulnerabilities of Embedded Oss

Experiment 6: Automate dig for vulnerabilities and match exploits using Armitage

Access Control Lists. - Cisco Adaptive Security Appliance Firewall - Configuration and Risk Analysis Tools for Firewalls and Routers - Intrusion Detection and Prevention Systems – Network-Based and Host-Based IDSs and IPSs - Web Filtering - Security Incident Response Teams – Honeypots.

 Experiment 7: View and capture network traffic using Wireshark.

The main objectives of the course are to:

• present the layered defense approach to securing systems.

• present risk assessment as a technique to protect assets and information.

• expose students to security policy design and implementation.

• expose students to practical techniques that aim in defending computer networks from network attacks and malicious software.

• provide a comprehensive view of contemporary network defensing technologies.

• motivate the need to integrate security in the system development lifecycle to better protect the system.

Security Fundamentals, Firewalls, Types of Firewalls, Linux firewall-Iptables, Overview of NextGen Firewall, Limitations of firewall, Intrusion Detection and Prevention, Intrusion risks, Security policy, Monitoring and reporting of traffics, Traffic shaping, Investigating and verifying detected intrusions, recovering from, reporting and documenting intrusions, Define the Types of intrusion Prevention Systems, Intrusion prevention system basics, Limitations of Intrusion Prevention System, Spoofing Detection & Prevention, DDos & Dos mitigation techniques, Qos Policy, Introduction of Web Application Firewall, Packet Signature and Analysis, Virtual Private Networks, Deploy and managing VPN, VPN Performance tuning and error handling, DMZ and virtual host, Unified Threat Management, Threat Hunting Model, Introduction of Reverse proxy and policies.

Practical:

Experiment on the network security.

R2. Auditing IT Infrastructures for Compliance by Marty M. Weiss and Michael G. Solomon – Jones & Bartlett Learning

The underlying purpose of any PKI setup is to manage the keys and certificates associated with it, thereby creating a highly secure network environment for use by applications and hardware.

Understand Basic Encryption Concepts, File Encryption, Encryption Folders (Graphical/ using cipher), Cryptographic Fundamentals, Cryptographic Ciphers (Symmetric and Asymmetric), Protocols (History, Usage, Key generation, Ciphering message), Symmetric Key Encryption (DES, AES, RC5), Asymmetric Key Encryption (RSA, ECC), Diffie-Hellman Key Exchange, Attacks against encryption, Cryptographic issues, Secure Hashing Methods, SHA Secure Hash algorithm, HMAC, PKI Fundamentals, Digital Signature, Digital Certificate, CA, Trust Model, Certificate Issuance Process, Certificate Revocation (CRL, OCSP), Types and Classes of Certificate, Introduction to Aadhaar and e-Sign, Time stamping Services, Public Key Cryptography Standards, PKCS, FIPS 140-2, Strong Authentication, Single Factor and Multi-factor authentication, Single Sign-on Solutions, Open-ID and OAUTH, Graphical Passwords, Authentication Protocols, FIDO Authentication, Zero Trust Architecture, Securing Websites and Emails, SSL, TLS, PGP and S/MIME.

Practical:

Experiment on the encryption techniques.

T2: Marjie T Britz, “Computer Forensics and Cyber Crime: An Introduction”, Pearson Education, 2nd Edition, 2008.

Evidence”, Jones & Bartlett Learning; 2nd Edition, 2014.

R2: Chad Steel, “Windows Forensics”, Wiley, 1st Edition, 2006.

R3: Majid Yar, “Cybercrime and Society”, SAGE Publications Ltd, Hardcover, 2nd Edition, 2013.

R4: Robert M Slade, “Software Forensics: Collecting Evidence from the Sceneof a Digital Crime”, Tata McGraw Hill, Paperback, 1st Edition, 2004.

This course will teach about the characteristics of Big Data and how to use it in Big Data Analytics. You will learn about the features, benefits, limitations, and applications of various Big Data processing tools. You'll learn how Hadoop, Hive, Apache Spark can help you reap the benefits of Big Data while overcoming some of its challenges. At the end of completing this course students will get job opportunities in the field of data engineering.

1)      To optimize business decisions and create competitive advantage with Big Data analytics.

2)      To explore the fundamental concepts of big data analytics

3)      To learn to analyze the big data using intelligent techniques

4)      To understand and analyze the applications using Map Reduce Concepts

5) To introduce programming tools PIG & HIVE in Hadoop echo system. 

What  is  big  data, why  big  data, Data,   Evolution  of  Big  data; Characteristics  of  Big Data;  Types  of  data;  Sources  of  data;  Data  Storage  and  Analysis,  Comparison  with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing,  Types  of  analytics;  Domain  Specific  Examples  of  Big  Data;  Analytics Flow for Big Data.

Getting  Started  with  NoSQL  and  MongoDB:  Introducing  NoSQL  and  MongoDB, Installing  and  Configuring  MongoDB;   Implementing  NoSQL  in  MongoDB: Configuring User Accounts and Access Control Managing Databases and Collections from  the  MongoDB  Shell;    Finding  Documents  in  the  MongoDB  Collection  from  the MongoDB  Shell;  Additional  Data-Finding  Operations  Using  the  MongoDB  Shell; Manipulating MongoDB Documents in a Collection; Utilizing the Power of Grouping, Aggregation, and Map Reduce; Implementing MongoDB in Python Applications.

Experiment 2:  Construct  databases,  collections  and  perform  CRUD  operations  in MongoDB shell.Apply MongoDB in Python Applications

History  of  Hadoop;  HDFC  concepts;  Design  of  HDFS;  Hadoop  Distributed  File System and its Features; Components of Hadoop; HDFS Commands;  Analyzing the Data with Hadoop;   Scaling Out ; Hadoop Streaming; Reading and writing data in Hadoop; Directories; Querying the File system; Deleting data; Data flow: Anatomy of a File Read and File Write.

Anatomy of a YARN Application Run, Resource Requests, Application Lifespan, Building   YARN   Applications;  Scheduling   in   YARN;   YARN   Distributed-Shell Structure of YARN Applications.

Experiment 3:

i. Experiment with various Hadoop commands in Hadoop environment.

ii. Develop python/java programs Reading and writing data in Hadoop.

Experiment 4: Build YARN Applications.

The Configuration API; Setting Up the Development Environment; Writing a Unit Test with MRunit – mapper and reducer; Running Locally on Test Data; Running on a cluster; MapReduce workflows; Anatomy of MapReduce job run; Failures; shuffle and sort; MapReduce types – input formats – output formats.

 Experiment 5: Analyze various map-reduce java/python applications in Hadoop platform.

Hbase – data model and implementations – Hbase clients – Hbase examples – praxis.  Hive  –  data  types  and  file  formats  –  HiveQL  data  definition  – HiveQL  data  manipulation  –HiveQL  queries-case  study.  Apache Spark – Limitations of Hadoop; Overcoming the limitations of Hadoop; Theoretical Concepts in Spark; Core Components in Spark; The architecture of spark.

Experiment 6: Develop Hbase and hive programs to manage big data.

Experiment 7:  Predict big data insights by applying ML algorithms using Apache Spark.

T2.  Dasgupta, Nataraj, “Practical Big Data Analytics:  Hands-on Techniques to Implement Enterprise Analytics and Machine Learning Using Hadoop, Spark, NoSQL and R”, United Kingdom: Packt Publishing, 2018.

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

This course includes a foundation of key ideas in machine learning. This course helps to impart expertise on using Python packages related to supervised and unsupervised Machine Learning techniques.

1. To gain knowledge in the Basics of Python Machine Learning Libraries.
2. To acquire knowledge about the concepts and its applicability, benefits, and drawbacks of the Supervised and Unsupervised machine learning techniques.
3. To illustrate the working methodology on the recommendation systems.

Artificial Intelligence - Introduction to Artificial Intelligence, Machine Learning, Deep Learning, Applications of Artificial Intelligence, Learning Agent.

Forms of Learning - Supervised Learning, Unsupervised Learning, Semi supervised Learning, Reinforcement Learning

Python libraries suitable for Machine Learning - Numpy, Pandas, Data visualization using matplotlib, sklearn.

Experiment 1: Implementation of Array operations using Numpy.

Experiment 2: Implementation of Data Analysis using Pandas.

Experiment 3: Implementation of Data visualization using matplotlib and basics of sklearn.

Linear and Non Linear regression -Simple Linear regression,Multiple Linear Regression - Multivariate Linear regression -Model Evaluation Methods(Loss Function, the cost function, Residual Errors and Mean Square Error(MSE))-Applications of Regression.

Experiment 4: Implementation of Simple Linear Regression algorithm.

Experiment 5: Implementation of Multiple Linear Regression algorithm.

K-Nearest Neighbors - Decision Tree - Support Vector Machines -Logistic Regression- Classification Metrics (Confusion Matrix, Accuracy, Precision

Recall (Sensitivity), F1 Score, Area Under the Curve-Receiver Operator Characteristic (AUC-ROC))-Applications of classification

Experiment 6: Implementation of K-Nearest Neighbors algorithm.

K-means clustering - Hierarchical clustering, Agglomerative Hierarchical clustering,Types of linkage - Density-Based Clustering, DBSCAN-Applications of Clustering

Experiment 7: Implementation of K-Means clustering algorithm.

Recommendation System,Content-based filtering,Collaborative filtering,Hybrid - Real time case studies on Recommender Systems.

Experiment 8: Demonstration of a simple Recommendation System.

 1.  Continuous Internal Assessment (CIA)       :  50  marks

2. End Semester Examination(ESE) - Theory   : 50 marks

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Project Work

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

This course provides a strong foundation of fundamental concepts in Agent Based Intelligent System. To provide an empirical evidence and the scientific approach applying the concept of advanced artificial intelligence techniques for problem solving using probabilistic, fuzzy, statistical and Deep Learning Models.

Course Objective

1.     To demonstrate the concepts and features of intelligent agents, environments and uniformed search strategies.

2.      To understand uninformed search algorithms used by intelligent agents to solve problems and applications

3.     To understand uninformed search algorithms used by intelligent agents to solve problems and applications

4.     To understand the games and their environment and apply game playing algorithms

5.    To demonstrate the intelligent games playing agents and their applications.

Intelligent Agents – Agents and environments - Good behaviour – The nature of environments – structure of agents - Problem Solving agents – Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited.

Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real-world problems. Uninformed Search Strategies, Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search, Bidirectional search

Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms and Optimization Problems, Local Search in Continuous Spaces.

Games, Optimal Decisions in Games,The minimax algorithm,Optimal decisions in multiplayer games, Alpha Beta Pruning, Move ordering, Imperfect Real-Time Decisions,Cutting off search, Forward pruning

Stochastic Games, Evaluation functions for games of chance, Partially Observable Games, Kriegspiel: Partially observable chess,Card games, State-of-the-Art Game Programs, Alternative Approaches.

2.     Michael Wooldridge, “An Introduction to Multi Agent System”, Second Edition, John Wiley, 2009.

3.     Patrick Henry Winston, “Artificial Intelligence”, Third Edition, Addison-Wesley, 2002.

4.     Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, First Edition, Harcourt Asia Pvt. Ltd., 2012.

2.     Francois Chollet “Deep Learning with Python”, First Edition Manning Publication, 2018

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to:

▪        Get an inside view of an industry and organization/company

▪        Gain valuable skills and knowledge

▪        Make professional connections and enhance student's network

▪        Get experience in a field to allow the student  to make a career transition

1.The student shall undergo an Internship for 30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  7th semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Assessment of Project Work(Phase I) ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Guide ♦ Mid-semester Project Report

End Semester Review. - 100 Marks

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

2. David Salomon : Data Compression – The Complete Reference, Springer Verlag New York Inc., 4th Edition, 2006

3. Yun Q.Shi, HuifangSun : Image and Video Compression for Multimedia Engineering -  Fundamentals, Algorithms & Standards, CRC press, 2008

4.Jan Vozer : Video Compression for Multimedia, AP Profes, NewYork, 1995.

5. William Stallings, “Cryptography and Network Security”, 6th. Ed, Prentice Hall of India, New Delhi ,2013

6. William Stallings, “Network Security Essentials”, 5^thed. Prentice Hall of India, New Delhi

ESE-50

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Project Work 

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks