CHRIST (Deemed to University), BangaloreDEPARTMENT OF COMPUTER SCIENCESchool of Business and Management 

Syllabus for

1 Semester  2023  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MAI131  MATHEMATICAL FOUNDATION FOR COMPUTATIONAL INTELLIGENCE  Core Courses  4  3  100 
MAI132  INTRODUCTION TO STATISTICS FOR MACHINE LEARNING  Core Courses  4  3  100 
MAI133  FOUNDATIONS OF ARTIFICIAL INTELLIGENCE  Core Courses  3  2  50 
MAI134  RESEARCH METHODOLOGY  Core Courses  3  2  50 
MAI171  MACHINE LEARNING  Core Courses  7  4  150 
MAI172  ADVANCED DATABASE TECHNOLOGIES  Core Courses  7  4  150 
2 Semester  2023  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MAI231  KNOWLEDGE VISUALIZATION  Core Courses  3  2  50 
MAI232  DATA ENGINEERING AND KNOWLEDGE REPRESENTATION  Core Courses  3  3  100 
MAI233  DESIGN AND ANALYSIS OF ALGORITHMS  Core Courses  4  3  100 
MAI251  RESEARCH PROJECT LAB  I  Core Courses  3  1  50 
MAI271  JAVA PROGRAMMING  Core Courses  8  5  150 
MAI272  ADVANCED MACHINE LEARNING  Core Courses  7  4  150 
3 Semester  2023  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MAI331A  AI IN AGRICULTURE  Discipline Specific Elective Courses  3  2  50 
MAI331B  AI IN CYBER SECURITY  Discipline Specific Elective Courses  3  2  50 
MAI331C  AI IN COGNITIVE SCIENCES  Discipline Specific Elective Courses  3  2  50 
MAI332A  BIG DATA ANALYTICS  Discipline Specific Elective Courses  3  2  50 
MAI332B  AUGMENTED REALITY AND VIRTUAL REALITY  Discipline Specific Elective Courses  3  2  50 
MAI332C  FORENSIC SCIENCES  Discipline Specific Elective Courses  3  2  50 
MAI351  RESEARCH PROJECT LAB  II  Core Courses  3  1  50 
MAI371  DEEP LEARNING  Core Courses  7  4  150 
MAI372  NATURAL LANGUAGE PROCESSING  Core Courses  7  4  150 
MAI373  COMPUTER VISION  Core Courses  7  4  150 
 
Introduction to Program:  
Machines are gaining more intelligence to perform human like tasks. Artificial Intelligence has spanned across the world irrespective of domains. MSc (Artificial Intelligence and Machine Learning) will enable to capitalize this wide spectrum of opportunities to the candidates who aspire to master the skill sets with a research bent. The curriculum supports the students to obtain adequate knowledge in the theory of artificial intelligence with handson experience in relevant domains with tools and techniques to address the latest demands from the industry. Also, candidates gain exposure to research models and industry standard application development in specialized domains through guest lectures, seminars, industry offered electives, projects, internships, etc.  
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: Conduct investigation and develop innovative solutions for real world problems in industry and research establishments related to Artificial Intelligence and Machine LearningPO2: Apply programming principles and practices for developing automation solutions to meet future business and society needs. PO3: Ability to use or develop the right tools to develop high end intelligent systems PO4: Adopt professional and ethical practices in Artificial Intelligence application development PO5: Understand the importance and the judicious use of technology for the sustainability of the environment.  
Assesment Pattern  
CIA: 50%
ESE: 50%  
Examination And Assesments  
Continuous Internal Assessment: 50% Weightage
End Semester Examination: 50% Weightage 
MAI131  MATHEMATICAL FOUNDATION FOR COMPUTATIONAL INTELLIGENCE (2023 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This course aims to provide fundamental knowledge of mathematical foundations for computational Intelligence in Artificial Intelligence and Machine Learning. 

Course Outcome 

CO1: Understand the concepts of Linear and Matrix Algebra, Vector spaces, eigen values and eigen vectors CO2: Understand the Statistical concepts and Probability theorem for AI and ML applications CO3: Design and Develop different real time applications using different mathematical concepts. (Python, R, and other tools) 
Unit1 
Teaching Hours:9 
LINEAR EQUATIONS IN LINEAR ALGEBRA


Systems of Linear EquationsRow reduction and Echelon FormsVector EquationsMatrix EquationSolution Sets of Linear SystemsApplications of Linear SystemsLinear IndependenceIntroduction to Linear TransformationsThe Matrix of Linear TransformationLinear Models in Business, Science and Engineering  
Unit2 
Teaching Hours:9 
MATRIX ALGEBRA


Matrix OperationsThe Inverse of a MatrixCharacterizations of Invertible MatricesPartitioned MatricesMatrix FactorizationsThe Leontief InputOutput Model Application to Computer GraphicsSubspaces OF RNDimension and Rank  
Unit3 
Teaching Hours:9 
VECTOR SPACES, EIGEN VALUES, AND EIGEN VECTORS


Vector Spaces and SubspacesNull Spaces, Column Spaces and Linear TransformationsLinearly Independent Sets; Bases Coordinate SystemsThe Dimension of a Vector SpaceRankChange of BasisApplications to Difference EquationsApplication to Markov Chains.
Eigenvectors and EigenvaluesThe Characteristics EquationDiagonalizationEigenvectors and Linear TransformationsComplex EigenvaluesDiscrete Dynamical SystemsApplication of Differential EquationsIterative Estimate for Eigenvalues  
Unit4 
Teaching Hours:9 
DATA SHINE


Presentation of data using graphsComputation of central tendency and dispersionCorrelation and RegressionCase studies  
Unit5 
Teaching Hours:9 
PROBABILITY


Definition of Probability, conditional probability, Total probability theorem, Bayes theorem.
Random Variables: Continuous and discrete random variableDefinition probability mass function Probability density function  Expectation and varianceStandard discrete distributionsBernoulli, binomial, Poisson and geometricStandard continuous distributionsNormal and Exponential.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI132  INTRODUCTION TO STATISTICS FOR MACHINE LEARNING (2023 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This course is designed to teach the basic statistical concepts. This will help students to develop an understanding of random variables, probability distributions, and highdimensional random variables, as well as sampling distributions and inferential statistics. 

Course Outcome 

CO1: Understand the probability concepts applied to random data. CO2: Apply various probability distributions to both continuous and discrete data. CO3: Formulate testing of hypothesis procedures 
Unit1 
Teaching Hours:9 
INTRODUCTION TO DATA AND DESCRIPTIVE MEASURES


Data  qualitative and quantitative data: binary, categorical, continuous  measures of central tendency  measures of dispersion – skewness  
Unit2 
Teaching Hours:9 
PROBABILITY AND RANDOM VARIABLE


Random experiment  events  probability  classical definition  addition rule  random variable: discrete and continuous  expectation – bivariate and multivariate random vectors: definition – expectation and covariance matrix (only statement)  
Unit3 
Teaching Hours:9 
PROBABILITY DISTRIBUTIONS


Probability distributions for discrete data: Bernoulli  binomial  Poisson – multinomial ,Probability distributions for continuous data: Normal – logistic distribution – multivariate normal: pdf and mean vector and covariance matrix (only statement)
 
Unit4 
Teaching Hours:9 
STATISTICAL INFERENCE FOR NUMERICAL DATA


Population and sample  parameter and statistic – sampling error  sampling distributions: chisquare, t, F (only definition and statement of applications) – hypotheses: null and alternative – types of errors – level of significance – pvalue  test statistics – critical region
One sample and two sample ttest – ANOVA (only hypothesis, the test statistic and numerical illustration)  
Unit5 
Teaching Hours:9 
STATISTICAL INFERENCE FOR CATEGORICAL DATA


Inference for single proportions – Inference for two proportions  testing for the goodness of fit using chisquare – Testing for independence (twoway tables)  
Text Books And Reference Books:
1. Barr, Christopher, David M. Diez, and Cetinkaya Rundel. OpenIntro statistics. (2019).  
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI133  FOUNDATIONS OF ARTIFICIAL INTELLIGENCE (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

This course aims at developing an understanding about the fundamental concepts in defining and simulating perception, identifying the problems where AI is required. And the different AI techniques available to define and explain learning algorithms


Course Outcome 

CO1: Express the modern view of AI and its foundation CO2: Illustrate Search Strategies with algorithms and Problems. CO3: Implement Proportional logic and apply inference rules. 
Unit1 
Teaching Hours:5 
INTRODUCTION TO AI


Introduction to AI, The Foundations of AI, AI Technique TicTacToe. Problem characteristics, Production system characteristics, Production systems: 8puzzle problem.  
Unit2 
Teaching Hours:5 
INTELLIGENT AGENTS


Intelligent Agents: Agents and Environments, Good Behavior: The concept of rationality – The nature of Environments, The Structure of Agents Expert SystemsTypes of Expert Systems  
Unit3 
Teaching Hours:8 
LOCAL SEARCH ALGORITHM


Searching: Uninformed search strategies – Breadth first search, depth first search. Generate and Test, Hill climbing, simulated annealing search, Greedy best first search, A* search, AO* search  
Unit4 
Teaching Hours:7 
KNOWLEDGE REPRESENTATION


Propositional logic  syntax & semantics  First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts, Clausal form conversion, Forward chaining, Backward chaining, Resolution
 
Unit5 
Teaching Hours:5 
ETHICS AND SOCIAL IMPLICATIONS OF AI


Ethical Considerations on AI – bias – privacy – philosophical challenge in human judgement – faulty algorithms  Social Implications of AI – Case studies Planning and Acting in the Real World  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI134  RESEARCH METHODOLOGY (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. It continues with finding out the literature using computer technology, basic statistics required for research and ends with linear regression. 

Course Outcome 

CO1: Understand the essence of research and the necessity of defining a research problem. CO2: Apply research methods and methodologies including research design, data collection, data analysis, and interpretation CO3: Create scientific reports according to specified standards 
Unit1 
Teaching Hours:6 
RESEARCH METHODOLOGY


Defining research problem  selecting the problem  necessity of defining the problem  techniques involved in defining a problem Ethics in Research.  
Unit2 
Teaching Hours:6 
RESEARCH DESIGN


Principles of experimental design Working with Literature: Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review. Online Searching: Database – SCIFinder – Scopus  Science Direct  Searching research articles  Citation Index  Impact Factor  Hindex etc  
Unit3 
Teaching Hours:6 
RESEARCH DATA


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:6 
SCIENTIFIC WRITING


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report.  
Unit5 
Teaching Hours:6 
REPORT WRITING


Latex: IntroductionTextTables Figures Equations Citations Referencing and Templates (IEEE style).  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4thed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010. a  
Evaluation Pattern ESE50% CIA50%  
MAI171  MACHINE LEARNING (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

This course is designed to introduce the principles and design of machine learning techniques. This course aims to provide foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems. 

Course Outcome 

CO1: Understand the basic principles of machine learning models CO2: Evaluate and prepare data for machine learning models. CO3: Formulate machine learning problems and their solutions CO4: Evaluate different models used for classification 
Unit1 
Teaching Hours:15 
INTRODUCTION TO DATA PREPROCESSING


Getting to Know your data: Data Objects and Attribute Types, Measuring Data Similarity and Dissimilarity – Data Preprocessing: An Overview – Data Cleaning – Data Integration – Data Reduction – Data Transformation – Data Discretization. INTRODUCTION TO MACHINE LEARNING: Origins of Machine Learning – Basic learning process – Machine Learning in Practice – Types of Machine Learning Algorithms Lab Exercises: 1. Data Exploration for identifying different datasets 2. Preprocessing the dataset using normalization techniques  
Unit2 
Teaching Hours:15 
RULE BASED MACHINE LEARNING


Mining Frequent Patterns, Associations and Correlations  Basic Concepts  Frequent Itemset Mining Methods – Pattern Evaluation Methods Lab Exercises: 1. Identify frequent itemsets using Apriori Algorithm 2. Generate FP Tree for a transaction dataset  
Unit3 
Teaching Hours:15 
ADVANCED PATTERN MINING:


Pattern Mining – Pattern Mining in Multilevel, Multidimensional space – Constraintbased Frequent Pattern Mining – Mining HighDimensional Data and Colossal Patterns – Mining Compressed or Approximate Patterns – Pattern Exploration and Application Lab Exercises: 1. Explore generating multilevel association rules 2. Explore multidimensional associations  
Unit4 
Teaching Hours:15 
SUPERVISED LEARNING I:


Classification – Basic Concepts – Decision Tree Induction – Bayes Classification Methods – RuleBased Classification – Model Evaluation and Selection – Techniques to improve Classification Accuracy. Lab Exercises: 1. Demonstrate Naïve Bayes classifier 2. Construct Decision Tree for a dataset and identify the order of attributes  
Unit5 
Teaching Hours:15 
SUPERVISED LEARNING II:


Bayesian Belief Networks – Support Vector Machines – Classification using Frequent Patterns – Lazy Learners – Self Study: Additional topics regarding classification Lab Exercises: 1. Explore SVM Classifier 2. Demonstrate Lazy Learner  
Text Books And Reference Books: [1] Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall, Morgan and Kaufmann Publisher, Third Edition, 2014 [2] Introduction to Machine Learning, E. Alpaydin, 3rd Edition, MIT Press, 2014. [3] Machine Learning with R: Expert techniques for predictive modeling, Brett Lantz, 3rd Edition, Packt Publishing, 2019  
Essential Reading / Recommended Reading [1] Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kaufmann Publisher, Third Edition, 2012 [2] Data Mining Techniques, Arun K Pujari, Second Edition, Universities Press India Pvt. Ltd. 2010 Note: Python libraries like MLxtend and Scikit Learn can be used for lab exercises  
Evaluation Pattern ESE50% CIA50%  
MAI172  ADVANCED DATABASE TECHNOLOGIES (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

To provide a strong foundation for database application design and development by introducing the fundamentals and advanced concepts of database technologies. 

Course Outcome 

CO1: Understand the basic concepts of database management systems, structured query language, transactions, and related database facilities CO2: Analyze the database requirements and develop the logical design of the database CO3: Design NoSQL database applications using storing, accessing, and querying CO4: Develop new applications in databases based on knowledge of existing techniques. 
Unit1 
Teaching Hours:15 
DATABASE SYSTEM CONCEPTS AND CONCEPTUAL MODELING


Data models, schemas and instances, DBMS architecture and data independence, Database languages and interfaces, database system environment, and Classification of DBMS. Using HighLevel Conceptual Data Models for Database Design  Entity Types, Entity Sets, Attributes, and Keys  Relationship Types, Relationship Sets, Roles, and Structural Constraints, Enhanced Entity Relationship Model  SQL Data Definition and Data Types, Specifying Constraints in SQL, Basic Retrieval Queries in SQL, Additional features of SQL. Complex Queries, Triggers, Views, and Schema Modification More Complex SQL Retrieval Queries, Specifying Constraints as Assertions and Actions as Triggers, Views (Virtual Tables) in SQL, Schema Change Statements in SQL. Lab Exercises: 1. DDL, DML, and TCL commands 2. Use of integrity constraints and referential integrity.  
Unit2 
Teaching Hours:15 
RELATIONAL DATA MODEL, DATABASE DESIGN, AND INTRODUCTION TO FILE ORGANIZATION


Design Guidelines for Relation Schemas  Functional Dependencies  Normal Forms Based on Primary Keys  Second and Third Normal Forms  BoyceCodd Normal Form – Multivalued Dependency and Fourth Normal Form  Join Dependencies and Fifth Normal Form – Inference Rules, Equivalence and Minimal Cover  Properties of Relational Decompositions  Nulls and Dangling Tuples  File Organization  Organization of Records in Files  Ordered Indices  B+ Tree Index Files  Static Hashing  Bitmap Indices. Lab Exercises: 3. Data Retrieval using JOINS 4. Subqueries and Correlated queries  
Unit3 
Teaching Hours:15 
TRANSACTION PROCESSING, CONCURRENCY CONTROL, AND RECOVERY


Transaction  Introduction to transaction processing transaction and system concept Desirable properties of the transaction Transaction support in SQL concurrency control techniques – Twophase Locking techniques for concurrency Concurrency Control Based on Timestamp Ordering. Recovery Concepts NOUNDO/REDO Recovery Based on Deferred Update Recovery Techniques Based on Immediate Update Shadow Paging. Lab Exercises: 5. Views in SQL 6. Stored Procedures and Triggers  
Unit4 
Teaching Hours:15 
DISTRIBUTED DATABASES AND NOSQL SYSTEMS


Distributed databases: Distributed Database concepts Types  Data Fragmentation Replication Allocation Techniques. Overview of Transaction Management  Overview of Concurrency Control and Recovery.NOSQL DatabasesIntroduction to NOSQL Systems, The CAP Theorem, DocumentBased NOSQL Systems and MongoDB, NOSQL KeyValue Stores, ColumnBased or Wide Column NOSQL Systems, NOSQL Graph Databases. Lab Exercises: 7. NOSQL CRUD operations 8. .NOSQL Aggregate functions  
Unit5 
Teaching Hours:15 
NoSQL STORES AND INDEXING AND ORDERING DATA SETS


Accessing Data from ColumnOriented Databases Like HBaseQuerying Redis Data stores Querying in Neo4JChanging Document DatabasesSchema Evolution in ColumnOriented DatabasesHBase Data Import and ExportData Evolution in Key/Value StoresMapReduce Basic MapReduceMapReduce Calculations2 stage example. Indexing and Ordering Data SetsEssential Concepts Behind A Database IndexIndexing and Ordering in MongoDBCreating and Using Indexes in MongoDBIndexing and Ordering in CouchDBIndexing in Apache CassandraIndexing and Ordering in Neo4J. Lab Exercises: 9. NoSQL data IMPORT and EXPORT 10. MAPREDUCE in NoSQL  
Text Books And Reference Books: [1] Elmasri & Navathe, Fundamentals of Database Systems, AddisonWesley, 7th Edition, 2021. [2] Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley, 2021, ISBN: 9780470942246  
Essential Reading / Recommended Reading [1] Korth F. Henry and Silberschatz Abraham, Database System Concepts, McGraw Hill, 6th Edition, 2010. [2] O’neil Patric, O’neil Elizabeth, Database Principles, Programming and Performance, Argon Kaufmann Publishers, 2nd Edition, 2002. [3] Ramakrishnan and Gehrke, Database Management System, McGrawHill, 3rd Edition, 2003.
[4] Gaurav Vaish, Getting Started with NoSQL, Packt Publishing, 2013.  
Evaluation Pattern ESE50% CIA50%  
MAI231  KNOWLEDGE VISUALIZATION (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

Data visualization techniques allow people to use their perception to better understand the data. The goal of this course is to introduce students to data visualization which includes principles and techniques. Students will learn the value of visualization, specific techniques in information visualization and scientific visualization. 

Course Outcome 

CO1: Understand the usage of various visualization structures like tables,tree,network etc CO2: Evaluate information visualization systems and other forms of visual presentation for their effectiveness CO3: Design and build data visualization system 
Unit1 
Teaching Hours:6 
Value of Visualization


Value of Visualization – What is Visualization and Why do it: External representation – Interactivity – Difficulty in Validation. Data Abstraction: Dataset types – Attribute types – Semantics. Task Abstraction – Analyze, Produce, Search, Query.  
Unit2 
Teaching Hours:6 
Four levels of validation


Four levels of validation – Validation approaches – Validation examples. Marks and Channels. Rules of thumb – Arrange tables: Categorical regions – Spatial axis orientation – Spatial layout density. Arrange spatial data:  
Unit3 
Teaching Hours:6 
Geometry


Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels.  
Unit4 
Teaching Hours:6 
Manipulate view


Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views  
Unit5 
Teaching Hours:6 
Partition into views


Partition into views – Static and Dynamic layers – Reduce items and attributes: Filter – Aggregate. Focus and context: Elide – Superimpose – Distort – Case studies.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI232  DATA ENGINEERING AND KNOWLEDGE REPRESENTATION (2023 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

To provide a foundational knowledge of data engineering and knowledge representation. To store , retrieve, analyze and design data for various applications To represent different sorts of knowledge, such as uncertain or incomplete knowledge 

Course Outcome 

CO1: Store and retrieve data effectively CO2: Analyze the data from different sources CO3: Analyze and design knowledgebased systems 
Unit1 
Teaching Hours:9 
DATA ENGINEERING and DATA MODELS


Data Engineering Introduction to Data Engineering  Data Engineering versus Data Science – Data Engineering tools– Data Engineering Lifecycle Data Models Data Systems – Reliability – Scalability – Maintainability Data Models and Query Languages.  Relational Model Versus Document Model  Query Languages for Data Query Languages for Data,Declarative Queries on the Web ,MapReduce Querying ,GraphLike Data Models Property Graphs ,The Cypher Query Language ,Graph Queries in SQL ,TripleStores and SPARQL  
Unit2 
Teaching Hours:9 
BUILDING DATA PIPELINES


Introduction – Data Engineering ecosystem  Building data pipelines—Extract, Transform, Load ETL Process – Data Structures related to Database – Other data integration methods – Benefits and Challenges of ETL – ETL tools .Data Warehousing  Stars and Snowflakes: Schemas for Analytics ColumnOriented Storage  Column Compression Sort Order in Column Storage  Writing to ColumnOriented Storage .  
Unit3 
Teaching Hours:9 
DATA STORAGE AND RETRIEVAL


Data Storage and Retrieval Non Relational data Non Relational data – NoSQL LanguageSpecific Formats JSON, XML, and Binary Variants  Modes of Dataflow Dataflow Through Databases DATA in Distributed systems.Data in distributed systems – Partitioning and Replication  Partitioning of KeyValue Data  Partitioning and Secondary Trouble with Distributed Systems Faults and Partial Failures  Unreliable Networks  Unreliable Clocks  
Unit4 
Teaching Hours:9 
Knowledge Representation


Knowledge Representation  Ontological Engineering  Categories and Objects . Events  Mental Events and Mental Objects  Reasoning Systems for Categories  Reasoning with Default Information Uncertain knowledge and reasoning Quantifying Uncertainty  Acting under Uncertainty  Basic Probability Notation .  
Unit5 
Teaching Hours:9 
Knowledge Representation in an uncertain domain


Probabilistic ReasoningRepresenting Knowledge in an Uncertain Domain The Semantics of Bayesian Networks Efficient Representation of Conditional Distributions Exact Inference in Bayesian Networks Relational and FirstOrder Probability Models  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI233  DESIGN AND ANALYSIS OF ALGORITHMS (2023 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This core course covers principles of algorithm design, elementary analysis of algorithms, and fundamental data structures. The emphasis is on choosing appropriate data structures and designing correct and efficient algorithms to operate on these data structures.


Course Outcome 

CO1: Analyze the complexity of polynomial algorithms CO2: Apply various design strategies for solving problems CO3: Distinguish NP hard and NP complete problems from other problems 
Unit1 
Teaching Hours:9 
Algorithms as technology


Algorithms as technology – Analyzing and Designing algorithms – Asymptotic notations – Recurrences – Methods to solve recurrences – Heap Sort  Quick Sort – Sorting in linear time – Radix sort – Selection in linear time. Introduction: Algorithms, Pseudo code for expressing algorithms, performance analysis Space complexity, Time Complexity, Asymptotic notation Big oh notation, omega notation and theta notation  
Unit2 
Teaching Hours:9 
Divide and conquer methodology


Divide and conquer methodology – Multiplication of large integers – Strassen's matrix multiplication – Greedy method – Prim's algorithm – Kruskal's algorithm – algorithm for Huffman codes, Knapsack problem, Spanning trees, Minimum cost spanning trees, Single source shortest path problem.  
Unit3 
Teaching Hours:9 
Dynamic Programming


Dynamic Programming: General method, applications Matrix chained multiplication,Optimal binary search trees, 0/1 Knapsack problem, All pairs shortest path problem, Traveling sales person problem, Reliability design.  
Unit4 
Teaching Hours:9 
Backtracking


Backtrcking: General method, Applications nqueue problem, Sum of subsets problem, Graph coloring, Hamiltonian cycles.  
Unit5 
Teaching Hours:9 
Branch and Bound


Branch and Bound: General method, applications Travelling sales person problem, 0/1 Knapsack problem LC branch and Bound solution, FIFO branch and Bound solution. NPHard and NPComplete Problems: Basic concepts, Non deterministic algorithms, NPHard and NP Complete classes  
Text Books And Reference Books: [1]Fundamentals of Computer Algorithms, Ellis Horowitz, SartajSahni and Rajasekharan,Universities press [2] Design and Analysis of Algorithms, P. h. Dave,2nd Edition, Pearson Education  
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI251  RESEARCH PROJECT LAB  I (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

This course is intended to carry out supervised research in a particular domain. The students are expected to identify, formulate and analyze the research problem. The students are also expected to conduct critical review of literature, choosing the study design, deciding on the sample design, become proficient in tools to solve the research problem. Students are expected to adhere research ethical practices at every phase of development and submit the intermediate reports.


Course Outcome 

CO1: Identify and formulate the research problem in the chosen domain. CO2: Analyze the research gaps and propose the novel solutions to the chosen problem 
Unit1 
Teaching Hours:30 
RESEARCH PROJECT


The students are expected to carry out the following: · Identify the background of research and conduct critical review of literature to understand the context. · Identification of research gaps · Formulate research questions/Objectives and hypothesis based on the research problem. · Methodology or approach intended to be adopted in the execution of the research · Expected outcome of research  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern CIA ESE 50% 5O%  
MAI271  JAVA PROGRAMMING (2023 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:8 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

This course will help the learner to gain sound knowledge in objectoriented principles, GUI application development, web application development and enterprise application development by using different features of Java technologies. 

Course Outcome 

CO1: Understanding and applying the principles of objectoriented programming in the construction of robust, maintainable programs CO2: Analyze the various societal and environmental problems critically to apply the concepts of generic, lambda and collections CO3: Develop sustainable and innovative GUI/Web based/Enterprise solutions for realtime problems. 
Unit1 
Teaching Hours:18 
INTRODUCTION TO OBJECT ORIENTED PROGRAMMING (OOP) AND CLASSES


Introduction to Object Oriented Programming (OOP) ObjectOriented Programming (OOP) Principles Class Fundamentals  Declaring Objects  Introducing Methods  Overloading methods – Constructors  Parameterized Constructors  this Keyword. Class Features Garbage Collection  the finalize () Method  Introducing Access Control  Understanding static  Introducing nested and inner classes  String class  String Buffer Class  Command Line Arguments Lab Exercises: 1. Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification. 2. Implement the concept of class, data members, member functions and access specifiers, function overloading and constructor overloading 3. Implement the features of static keyword, command line argument, String class and String Buffer class  
Unit2 
Teaching Hours:18 
INHERITANCE, INTERFACES & PACKAGES AND MULTITHREADING IN JAVA


Inheritance in Java Inheritance Basics  Multilevel Hierarchy Using super  Method overriding  Dynamic Method Dispatch Abstract keyword Using final with inheritance  The Object Class. Interfaces and Packages Inheritance in java with Interfaces – Defining Interfaces  Implementing Interfaces  Extending Interfaces Creating Packages  CLASSPATH variable  Access protection  Importing Packages  Interfaces in a Package. Multithreading Java Thread Model  Life cycle of a Thread  Java Thread Priorities  Runnable interface and Thread Class Thread Synchronization – Inter Thread Communication. Lab Exercises: 4. Implement the concept of inheritance, super, abstract and final keywords. 5. Implement the concept of package and interface. 6. Implement the concept of multithreading.  
Unit3 
Teaching Hours:18 
GENERICS, LAMBDA AND THE COLLECTIONS FRAMEWORK


Generics Generics Concept  General Form of a Generic Class – Bounded Types – Generic Class Hierarchy  Generic Interfaces – Restrictions in Generics. Lambda Expression Introduction to Lambda expression Block Lambda Expressions  Generic Functional Interfaces  Passing lambda expressions as arguments  Lambda expressions and exceptions Lambda expressions and variable capture. The Collections Framework The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface  ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement. Working with maps – The map interfaces, the map classes. Comparators the collection algorithms Lab Exercises: 7. Implement the concept of Generics 8. Implement the concept of the lambda expression 9. Implement the concept of a collection framework  
Unit4 
Teaching Hours:18 
JAVA BEANS AND JDBC


JDBC Introduction to JDBC Connecting to the database Basic JDBC Operations – Essential JDBC Classes – JDBC Drivers – JDBCODBC Bridge – Connecting to a database with driver manager – JDBC database URL. JAVA BEANS Java beans  Advantages of Beans – Introspection Bound and Constrained Properties – Persistence – Customizers  The JavaBeans API. JAVA SWING Swing Basics – Components and Containers – JLabel and ImageIcons JTextField – Swing Buttons – JTabbedPane – JScrollPane – JList – JComboBox – JTable – Swing Menus. Lab Exercises: 10. Implement the concept of JDBC and Java Beans 11. Implement the features of java swing package  
Unit5 
Teaching Hours:18 
JAVA SERVLETS & JSP


JAVA SERVLETS Servlets Basics – Life Cycle of a Servlet –A Simple Servlet  The Servlet API – Servlet Interfaces – Generic Servlet Class HttpServletRequest Interface – HttpServeltResponse JSP The JSP development model – component of jsp page – Page directive – Action – scriptlet – JSP expression, JSP Syntax and semantics, JSP in XML. Lab Exercises: 12. Implement the concept of java servlets 13. Implement the concept of JSP  
Text Books And Reference Books: [1] Schildt Herbert, Java : The Complete Reference, Tata McGraw Hill, 11 th Edition,2019 [2] The complete reference JSP 2.0, Tata McGraw Hill, 2nd Edition, Phil Hanna
 
Essential Reading / Recommended Reading [1]Cay S Horstmann, Core Java Volume 1 Fundamentals, Prentice Hall, 11th Edition, 2018.  
Evaluation Pattern ESE50% CIA50%  
MAI272  ADVANCED MACHINE LEARNING (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

This course covers the most popular machine learning algorithms such as regression techniques with a modern outlook focusing on the recent advances and examples. It also aims to provide the foundations for dimensionality reduction techniques and clustering techniques with their applications to solve real world problems. 

Course Outcome 

CO1: Demonstrate classification and clustering techniques CO2: Evaluate different models used for feature selection CO3: Understand the strengths and weaknesses of many popular machine learning techniques. CO4: Design and implement various machine learning algorithms in a rnge of realworld applications 
Unit1 
Teaching Hours:15 
REGRESSION METHODS


Understanding Regression: Simple Linear regression  Ordinary least squares estimation  Gradient Descent  multiple linear regression  Multivariate linear regression – Polynomial regression – Regularization – Ridge and Lasso Regression  Understanding regression trees and model trees  Logistic regression  Bias and Variance Tradeoff – Overfitting and underfitting models. SelfStudy: Support Vector Regression, Decision Tree Regression – Random Forest Regression Lab Exercises: 1. Implement various types of linear regression techniques 2. Explore nonlinear regression techniques  
Unit2 
Teaching Hours:15 
DIMENSIONALITY REDUCTION


Factor Analysis, Low Variance Filter, High Correlation Filter, Backward Feature Elimination – Forward Feature Selection – Principal Component Analysis – Factor Analysis – Multidimensional Scaling  Linear Discriminant Analysis – Independent Component Analysis – Isomap – Maximum Relevance Minimum Redundancy SelfStudy:  Combining Multiple Learners Lab Exercises: 1. Demonstrate Feature selection 2. Explore and compare PCA, LDA and ICA techniques  
Unit3 
Teaching Hours:15 
UNSUPERVISED LEARNING


Cluster Analysis  Partitioning Methods – KMeans – KMedoids – Hierarchical Methods – Agglomerative Vs Divisive – Distance measures in algorithmic methods – BIRCH – Chameleon – Probabilistic Hierarchical Clustering – Evaluation of Clustering: Assessing clustering Tendency – Determining the Number of Clusters – Measuring Clustering Quality Lab Exercises: 1. Demonstrate KMeans algorithm with optimum number of clusters 2. Demonstrate Hierarchical clustering 3. Evaluate quality of clusters  
Unit4 
Teaching Hours:15 
REINFORCEMENT LEARNING


Introduction – Single State Case: KArmed Bandit – Elements of Reinforcement Learning – ModelBased Learning – Temporal Difference Learning – Generalization – Partially Observable States Selfstudy and Discussion: Case Studies and recent applications. Lab Exercises: 1. Explore model based reinforcement learning  
Unit5 
Teaching Hours:15 
NEURAL NETWORKS


Application scope of Neural Networks – Fundamental Concept of ANN: The Artificial Neural Network – Biological Neural Network – Comparison between Biological neuron and Artificial Neuron – Evolution of Neural Network. Basic models of ANN – Learning Methods – Activation Functions – Importance Terminologies of ANN – Single / Multilayer perceptron Lab Exercises: 1. Calculate the output of a simple neuron using binary and bipolar sigmoidal activation functions 2. Demonstrate classification using MLP  
Text Books And Reference Books: [1] Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, Bradford Books, 2018 [3] Machine Learning with R: Expert techniques for predictive modeling, Brett Lantz, 3 Edition, Packt Publishing, 2019  
Essential Reading / Recommended Reading [1] Introduction to Machine Learning, E. Alpaydin, 3rd Edition, MIT Press, 2014. [2] Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kauf [3] S.N.Sivanandam, S. N. Deepa, Principles of Soft Computing, WileyIndia, 3rd Edition,2018. Note: Scikit learn python library can be used for lab exercises.  
Evaluation Pattern CIA50% ESE50%  
MAI331A  AI IN AGRICULTURE (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To explore the current and potential applications of AI and various technologies in agriculture, such as crop monitoring, yield prediction, soil analysis, Plant Disease Identification and pest control to improve the agriculture productivity in India 

Course Outcome 

CO1: To Understand the basic concepts and techniques of artificial intelligence and how they can be applied in the field of agriculture. CO2: To develop skills in the design and implementation of AI & IoTbased agriculture systems. 
Unit1 
Teaching Hours:6 
SMART FARMING USING ARTIFICIAL INTELLIGENCE


Introduction – Role of AI in advanced farming  Role of IoT in advanced farming  Role of Robotics in advanced farming – Smart Farming – Smart Agriculture – AI in Agriculture  How Data Analytics Transforming Agriculture – Agricultures Data Analytics Benefits – Challenges of AI in Agriculture  Case Study  
Unit2 
Teaching Hours:6 
Precision Agriculture


History of Precision Agriculture – Introduction – Components – Tools and Techniques – Site Specific Crop Management – VRA & VRT – Adoption of Smart Precision Agriculture  Modern Day Agriculture – Smart Precision Agriculture – Agriculture Digital Farming – Benefits – Soil Management – High Accuracy in Disease prediction, Detection and Control – Application of WSN in Precision Agriculture  
Unit3 
Teaching Hours:6 
AI AND DATA ANALYTICS IN AGRICULTURE


Prediction of Crop Yield and Pest Disease Infestation – Prediction system for Cropyield and livestock – Climate Condition Monitoring and Automated Systems – Decision Making system for Crop Selection based on Soil.  
Unit4 
Teaching Hours:6 
AGRICULTURE DATA MINING AND INFORMATION EXTRACTION


Introduction – Data Mining Techniques in Farming – Case Studies in Agricultural Data Mining – Research Challenges – Machine Learning and its Application in Food Processing and Preservation.  
Unit5 
Teaching Hours:6 
MODERN AGRICULTURAL APPLICATIONS USING AI


Introduction – Smart farming Tools – Technological Advancements – Climate – Smart Agriculture – Evolution of Cutting Edge Technologies that are revolutionizing the Agriculture in India – Smart Farming Applications  Future Scope and Challenges.  
Text Books And Reference Books: [1]Smart farming technologies for sustainable Agricultural Development, Digital Computer Fundamentals, Floyd, Thomas L, Pearson International, 11th Edition, 2015
[2]Smart farming technologies for sustainable Agricultureal Development, Poonia, Ramesh C., Gao, XiaoZhi, Raja, Linesh,IGI Global, 2018  
Essential Reading / Recommended Reading [1]Artificial Intelligence and Smart Agriculture Technology, Utku Gose, V B Surya Prasath, Hossain, Subrato Bharati, Prajoy Podder, CRC Press,1st Edition 2022 [2]AI, Edge and IoTbased Smart Agriculture, Ajith Abraham, Sujata Dash, Joel J.P.C. Rodrigues, Academic Press, 2021,Agriculture 5.0,Latief Ahmad, Firasath Nabi,CRC Press 2021  
Evaluation Pattern ESE50% CIA50%  
MAI331B  AI IN CYBER SECURITY (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To select suitable ethical principles and commit to professional responsibilities and human values and contribute value and wealth for the benefit of the society. 

Course Outcome 

CO1: Identify existing legal framework and laws on cyber security CO2: Apply the security aspects of social media platforms and ethical aspects associated with use of social media 
Unit1 
Teaching Hours:6 
INTRODUCTION TO AI FOR CYBER SECURITY


Applying AI in cyber securityThe evolution from expert systems to data mining and AIThe different forms of automated learningThe characteristics of algorithm training and OptimizationBeginning with AI via Jupyter NotebooksIntroducing AI in the context of cyber security.  
Unit2 
Teaching Hours:6 
AI FOR CYBER SECURITY ARSENAL:


ClassificationRegressionDimensionality ReductionClusteringVideo anomaly detection Natural Language processing (NLP) for Social media analysisLargescale image Processing.  
Unit3 
Teaching Hours:6 
DETECTING CYBER SECURITY THREATS WITH AI:


How to detect spam with Perceptron’s Image spam detection with support vector machines (SVMs)Phishing detection with logistic regression and decision TreesSpam detection with Naive BayesSpam detection adopting NLP  
Unit4 
Teaching Hours:6 
PROTECTING SENSITIVE INFORMATION AND ASSETS:


Authentication abuse PreventionAccount Reputation ScoringUser authentication with keystroke RecognitionBiometric authentication with facial recognition.  
Unit5 
Teaching Hours:6 
FRAUD PREVENTION WITH AI SOLUTIONS:


How to leverage machine learning (ML) algorithms for fraud DetectionHow bagging and boosting techniques can improve an algorithm's EffectivenessHow to analyze data with Jupyter NotebookHow to resort to statistical metrics for results evaluation.  
Text Books And Reference Books: [1] Cyber Security Understanding Cyber Crimes, Computer Forensics and Legal Perspectives by Sumit Belapure and Nina Godbole, Wiley India Pvt. Ltd.
[2] Data Privacy Principles and Practice by Natraj Venkataramanan and Ashwin Shriram, CRC Press  
Essential Reading / Recommended Reading
 
Evaluation Pattern ESE50% CIA50%  
MAI331C  AI IN COGNITIVE SCIENCES (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

It is designed to be a challenging course, involving significant independent work, readings, assignments, and projects. It covers structured knowledge representations, as well as knowledgebased methods of problem solving, planning, decisionmaking, and learning in cognitive system with help of AI models. 

Course Outcome 

CO1: Understand the basics and fundamental concepts, methods, and prominent issues in knowledgebased artificial intelligence CO2: Understand the specific skills and abilities needed to apply those concepts to the design of knowledgebased AI agents CO3: Find the relationship between knowledgebased artificial intelligence and the study of human cognition 
Unit1 
Teaching Hours:6 
INTRODUCTION AND FUNDAMENTALS


Introduction to KnowledgeBased AI – Where KB AI fits into AI as a whole  Cognitive systems: what are they?  AI and cognition: how are they connected Fundamentals Semantic Networks  Generate & Test  MeansEnds Analysis  Problem Reduction  Production Systems  
Unit2 
Teaching Hours:6 
COMMON SENSE REASONING AND PLANNING


Frames  Understanding  Common Sense Reasoning  Scripts  Logic  Planning  
Unit3 
Teaching Hours:6 
LEARNING AND ANALOGICAL REASONING


Learning by Recording Cases  Incremental Concept Learning  Classification  Version Spaces & Discrimination Trees CaseBased Reasoning  ExplanationBased Learning  Analogical Reasoning  
Unit4 
Teaching Hours:6 
VISUOSPATIAL REASONING AND DESING & CREATIVITY


Constraint Propagation  Visuospatial Reasoning Configuration  Diagnosis  Design  Creativity  
Unit5 
Teaching Hours:6 
METACOGNITION


Learning by Correcting Mistakes  MetaReasoning  AI Ethics  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
Artificial Intelligence: A Modern Approach. Stuart J., and Peter Norvig. 2nd ed. Upper Saddle River, N.J.: Prentice Hall/Pearson Education, 2003. ISBN: 0137903952  
Evaluation Pattern CIA50% ESE50%  
MAI332A  BIG DATA ANALYTICS (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The student can understand the Big Data Platform and its Use cases and get an overview of Apache Hadoop. The course will provide HDFS Concepts and Interfacing with HDFS and the student can understand Map Reduce Jobs. It provides knowledge in NOSQL Data Base, Apache Hadoop architecture, ecosystem, and explores related applications including HDFS, Spark, and MapReduce with Hive and Pig. 

Course Outcome 

CO1: Explore the fundamental concepts of big data analytics CO2: Provide an overview of Apache Hadoop with NOSQL and REDIS Data Store CO3: Understand Map Reduce Jobs/spark framework for processing Big Data for Analytics 
Unit1 
Teaching Hours:6 
INTRODUCTION TO BIG DATA ANALYTICS


Big Data Overview: Data Structures  Analyst Perspective on Data Repositories  State of the Practice in Analytics  Current Analytical Architecture  Drivers of Big Data  Emerging Big Data Ecosystem and a New Approach to Analytics  Key Roles for the New Big Data Ecosystem  Examples of Big Data Analytics.  
Unit2 
Teaching Hours:8 
NOSQL BIG DATA MANAGEMENT


NoSQL Definition and introduction  Document databases – MongoDB  Storing data and accessing data from MongoDB  Querying MongoDB  Document store internals  MongoDB reliability and durability  Horizontal scaling  CRUD operations in MongoDB  Creating and using indexes in MongoDB. Understanding Key/Value Stores in Memcached and RedisEventually Working with ColumnOriented DatabasesHBase Distributed Storage Architecture.  
Unit3 
Teaching Hours:5 
UNDERSTANDING MAPREDUCE


Introduction to Hadoop and MapReduce Programming Hadoop Overview, HDFS (Hadoop Distributed File System), Processing– Data with Hadoop, Managing Resources and Applications with Hadoop YARN (Yet Another Resource Negotiator). Introduction to MAPREDUCE Programming: Introduction, Mapper, Reducer, Combiner, Partitioner, Searching, Sorting, Compression  
Unit4 
Teaching Hours:5 
HIVE


Introduction to Hive  Hive Architecture  Characteristics  Comparison with RDBMS (Traditional Database) – HIVE modes – HIVE Server2(HS2)  Hive Data Types and File Formats  Hive Data Model  Hive Integration and Workflow Steps Hive Builtin Functions  HiveQL  HiveQL. Data Definition Language (DDL)  HiveQL. Data Manipulation Language (DML)  HiveQL for Querying the Data  Aggregation  Join  Group by Clause.  
Unit5 
Teaching Hours:6 
SPARK


Introduction  Spark and Big Data Analytics Spark  Introduction to Big Data ToolSpark  Introduction to Data Analysis with Spark  Spark SQL  Using Python Advanced Features with Spark.  
Text Books And Reference Books: [1] Raj Kamal, Preeti Saxena, Big Data Analytics, Introduction to Hadoop, Spark, and MachineLearning, McGrawHill India, 2019. [2] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015. [3] Gaurav Vaish, Getting Started with NoSQL, Packt Publishing,2013.
[4] High performance spark by Holden Karau and Rachel Warren published by O’Reilly Media 2017.  
Essential Reading / Recommended Reading [1] Pethuru Raj, Anupama Raman, Dhivya Nagaraj and Siddhartha Duggirala, HighPerformance BigData Analytics: Computing Systems and Approaches, Springer, 2015. [2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop RealWorld Solutions Cookbook, Packt Publishing, 2013. [3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.
[4] John Sharp, Data Access for HighlyScalable Solutions: Using SQL, NoSQL, and Polyglot Persistence,Microsoft,2013  
Evaluation Pattern CIA50% ESE50%  
MAI332B  AUGMENTED REALITY AND VIRTUAL REALITY (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

This course will introduce students to the concepts and applications of Augmented Reality and Virtual Reality technologies. Students will learn the fundamentals of AR/VR, the differences between the two, and how to create AR/VR applications. The course will cover a variety of topics such as AR/VR hardware, software, 3D modeling, user interfaces, and interaction design. 

Course Outcome 

CO1: Understand the fundamentals of AR/VR CO2: Understand the process to create AR/VR Applications CO3: Develop user interfaces for AR/VR 
Unit1 
Teaching Hours:6 
INTRODUCTION TO AR/VR


Overview of AR/VR, Brief history of AR/VR, Differences between AR and VR, Advantages and Disadvantages of AR/VR, Applications of AR/VR AR/VR Hardware and Software AR/VR devices, software and sensors, VR headsets and controllers, AR/VR software platforms, AR/VR development tools.  
Unit2 
Teaching Hours:6 
AR/VR DESIGN PRINCIPLES


Interaction design principles for AR/VR, User interface design for AR/VR, Human factors in AR/VR design AR/VR Content Creation Introduction to 3D modeling, Creating 3D assets for AR/VR, creating models for AR/VR, adding interactivity to AR/VR experiences, Texturing and lighting for AR/VR. Using Unity3D or Unreal Engine for AR/VR development.  
Unit3 
Teaching Hours:6 
AR/VR DEVELOPMENT


Designing intuitive interfaces for AR/VR, User interface considerations for different AR/Vr devices, Developing AR/VR applications, AR/VR programming languages, AR/VR frameworks and libraries, Best practices for AR/VR UI. AR/VR Deployment Testing and debugging AR/VR applications, Deploying AR/VR applications to devices, AR/VR best practices  
Unit4 
Teaching Hours:6 
AR/VR PROJECT DEVELOPMENT


AR/VR emerging technologies, potential future applications, challenges and opportunities.  
Unit5 
Teaching Hours:6 
Unit5


Students will work on their AR/VR projects  AR/VR Project Presentation  Students will present their AR/VR projects to the class  Industry Visit/ Experiential Learning  
Text Books And Reference Books: [1] Augmented Reality: Principles and Practice Authors: Dieter Schmalstieg and Tobias Hollerer Publisher: AddisonWesley Professional Year: 2016 [2] Learning Virtual Reality: Developing Immersive Experiences and Applications for Desktop, Web, and Mobile Author: Tony Parisi Publisher: O'Reilly Media Year: 2019 [3] Virtual Reality: Concepts and Technologies" by Philippe Fuchs and Guillaume Moreau (2019) [4] "Augmented Reality: Principles and Practice" by Dieter Schmalstieg and Tobias Hollerer (2016)  
Essential Reading / Recommended Reading [1] Unity AR/VR Development by Harrison Ferrone [2] VR Development with Unity: Create Immersive VR Experiences with Unity 3D by Jonathan Linowes [3] ARKit 101: Creating Augmented Reality Apps by Erin P. Friar [4] "Creating Augmented and Virtual Realities: Theory and Practice for NextGeneration Spatial Computing" by Erin Pangilinan, Shaowen Bardzell, and Jeffrey Bardzell (2019) [5] "Unity Virtual Reality Projects" by Jonathan Linowes (2018)  
Evaluation Pattern ESE50% CIA50%  
MAI332C  FORENSIC SCIENCES (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

To provide extensive knowledge about computer forensic and recognize diverse aspects of forensics science. It is also used to acquire the knowledge to examine and analyze evidence from image, video, email, data, document, mobile and network. 

Course Outcome 

Unit1 
Teaching Hours:6 
Contemporary Computer Crime:


WebBased Criminal Activity: Interference with Lawful Use of Computer. Malware: Viruses and WormsDoS and DDoS Attacks Botnets and Zombie Armies Spam Ransomware and the Kidnapping of Information. Theft of Information, Data Manipulation, and Web Encroachment: Traditional Methods of Proprietary Information TheftTrade Secrets and Copyrights Political Espionage. Terrorism: Cyberterrorism Threatening and Harassing CommunicationsCyberstalking and Cyber harassment Cyberbullying.  
Unit2 
Teaching Hours:6 
Image and video forensics:


Image Forensics: Importance of image forensic detection, Active Methods: Digital watermarking, digital signatures. Passive methods: Image source identification, image tamper detection.
Video Forensics: Active approaches, Blind approaches: copymove, splicing, Frame insertion, frame deletion, frame duplication, frame replacing, frame shuffling  
Unit3 
Teaching Hours:6 
EMail and Web Forensics


Opening Pandora’s Box of EMailFollowing the route of email packets Becoming Exhibit A Scoping Out EMail Architecture: Email structures Email addressing Email lingo Email in motion Seeing the EMail Forensics Perspective: Dissecting the message Expanding headers Checking for email extras Extracting email from clients Getting to know email file extensions Copying the email Printing the email Investigating WebBased Mail Searching Browser Files Looking through Instant Messages  
Unit4 
Teaching Hours:6 
Data and Document Forensics


Delving into Data Storage Finding Digital Cavities Where Data Hides Extracting Data Rebuilding Extracted Data Document Forensics: Finding Evidential Material in Documents: Metadata Honing In on CAM (Create, Access, Modify) Facts Discovering Documents.  
Unit5 
Teaching Hours:6 
Mobile & Network Forensics


Mobile Forensics: Keeping Up with Data on the Move Making a Device Seizure CuttingEdge Cellular Extractions Network Forensics: Mobilizing Network Forensic Power Identifying Network Components Saving Network Data. Wiretap ActCommunications Assistance for Law Enforcement ActForeign Intelligence Surveillance ActComprehensive Crime Control ActElectronic Communications Privacy Act and the Privacy Protection Act.
 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA 50% ESE 50%  
MAI351  RESEARCH PROJECT LAB  II (2023 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:3 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

The students are expected to reveal the core competency aimed by this course which includes development of effective solution to the chosen problem, deployment of solution and research findings. The students are expected to submit the final report as well as they are expected to defend their research work 

Course Outcome 

CO1: Analyze proposed solutions to the identified research problem. CO2: Develop a solution to the problem and analyze results 
Unit1 
Teaching Hours:30 
RESEARCH PROJECT


The students are expected to carry out the following:
 
Text Books And Reference Books: 1. Relevant research articles for the research problem  
Essential Reading / Recommended Reading   
Evaluation Pattern CIA ESE 50% 50%  
MAI371  DEEP LEARNING (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

The main objective of this course is to make students comfortable with the tools and techniques required to handle large datasets. Several libraries and datasets publicly available will be used to illustrate the application of these algorithms. This will help students develop the skills required to gain experience of doing independent research and study. 

Course Outcome 

CO1: Recognize the basic concepts and techniques of deep learning CO2: Evaluate and prepare to apply deep learning algorithms CO3: Apply deep learning models for applications. CO4: Identify appropriate tools to implement the solutions to problems related for deep learning 
Unit1 
Teaching Hours:15 
DEEP FEEDFORWARD NETWORKS


An overview of ANN, Back Propagation Neural Networks, Deep Feedforward Networks: Deep network for Universal Boolean function representation, classification and Approximation, perceptron Learning, Perceptron with activation functions Lab Exercises: 1. Demonstrate MLP in Keras/Tensorflow 2. Demonstrate Deep Feedforward Network  
Unit2 
Teaching Hours:15 
REGULARIZATION FOR DEEP MODELS


Regularization for Deep models: L2 and L1 Regularization, Constrained Optimization and Under Constrained, Early Stopping, Parameter Tying and Parameter Sharing, Sparse representations, Dropout Lab Exercises: 1. Demonstrate Regularization L1 for Deep learning model 2. Demonstrate Regularization L2 for Deep learning model  
Unit3 
Teaching Hours:15 
CONVOLUTIONAL NEURAL NETWORK


The Convolution Operation, Pooling, Structured Outputs, Variants of convolution, Variants of CNN – ImageNet, Alexnet, VGG16, ResNet, Applications in Computer Vision Lab Exercises: 1. Demonstrate Convolution Neural Network 2. Demonstrate VGG16 or ResNet  
Unit4 
Teaching Hours:15 
RECURRENT NEURAL NETWORKS


Sequence Processing, Unfolding Computational Graphs, Training recurrent networks The Long ShortTerm Memory (LSTM), Optimization for Long Term Dependencies, EncoderDecoder SequencetoSequence processing Lab Exercises: 1. Demonstrate Recurrent Neural Network 2. Demonstrate ShortTerm Long Memory (LSTM)  
Unit5 
Teaching Hours:15 
AUTOENCODERS


The architecture of autoencoders  relationship between the Encoder, Bottleneck, and Decoder, how to train autoencoders? Types of autoencoders: Undercomplete autoencoders, Sparse autoencoders, Contractive autoencoders, Denoising autoencoders, Variational Autoencoders Lab Exercises: 1. Demonstrate Sparse Autoencoders 2. Demonstrate Contractive Autoencoders  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA50% ESE50%  
MAI372  NATURAL LANGUAGE PROCESSING (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

Students who complete this course will gain a foundational understanding in natural language processing methods and strategies. They will also learn how to evaluate the strengths and weaknesses of various NLP technologies and frameworks as they gain practical experience in the NLP toolkits available. Students will also learn how to employ literaryhistorical NLPbased analytic techniques like stylometry, topic modeling, synsetting and named entity recognition in their personal research. 

Course Outcome 

CO1: Understand various approaches on syntax and semantics in NLP CO2: Apply various methods to discourse, generation, dialogue and summarization using NLP CO3: Analyze various methodologies used in Machine Translation, machine learning techniques used in NLP including unsupervised models 
Unit1 
Teaching Hours:15 
INTRODUCTION


Introduction to NLP Background and overview NLP Applications NLP hard Ambiguity Algorithms and models, Knowledge Bottlenecks in NLP Introduction to NLTK, Case study. Lab Exercises: 1. Write a program to tokenize text. 2. Write a program to count word frequency and to remove stop words.  
Unit2 
Teaching Hours:15 
PARSING AND SYNTAX


Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax Spelling, Error Detection and correctionWords and Word classes Partof Speech Tagging, Naive Bayes and Sentiment Classification: Case study. Lab Exercises: 3. Write a program to program to tokenize NonEnglish Languages 4. Write a program to get synonyms from WordNet  
Unit3 
Teaching Hours:15 
SMOOTHED ESTIMATION AND LANGUAGE MODELLING


Ngram Language Models: NGrams, Evaluating Language Models The language modelling problem Semantic Analysis and Discourse Processing Semantic Analysis: Meaning RepresentationLexical Semantics AmbiguityWord Sense Disambiguation. Discourse Processing: cohesionReference Resolution Discourse Coherence and Structure. Lab Exercises: 5. Write a program to get Antonyms from WordNet 6. Write a program for stemming nonEnglish words  
Unit4 
Teaching Hours:15 
NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION


Natural Language Generation: Architecture of NLG Systems, Applications. Machine Translation: Problems in Machine TranslationMachine Translation Approaches. Evaluation of Machine Translation systems. Case study: Characteristics of Indian Languages. Lab Exercises: 7. Write a program for lemmatizing words Using WordNet 8. Write a program to differentiate stemming and lemmatizing words  
Unit5 
Teaching Hours:15 
INFORMATION RETRIEVAL AND LEXICAL RESOURCES


Information Retrieval: Design features of Information Retrieval SystemsClassical, Non classical, Alternative Models of Information Retrieval – valuation Lexical Resources: Word Embeddings  Word2vecGlove. Unsupervised Methods in Nlp Graphical Models for Sequence Labelling in NLP. Lab Exercises: 9. Write a program for POS Tagging. 10. Write a program to implement Word Embeddings. 11. Case studybased program (IBM) or Sentiment analysis or ChatGpt  
Text Books And Reference Books: [1] Roland R. Hausser, Foundations of Computational Linguistics: Human computer Communication in Natural Language, Springer, 2014. [2] Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O’Reilly Media, First edition, 2009.  
Essential Reading / Recommended Reading [1] Speech and Language Processing, Daniel Jurafsky and James H., 3rd Edition, Martin Prentice Hall, 2023. [2] Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press,1999.  
Evaluation Pattern CIA 50% ESE 50%  
MAI373  COMPUTER VISION (2023 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:7 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

The Objective of this course is to cover the basic theory and algorithms that are widely used in computer vision. Develop handson experience in using computers to process images for image enhancement, restoration, filtering and feature extraction to recognize objects. 

Course Outcome 

CO1: Describe the theoretical background of image processing CO2: Design various image enhancement methods and filtering techniques CO3: Apply restoration, compression and segmentation methods in both frequency and spatial domain CO4: Perform feature extraction and classification using real time dataset 
Unit1 
Teaching Hours:15 
INTRODUCTION TO DIGITAL IMAGE PROCESSING


Fundamentals: Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Image formation model, Grayscale and Color images representation, Introduction to Digital Video. Lab Exercises: 1. Program to perform Resize, Rotation of binary, Grayscale and color images using various methods. 2. Demonstrate frame extraction from the video and display the color components of the images.  
Unit2 
Teaching Hours:15 
IMAGE ENHANCEMENT


Spatial Domain Gray Level Transformations, point operations, Histogram Processing, Histogram equalization, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters. Frequency Domain Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters, DCT, Homomorphic Filtering Lab Exercises: 3. Program to implement various image enhancement techniques using Builtin and user defined functions. 4. Program to implement Linear Spatial Filtering using Builtin and user defined functions 5. Program to implement Low and High Pass Filtering of images in frequency domain.  
Unit3 
Teaching Hours:15 
IMAGE RESTORATION AND IMAGE COMPRESSION


A model of the Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering. Image Compression models: Huffman coding, Run length coding, LZW coding, JPEG. Lab Exercises: 6. Program to implement NonLinear Spatial Filtering using Builtin and user defined functions 7. Demonstrate denoising of the images  
Unit4 
Teaching Hours:15 
IMAGE SEGMENTATION AND REPRESENTATION


Point, Line and Edge detection, Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes, Polygonal approximations using minimum perimeter polygons. Lab Exercises: 8. Demonstrate Edge detection using various methods. 9. Perform frame extraction from the video and display the color components of the images.  
Unit5 
Teaching Hours:15 
DESCRIPTION AND OBJECT RECOGNITION


Boundary descriptors – Fourier descriptors, regional descriptors –Topological descriptors. Introduction to Patterns and Pattern Classes: Minimum distance classifier, KNN classifier. Object detection and recognition – Face recognition (Eigen faces). Lab Exercises: 10. Program to demonstrate Fourier descriptors 11. Extracting feature descriptors from the image dataset. 12. Implement image classification using extracted relevant features.
 
Text Books And Reference Books: [1] Digital Image Processing: An algorithmic approach, M. A. Joshi, PHI, 2nd Edition 2009. [2] Digital Image Processing and analysis, B.Chanda, D. DuttaMajumdar, PHI, 1st Edition, 2011.  
Essential Reading / Recommended Reading [1] Digital Image Processing, R. C. Gonzalez & R. E. Woods, Pearson Education, 4th Edition, 2018. [2] Fundamental of Digital Image Processing, A.K. Jain, PHI, 4th Edition, 2011. [3] Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, PHI, 2nd Edition, 2017.
[4] Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2nd Edition, 2022.  
Evaluation Pattern CIA 50% ESE 50% 