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Syllabus
AI
Artificial Intelligence (Syllabus)
310253: Artificial Intelligence
Unit I Introduction 07 Hours
Introduction to Artificial Intelligence, Foundations of Artificial Intelligence, History of Artificial
Intelligence, State of the Art, Risks and Benefits of AI, Intelligent Agents, Agents and
Environments, Good Behavior: Concept of Rationality, Nature of Environments, Structure of
Agents.
Unit II Problem-solving 07 Hours
Solving Problems by Searching, Problem-Solving Agents, Example Problems, Search Algorithms,
Uninformed Search Strategies, Informed (Heuristic) Search Strategies, Heuristic Functions, Search
in Complex Environments, Local Search and Optimization Problems.
Unit III Adversarial Search and Games 07 Hours
Game Theory, Optimal Decisions in Games, Heuristic Alpha–Beta Tree Search, Monte Carlo Tree
Search, Stochastic Games, Partially Observable Games, Limitations of Game Search Algorithms,
Constraint Satisfaction Problems (CSP), Constraint Propagation: Inference in CSPs, Backtracking
Search for CSPs.
Unit IV Knowledge 07 Hours
Logical Agents, Knowledge-Based Agents, The Wumpus World, Logic, Propositional Logic: A
Very Simple Logic, Propositional Theorem Proving, Effective Propositional Model Checking,
Agents Based on Propositional Logic, First-Order Logic, Representation Revisited, Syntax and
Semantics of First-Order Logic, Using First-Order Logic, Knowledge Engineering in First-Order
Logic.
Unit V Reasoning 07 Hours
Inference in First-Order Logic, Propositional vs. First-Order Inference, Unification and First-Order
Inference, Forward Chaining, Backward Chaining, Resolution, Knowledge Representation,
Ontological Engineering, Categories and Objects, Events, Mental Objects and Modal Logic,
Reasoning Systems for Categories, Reasoning with Default Information
Unit VI Planning 07 Hours
Automated Planning, Classical Planning, Algorithms for Classical Planning, Heuristics for
Planning, Hierarchical Planning, Planning and Acting in Nondeterministic Domains, Time,
Schedules, and Resources, Analysis of Planning Approaches, Limits of AI, Ethics of AI, Future of
AI, AI Components, AI Architectures.
Text Books:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third
edition, Pearson, 2003, ISBN :10: 0136042597
2. Deepak Khemani, “A First Course in Artificial Intelligence”, McGraw Hill Education(India),
2013, ISBN : 978-1-25-902998-1
3. Elaine Rich, Kevin Knight and Nair, “Artificial Intelligence”, TMH, ISBN-978-0-07-
008770-5
Reference Books:
1. Nilsson Nils J , “Artificial Intelligence: A new Synthesis”, Morgan Kaufmann Publishers
Inc. San Francisco, CA, ISBN: 978-1-55-860467-4
2. Patrick Henry Winston, “Artificial Intelligence”, Addison-Wesley Publishing Company,
ISBN: 0-201-53377-4
3. Andries P. Engelbrecht-Computational Intelligence: An Introduction, 2nd Edition-Wiley
India- ISBN: 978-0-470-51250-0
4. Dr. Lavika Goel, “Artificial Intelligence: Concepts and Applications”, Wiley publication,
ISBN: 9788126519934
5. Dr. Nilakshi Jain, “Artificial Intelligence, As per AICTE: Making a System Intelligent”,
Wiley publication, ISBN: 9788126579945
WT
Web Technology (Syllabus)
310252: Web Technology
Unit I Web Essentials and Mark-up language- HTML 07 Hours
The Internet, basic internet protocols, the World Wide Web, HTTP Request message, HTTP
response message, web clients, web servers.HTML: Introduction, history and versions.HTML
elements: headings, paragraphs, line break, colors and fonts, links, frames, lists, tables, images
and forms, Difference between HTML and HTML5. CSS: Introduction to Style Sheet, CSS
features, CSS core syntax, Style sheets and HTML, Style rule cascading and inheritance, text
properties. Bootstrap.
Unit II Client Side Technologies: JavaScript and DOM 07 Hours
JavaScript: Introduction to JavaScript, JavaScript in perspective, basic syntax, variables and data
types, statements, operators, literals, functions, objects, arrays, built in objects, JavaScript
debuggers. DOM: Introduction to Document Object Model, DOM history and levels, intrinsic
event handling, modifying element style, the document tree, DOM event handling, jQuery,
Overview of Angular JS.
Unit III Java Servlets and XML 07 Hours
Servlet: Servlet architecture overview, A “Hello World” servlet, Servlets generating dynamic
content, Servlet life cycle, parameter data, sessions, cookies, URL rewriting, other Servlet
capabilities, data storage, Servlets concurrency, databases (MySQL) and Java Servlets. XML:
XML documents and vocabularies, XML declaration, XML Namespaces, DOM based XML
processing, transforming XML documents, DTD: Schema, elements, attributes. AJAX:
Introduction, Working of AJAX.
Unit IV JSP and Web Services 07 Hours
JSP: Introduction to Java Server Pages, JSP and Servlets, running JSP applications, Basic JSP,
JavaBeans classes and JSP, Support for the Model-View-Controller paradigm, JSP related
technologies. Web Services: Web Service concepts, Writing a Java Web Service, Writing a Java
web service client, Describing Web Services: WSDL, Communicating Object data: SOAP.
Struts: Overview, architecture, configuration, actions, interceptors, result types, validations,
localization, exception handling, annotations.
Unit V Server Side Scripting Languages 07 Hours
PHP: Introduction to PHP, uses of PHP, general syntactic characteristics, Primitives, operations
and expressions, output, control statements, arrays, functions, pattern matching, form handling,
files, cookies, session tracking, using MySQL with PHP, WAP and WML. Introduction to
ASP.NET: Overview of the .NET Framework, Overview of C#, Introduction to ASP.NET,
ASP.NET Controls, Web Services. Overview of Node JS.
Unit VI Ruby and Rails 07 Hours
Introduction to Ruby: Origins & uses of Ruby, scalar types and their operations, simple input
and output, control statements, fundamentals of arrays, hashes, methods, classes, code blocks and
iterators, pattern matching. Introduction to Rails: Overview of Rails, Document Requests,
Processing Forms, Rails Applications and Databases, Layouts, Rails with Ajax. Introduction to
EJB.
Learning Resources
Text Books:
1. Jeffrey C.Jackson, "Web Technologies: A Computer Science Perspective", Second
Edition, Pearson Education, 2007, ISBN 978-0131856035
2. Robert W. Sebesta,“ Programming the World Wide Web”, 4th Edition, Pearson education,
2008
Reference Books :
1. Marty Hall, Larry Brown, “Core Web Programming", Second Edition, Pearson Education,
2001, ISBN 978-0130897930.
2. H.M. Deitel, P.J. Deitel and A.B. Goldberg, "Internet & World Wide Web How To
Program", Third Edition, Pearson Education, 2006, ISBN 978-0131752429.
3. Chris Bates, “Web Programming Building Internet Applications”, 3rd Edition, Wiley
India, 2006.
4. Xue Bai et al, “The web Warrior Guide to Web Programming”, Thomson, 2003.
DS & BD
Data Science and Big Data Analytics (Syllabus)
Unit I Introduction to Data Science and Big Data
Basics and need of Data Science and Big Data, Applications of Data Science, Data explosion, 5 V’s of Big Data, Relationship between Data Science and Information Science, Business intelligence versus Data Science, Data Science Life Cycle, Data: Data Types, Data Collection. Need of Data wrangling, Methods: Data Cleaning, Data Integration, Data Reduction, Data Transformation, Data Discretization.
#Exemplar/Case Studies: Create academic performance dataset of students and perform data pre- processing using techniques of data cleaning and data transformation.
Unit II Statistical Inference
Need of statistics in Data Science and Big Data Analytics, Measures of Central Tendency: Mean, Median, Mode, Mid-range. Measures of Dispersion: Range, Variance, Mean Deviation, Standard Deviation. Bayes theorem, Basics and need of hypothesis and hypothesis testing, Pearson Correlation, Sample Hypothesis testing, Chi-Square Tests, t-test.
#Exemplar/Case Studies: For an employee dataset, create measure of central tendency and its measure of dispersion for statistical analysis of given data.
Unit III Big Data Analytics Life Cycle
Introduction to Big Data, sources of Big Data, Data Analytic Lifecycle: Introduction, Phase 1: Discovery, Phase 2: Data Preparation, Phase 3: Model Planning, Phase 4: Model Building, Phase 5: Communication results, Phase 6: Operation alize.
#Exemplar/Case Studies Case study: Global Innovation Social Network and Analysis (GINA).
Unit IV Predictive Big Data Analytics with Python
Introduction, Essential Python Libraries, Basic examples. Data Preprocessing: Removing Duplicates, Transformation of Data using function or mapping, replacing values, Handling Missing Data. Analytics Types: Predictive, Descriptive and Prescriptive. Association Rules: Apriori Algorithm, FP growth. Regression: Linear Regression, Logistic Regression. Classification: Naïve Bayes, Decision Trees. Introduction to Scikit-learn, Installations, Dataset, mat plotlib, filling missing values, Regression and Classification using Scikit-learn.
#Exemplar/Case Studies Use IRIS dataset from Scikit and apply data preprocessing methods
Unit V Big Data Analytics and Model Evaluation
Clustering Algorithms: K-Means, Hierarchical Clustering, Time-series analysis. Introduction to Text Analysis: Text-preprocessing, Bag of words, TF-IDF and topics. Need and Introduction to social network analysis, Introduction to business analysis. Model Evaluation and Selection: Metrics for Evaluating Classifier Performance, Holdout Method and Random Sub sampling, Parameter Tuning and Optimization, Result Interpretation, Clustering and Time-series analysis using Scikit- learn, sklearn. metrics, Confusion matrix, AUC-ROC Curves, Elbow plot.
#Exemplar/Case Studies Use IRIS dataset from Scikit and apply K-means clustering methods
Unit VI Data Visualization and Hadoop
Introduction to Data Visualization, Challenges to Big data visualization, Types of data visualization, Data Visualization Techniques, Visualizing Big Data, Tools used in Data Visualization, Hadoop ecosystem, Map Reduce, Pig, Hive, Analytical techniques used in Big data visualization. Data Visualization using Python: Line plot, Scatter plot, Histogram, Density plot, Box- plot.
#Exemplar/Case Studies Use IRIS dataset from Scikit and plot 2D views of the dataset