WEEK 1 : b) Program to calculate the entropy and the information gain

 WEEK 1 : a) Write a program using scikit-learn to implement K-means Clustering

 WEEK 1 :  c) Program to implement perceptron

SVM

ARTIFICIAL INTELLIGENCE SYLLABUS

 ARTIFICIAL INTELLIGENCE

UNIT I

Introduction to Artificial Intelligence:

Introduction, History, Intelligent Systems, Foundations of AI, Applications, Tic- tac toe game playing, development of AI languages, Current trends in AI

Problem Solving:

State-Space Search and Control Strategies: Introduction, General Problem Solving, Characteristics of Problem, Exhaustive searches, heuristic search techniques, iterative deepening a*, Constraint Satisfaction

UNIT II

Problem Reduction and Game Playing: Introduction, problem reduction, game playing, alpha-beta pruning, two-player perfect information games Logic Concepts: Introduction, propositional calculus, proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in proportional logic, predicate logic

UNIT III

Knowledge Representation: Introduction, Approaches to Knowledge representation, Knowledge representation using Semantic Network, Extended Semantic Network for KR, Knowledge representation using Frames 

Advance Knowledge Representation Techniques: Introduction, Conceptual Dependency Theory, Script Structure, CYC Theory, Case Grammars, Semantic Web

UNIT IV

Expert Systems:

Expert Systems, Phases in building expert systems, Expert System Architecture, Rule Based Systems, Forward Chaining, Blackboard Systems, Blackboard architecture, Blackboard System vs Rulebased system, Truth maintenance system.

UNIT V

Uncertainty measure & Fuzzy sets and fuzzy logic:

Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty factor theory, dempster-shafer theory Fuzzy. 

sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.

Text Books:

1. Artificial Intelligence by Elaine Rich, Kevin Knight and Shivashankar B Nair, Tata McGraw Hill.

2. Introduction to Artificial Intelligence and Expert Systems by Dan W. Patterson, Pearson Education.

Reference Books:

1. Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig, Prentice Hall

Software Engineering Syllabus

 Software Engineering

UNIT-I: Software and Software Engineering: The Nature of Software, The Unique Nature of WebApps, Software Engineering, Software Process, Software Engineering Practice, Software Myths. Process Models: A Generic Process Model, Process Assessment and Improvement, Prescriptive Process Models, Specialized Process Models, The Unified Process, Personal and Team Process Models, Process Terminology, Product and Process.

UNIT-II: Requirements Analysis And Specification: Requirements Gathering and Analysis, Software Requirement Specification (SRS), Formal System Specification. Software Design: Overview of the Design Process, How to Characterise of a Design?, Cohesion and Coupling, Layered Arrangement of Modules, Approaches to Software Design

UNIT – III: Function-Oriented Software Design: Overview of SA/SD Methodology, Structured Analysis, Developing the DFD Model of a System, Structured Design, Detailed Design, Design Review, over view of Object Oriented design. User Interface Design: Characteristics of Good User Interface, Basic Concepts, Types of User Interfaces, Fundamentals of Component-based GUI Development, A User Interface Design Methodology.

UNIT – IV: Coding And Testing: Coding, Code Review, Software Documentation, Testing, Unit Testing, Black-Box Testing, White-Box Testing, Debugging, Program Analysis Tool, Integration Testing, Testing Object-Oriented Programs, System Testing, Some General Issues Associated with Testing

UNIT – V: Software Reliability And Quality Management: Software Reliability, Statistical Testing, Software Quality, Software Quality Management System, ISO 9000, SEI Capability Maturity Model. Computer Aided Software Engineering: Case and its Scope, Case Environment, Case Support in Software Life Cycle, Other Characteristics of Case Tools, Towards Second Generation CASE Tool, Architecture of a Case Environment.

UNIT – VI Software Maintenance: Software maintenance, Maintenance Process Models, Maintenance Cost, Software Configuration Management. Software Reuse: what can be reused? Why almost No Reuse So Far? Basic Issues in Reuse Approach, Reuse at Organization Level.

TEXT BOOKS:

1.     Software engineering A practitioner’s Approach, Roger S. Pressman, Seventh Edition McGrawHill International Edition.

2.     Fundamentals of Software Engineering, Rajib Mall, Third Edition, PHI.

3.     Software Engineering, Ian Sommerville, Ninth edition, Pearson education

REFERENCE BOOKS:

1.     Software Engineering : A Primer, Waman S Jawadekar, Tata McGraw-Hill, 2008

2.     Software Engineering, A Precise Approach, Pankaj Jalote, Wiley India, 2010.

3.     Software Engineering, Principles and Practices, Deepak Jain, Oxford University Press.

4.     Software Engineering1: Abstraction and modeling, Diner Bjorner, Springer International edition, 2006

Data Wrangling Syllabus

 DATA WRANGLING

UNIT-1

Introduction to Data Wrangling: What Is Data Wrangling?- Importance of Data Wrangling -How is Data Wrangling performed?- Tasks of Data Wrangling-Data Wrangling Tools-Introduction to Python-Python Basics-Data Meant to Be Read by Machines-CSV Data-JSON Data-XML Data.

UNIT-2

Working with Excel Files and PDFs: Installing Python Packages-Parsing Excel Files-Parsing Excel Files -Getting Started with Parsing-PDFs and Problem Solving in Python-Programmatic Approaches to PDF Parsing-Converting PDF to Text-Parsing PDFs Using pdf miner-Acquiring and Storing Data-Databases, A Brief Introduction-Relational Databases, MySQL and PostgreSQL-Non-Relational Databases, NoSQL-When to Use a Simple File-Alternative Data Storage.

UNIT-3

Data Cleanup: Why Clean Data? - Data Cleanup Basics-Identifying Values for Data Cleanup-Formatting Data-Finding Outliers and Bad Data-Finding Duplicates-Fuzzy Matching-RegEx Matching-Normalizing and Standardizing the Data-Saving the Data-Determining suitable Data Cleanup-Scripting the Cleanup- Testing with New Data.

UNIT-4

Data Exploration and Analysis: Exploring Data-Importing Data-Exploring Table Functions-Joining Numerous Datasets-Identifying Correlations-Identifying Outliers-Creating Groupings-Analyzing Data-Separating and Focusing the Data-Presenting Data-Visualizing the Data-Charts-Time-Related Data-Maps- Interactives -Words-Images, Video, and Illustrations-Presentation Tools-Publishing the Data-Open Source Platforms.

UNIT-5

Web Scraping: What to Scrape and How-Analyzing a Web Page-Network/Timeline-Interacting with JavaScript-In-Depth Analysis of a Page-Getting Pages-Reading a Web Page-Reading a Web Page with LXML-XPath-Advanced Web Scraping-Browser-Based Parsing-Screen Reading with Selenium-Screen Reading with Ghost.Py, Spidering the Web-Building a Spider with Scrapy-Crawling Whole Websites with Scrapy.

TEXT BOOKS:

1. Jacqueline Kazil& Katharine Jarmul, “Data Wrangling with Python”, O’Reilly Media, Inc,2016

2. McKinney, William, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2 nd Edition, O’Reilly, 2017