RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA,
BHOPAL
New Scheme Based On AICTE Flexible Curricula
CSE-Artificial Intelligence and Machine Learning/ Artificial Intelligence and Machine
Learning, VII-Semester
AL701 Computer Vision
Course Objectives: Students should be able to
Understand practice and theory of computer vision.Elaborate computer vision algorithms, methods and
concepts
Implement computer vision systems with emphasis on applications and problem solving
Apply skills for automatic analysis of digital images to construct representations of physical objects and
scenes.
Design and implement real-life problems using Image processing and computer vision.
UnitI:Introduction to computer vision, Introduction to images, Image Processing VS Computer Vision,
Problems in Computer Vision, Basic image operations, Mathematical operations on images: Datatype
Conversion, Contrast Enhancement, Brightness Enhancement, Bitwise operations: Different Bitwise
Operations
Unit II: Binary Image Processing, thresholding, Erosion / Dilation, Overview on Opening and Closing,
Connected Component Analysis, Contour Analysis
Unit III: Image Enhancement and Filtering, Color Spaces, Color Transforms, Histogram Equalization,
Advanced Histogram Equalization(CLAHE),Color Adjustment using Curves, Image Filtering: Introduction
to Image Filtering, What is Convolution, Image Smoothing:-Box Blur, Gaussian Blur, Median Blur
Unit IV: Introduction to Image Gradients: - First Order Derivative Filters,Second Order Derivative Filters,
Edge Detection,Image Segmentation and Recognition,Image Classification,Object detection
UnitV: Applications of Computer Vision: Gesture Recognition, Motion Estimation and Object Tracking,
face detection, Deep Learning with OpenCV
Books and references
1. Forsyth & Ponce, “Computer Vision-A Modern Approach”, Pearson Education.
2. M.K. Bhuyan , “ Computer Vision and Image Processing: Fundamentals and Applications”, CRC Press,
USA, ISBN 9780815370840 - CAT# K338147.
3. Richard Szeliski, “Computer Vision- Algorithms & Applications”, Springer.
4. R.C Gonzalez & Richard E Wood, “Digital Image Processing” ,Addison WesleyPublishin
5. Bharti Motwani, “Machine Learning for Text and Image Data Analysis” , Publishers Wiley,2023
Online Lectures links
https://onlinecourses.nptel.ac.in/noc23_ee39/preview
https://onlinecourses.nptel.ac.in/noc19_cs58/preview
https://onlinecourses.nptel.ac.in/noc23_ee78/preview
PRACTICAL: Different problems to be framed to enable students to understand the concept learnt and get
hands-on on various tools and software related to the subject. Such assignments are to be framed for ten to
twelve lab sessions
RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA,
BHOPAL
New Scheme Based On AICTE Flexible Curricula
CSE-Artificial Intelligence and Machine Learning/ Artificial Intelligence and Machine
Learning, VII-Semester
AL 702(D) Machine Learning for Data Science
Course Objective: The students will be able to derive practical solutions using predictive analytics. They
will also understand the importance of various algorithms in Data Science.
Detailed Contents:
Unit I: Introduction
Algorithms and Machine Learning, Introduction to algorithms, Tools to analyze algorithms, Algorithmic
techniques: Divide and Conquer, examples, Randomization, Applications
Unit II: Algorithms
Graphs, maps, Map searching, Application of algorithms: stable marriages example, Dictionaries and
hashing, search trees, Dynamic programming
Unit III: Application to Personal Genomics
Linear Programming, NP completeness, Introduction to personal Genomics, Massive Raw data in
Genomics, Data science on Personal Genomes, Interconnectedness on Personal Genomes, Case studies
Unit IV: Machine Learning
Introduction, Classification, Linear Classification, Ensemble Classifiers, Model Selection, Cross Validation,
Holdout
Unit V: Machine Learning Applications
Probabilistic modelling, Topic modelling, Probabilistic Inference, Application: prediction of preterm birth,
Data description and preparation, Relationship between machine learning and statistics
Text Books/Suggested References:
1. Introduction to Machine Learning, Jeeva Jose, Khanna Book Publishing House.
2. Machine Learning, Rajiv Chopra, Khanna Book Publishing House.
3. Data Science and Machine Learning: Mathematical and Statistical Methods Machine Learning & Pattern
Recognition, by Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, Chapman & Hall/Crc,
2019.
4. Hands-On Data Science and Python Machine Learning, Frank Kane, Packt Publishers, 2017.
5. https://www.edx.org/course/machine-learning-for-data-science-and-analytics
6. Dr. Nageswara Rao,”Machine Learning in Data Science Using Python”,Publisher by Dreamtech, 2022
Course Outcomes: After completion of course, students would be able to:
1. Apply practical solutions using predictive analytics.
2. Understand the importance of various algorithms in Data Science.
3. Create competitive advantage from both structured and unstructured data.
4. Predict outcomes with supervised machine learning techniques.
5. Unearth patterns in customer behavior with unsupervised techniques
RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA,
BHOPAL
New Scheme Based On AICTE Flexible Curricula
CSE-Artificial Intelligence and Machine Learning/ Artificial Intelligence and Machine
Learning, VII-Semester
AL 703(D) Internet and Web Technology
UNIT I: Introduction: Concept of WWW, Internet and WWW, HTTP Protocol: Request andResponse, Web
browser and Web servers, Features of Web 2.0 Web Design: Concepts of effective web design, Web design
issues including Browser, Bandwidth and Cache, Display resolution, Look and Feel of the Website, Page
Layout and linking, User centric design, Sitemap, Planning and publishing website, Designing effective
navigation.
UNIT II: HTML: Basics of HTML, formatting and fonts, commenting code, color, hyperlink, lists, tables,
images, forms, XHTML, Meta tags, Character entities, frames and frame sets, Browser architecture and
Web site structure. Overview and features of HTML5
UNIT III: Style sheets : Need for CSS, introduction to CSS, basic syntax and structure, using
CSS,background images, colors and properties, manipulating texts, using fonts, borders and boxes, margins,
padding lists, positioning using CSS, CSS2, Overview and features of CSS3JavaScript : Client side
scriptingwith JavaScript, variables, functions, conditions, loops and repetition, Pop up boxes, Advance
JavaScript: Javascript and objects, JavaScript own objects, the DOM and web browser environments,
Manipulation using DOM, forms andvalidations,DHTML : Combining HTML, CSS and JavaScript, Events
and buttons.
UNIT IV:XML: Introduction to XML, uses of XML, simple XML, XML key components,
DTDandSchemas, Using XML with application. Transforming XML using XSL and XSLT
PHP:Introduction and basic syntax of PHP, decision and looping with examples, PHP and HTML,Arrays,
Functions, Browser control and detection, string, Form processing, Files, AdvanceFeatures: Cookies and
Sessions, Object Oriented Programming with PHP.
UNIT V: PHP and MySQL:Basiccommandswith PHP examples, Connection to server, creating atabase,
selecting a database, listing database, listing table names,creating a table, insertingdata, altering tables,
queries, deleting database, deleting data and tables, PHP myadminanddatabasebugs
Reference Books:
1.Developing Web Applications, Ralph Moseley and M. T. Savaliya, Wiley-India
2.Web Technologies, Black Book, dreamtech Press
3.HTML 5, Black Book, dreamtech Press
4.Web Design, Joel Sklar, Cengage Learning
5.Developing Web Applications in PHP and AJAX, Harwani, McGrawHill
6.Internet and World Wide Web How to program, P.J. Deitel& H.M. Deitel , PearsonEducation.
7 Developing Web Applications, 2ed by Ralph Mosely, MT Savaliya,Publisher Wiley India Pvt
Ltd
