Course Content

Computer Applications

Unit 1: Networking

• Internet: World Wide Web, web servers, web clients, web sites, web pages, web browsers, blogs, newsgroups, HTML, web address, e-mail address, downloading and uploading files from a remote site.
• Internet protocols: TCP/IP, SMTP, POP3, HTTP, HTTPS. Remote login and file transfer protocols: SSH,
• SFTP, FTP, SCP, TELNET, SMTP, TCP/IP.
• Services available on the internet: information retrieval, locating sites using search engines, and finding people on the net;
• Web services: chat, email, video conferencing, e-Learning, e-Banking, eShopping, e-Reservation, e-
• Governance, e-Groups, social networking.
• Mobile technologies: SMS, MMS, 3G, 4G.

Unit 2: HTML

• Introduction to web page designing using HTML: create and save an HTML document, access a web page using a web browser.
• HTML tags: html, head, title, body, (attributes: text, background, bgcolor, link, vlink, alink), br (break), hr(horizontal rule), inserting comments, h1..h6 (heading), p (paragraph), b (bold), i (italics), u (underline), ul (unordered list), ol (ordered list), and li (list item). Description lists: dl, dt and dd.
• Attributes of ol (start, type), ul (type).
• Font tags (attributes: face, size, color).
• Insert images: img (attributes: src, width, height, alt), sup (super script), sub (subscript).
• HTML Forms: Textbox, radio buttons, checkbox, password, list, combobox.
• Embed audio and video on an HTML page.
• Create a table using the tags: table, tr, th, td, rowspan, colspan
• Links: significance of linking, anchor element (attributes: href, mailto), targets.
• Cascading style sheets: color, background-color, border-style, margin, height, width, outline, font (family, style, size), align, float.

Unit 3: Cyber ethics

• Netiquettes.
• Software licenses and the open-source software movement.
• Intellectual property rights, plagiarism, and digital property rights.
• Freedom of information and the digital divide.
• E-commerce: Privacy, fraud, secure data transmission.

Unit 4: Lab Exercises

• Create static web pages.
• Use style sheets to enforce a format in an HTML page (CSS).
• Embed pictures, audio, and videos on an HTML page.
• Add tables and frames to an HTML page.
• Decorate web pages using graphical elements.
• Create a website using several web pages. Students may use any open source or proprietary tool.
• Work with HTML forms: text box, radio buttons, checkbox, password, list, combo box.
• Write a blog using HTML pages discussing viruses, malware, spam, and antiviruses
• Create a web page discussing plagiarism. List some reported cases of plagiarism and the consequent punishment meted out. Explain the nature of the punishment in different countries as per their IP laws.

Artificial Intelligence

Unit 1: INTRODUCTION TO AI

Foundational concepts of AI

• Session: What is Intelligence?
• Session: Decision Making.
  • How do you make decisions?
  • Make your choices!

Basics of AI: Let's Get Started

• Session: Introduction to AI and related terminologies.
  • Introducing AI, ML & DL.
  • Introduction to AI Domains (Data, CV & NLP)
• Session: Applications of AI – A look at Real-life AI implementations
• Session: AI Ethics

Unit 2: AI PROJECT CYCLE

Introduction

• Session: Introduction to AI Project Cycle

Problem Scoping

• Session: Understanding Problem Scoping & Sustainable Development Goals

Data Acquisition

• Session: Simplifying Data Acquisition

Data Exploration

• Session: Visualising Data

Modeling

• Session: Introduction to modeling
  • Introduction to Rule-Based & Learning Based AI Approaches
  • Introduction to Supervised Unsupervised & Reinforcement Learning Models
  • Neural Networks

Evaluation

• Session: Evaluating the idea!

Unit 3: ADVANCE PYTHON (To be assessed through Practicals)

• Session: Jupyter Notebook/or any other platform
• Session: Introduction to Python
• Session: Python Basics

Unit 4: DATA SCIENCES (To be assessed through Practicals)

Introduction

• Session: Introduction to Data Science
• Session: Applications of Data Science
• Session: Revisiting AI Project Cycle

Concepts of Data Sciences

• Session: Python for Data Sciences
• Session: Statistical Learning & Data Visualisation

K-nearest neighbor model (Optional)

• Activity: Personality Prediction (Optional)
• Session: Understanding K-nearest neighbor model (Optional)

Unit 5: COMPUTER VISION (To be assessed through Practicals)

Introduction

• Session: Introduction to Computer Vision
• Session: Applications of CV

Concepts of Computer Vision

• Session & Activity: Understanding CV Concepts
  • Pixels
  • How do computers see images?
  • Image Features

OpenCV

• Session: Introduction to OpenCV
• Hands-on: Image Processing

Convolution Operator (Optional)

• Session: Understanding Convolution operator (Optional)
• Activity: Convolution Operator (Optional)

Convolution Neural Network (Optional)

• Session: Introduction to CNN (Optional)
• Session: Understanding CNN (Optional)
• Activity: Testing CNN (Optional)
  • Kernel
  • Layers of CNN

Unit 6: NATURAL LANGUAGE PROCESSING

Introduction

• Session: Introduction to Natural Language Processing
• Session: NLP Applications
• Session: Revisiting AI Project Cycle

Chatbots

• Activity: Introduction to Chatbots

Language Differences

• Session: Human Language VS Computer Language

Concepts of Natural Language Processing

• Hands-on: Text processing
  • Data Processing
  • Bag of Words
  • TFIDF (Optional)
  • NLTK

Unit 7: EVALUATION

Introduction

• Session: Introduction to Model Evaluation

Confusion Matrix

• Session & Activity: Confusion Matrix

Evaluation Score Calculation

• Session: Understanding Accuracy, Precision, Recall & F1 Score

Activity: Practice Evaluation