Artificial Intelligence Class 10 Syllabus

Part B which is subject specific skills has seven units: (i) Introduction to Artificial Intelligence (AI), (ii) AI Project Cycle, (iii) Advance Python, (iv) Data Science, (v) Computer Vision, (vi) Natural Language Processing, and (vii) Evaluation.

Part B: Subject Specific Skills

  • Unit 1: Introduction to Artificial Intelligence (AI)
  • Unit 2: AI Project Cycle
  • Unit 3: Advance Python (To be assessed in Practicals only)
  • Unit 4: Data Science (To be assessed in Practicals only)
  • Unit 5: Computer Vision (To be assessed in Practicals only)
  • Unit 6: Natural Language Processing
  • Unit 7: Evaluation

Part C: Practical Work

  • Unit 3: Advance Python
  • Unit 4: Data Science
  • Unit 5: Computer Vision

Unit 1: Introduction to Artificial Intelligence (AI)

Foundational concepts of AI

Session: What is Intelligence?

Session: Decision Making.

  • How do you make decisions?
  • Make your choices!

Session: what is Artificial Intelligence and what is not?

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

Modelling

Session: Introduction to modelling

  • 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 in Practicals only)

Recap

Session: Jupyter Notebook

Session: Introduction to Python

Session: Python Basics

Unit 4: Data Science

(To be assessed in Practicals only)

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 neighbour model

Activity: Personality Prediction

Session: Understanding K-nearest neighbour model

Unit 5: Computer Vision

(To be assessed in Practicals only)

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

Session: Understanding Convolution operator

Activity: Convolution Operator

Convolution Neural Network

Session: Introduction to CNN

Session: Understanding CNN

  • Kernel
  • Layers of CNN

Activity: Testing 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
  • 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