Class 11 AI Notes | All Units | CBSE Subject Code 843
| Unit | Topic | Theory Hrs | Marks |
|---|---|---|---|
| Unit 1 | Introduction: AI for Everyone | 4 | 4 |
| Unit 2 | Unlocking Your Future in AI | 6 | 5 |
| Unit 3 | Python Programming | 10 | 5 |
| Unit 4 | Introduction to Capstone Project | 6 | 5 |
| Unit 5 | Data Literacy: Collection to Analysis | 6 | 6 |
| Unit 6 | Machine Learning Algorithms | 9 | 6 |
| Unit 7 | Leveraging Linguistics & CS (NLP) | 5 | 5 |
| Unit 8 | AI Ethics and Values | 4 | 4 |
Introduction: Artificial Intelligence for Everyone
- Communicate effectively about AI concepts in written and oral formats
- Describe the historical development of AI
- Differentiate between types and domains of AI including applications
- Recognize key terminologies related to ML and Deep Learning
- Formulate informed opinions on benefits and limitations of AI
1.1 What is Artificial Intelligence?
Artificial Intelligence (AI) is a branch of computer science that focuses on building machines that can perform tasks that normally require human intelligence. These tasks include reasoning, learning, problem-solving, perception, and language understanding.
Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. It includes learning, reasoning, and self-correction.
1.2 Evolution of AI
| Era | Key Development |
|---|---|
| 1950s | Alan Turing proposes the Turing Test; birth of AI as a discipline |
| 1960s–70s | Expert Systems developed; first AI winter due to limited computing power |
| 1980s–90s | Machine Learning emerges; neural networks revived |
| 2000s | Big Data and improved algorithms accelerate AI research |
| 2010s–now | Deep Learning, GPUs, Cloud AI — ChatGPT, self-driving cars, facial recognition |
1.3 Types of AI
Designed for a specific task. Examples: Siri, Google Translate, Chess engines, recommendation systems.
Can perform any intellectual task a human can. Still theoretical — does not yet exist.
Surpasses human intelligence in all areas. Hypothetical — subject of research and ethical debate.
1.4 Three Domains of AI
Enables machines to interpret and understand visual input. Examples: Face recognition, medical imaging, self-driving cars.
Enables machines to understand human language. Examples: Chatbots, translation, sentiment analysis.
Converts spoken language into text/commands. Examples: Alexa, Google Assistant, voice-to-text.
1.5 Key AI Terminologies
- AI = making machines think and act like humans
- Three domains: Computer Vision, NLP, Speech Recognition
- AI → Machine Learning → Deep Learning (nested relationship)
- Narrow AI exists today; General AI and Super AI are future concepts
Unlocking Your Future in AI
- Articulate demand for AI professionals and career opportunities
- Identify skills and tools for a career in AI
- Understand roles and responsibilities of AI professionals
- Evaluate personal interests for AI career pathways
2.1 Global Demand for AI Professionals
AI is one of the fastest-growing fields globally. According to World Economic Forum reports, AI and automation will create 97 million new jobs by 2025 while transforming existing ones. India is among the top countries with growing AI talent demand.
2.2 Common Job Roles in AI
Builds and deploys ML models and AI systems.
Analyses complex data to help organizations make decisions.
Develops systems that understand human language.
Ensures AI systems are fair, transparent and accountable.
Designs and programs intelligent robotic systems.
Builds systems that process and interpret images/video.
2.3 Essential Skills for AI Careers
- Python programming
- Mathematics (Statistics, Linear Algebra)
- Machine Learning frameworks
- Data analysis and visualization
- Cloud computing basics
- Critical thinking
- Problem-solving ability
- Communication skills
- Teamwork and collaboration
- Continuous learning mindset
Python Programming
- Explain basics of Python — character sets, tokens, modes, operators, datatypes
- Use selective and iterative (control) statements effectively
- Use libraries: NumPy, Pandas, Scikit-learn efficiently
- Work with CSV files for data handling
3.1 Level 1 — Python Basics
3.2 Control Statements
age = 18
if age >= 18:
print("Adult")
else:
print("Minor")
fruits = ["apple","mango"]
for fruit in fruits:
print(fruit)
i = 1
while i <= 5:
print(i)
i = i + 1
3.3 Level 2 — Libraries
Numerical computing library for arrays and mathematical operations.
import numpy as np a = np.array([1,2,3]) print(a.mean())
Data analysis library for working with tables (DataFrames).
import pandas as pd
df = pd.read_csv("data.csv")
print(df.head())
Machine learning library for building and training ML models.
from sklearn.linear_model import LinearRegression model = LinearRegression()
Introduction to Capstone Project
- Decompose any problem using the 5W1H method
- Apply Design Thinking methodology
- Create Empathy Maps
- Align problems to Sustainable Development Goals (SDGs)
4.1 Design Thinking
Design Thinking is a human-centered approach to problem-solving. It focuses on understanding users, challenging assumptions, and creating innovative solutions.
4.2 Empathy Map
An Empathy Map helps you understand your users better by examining what they Say, Think, Do and Feel. It is used in the first stage of Design Thinking (Empathise).
Data Literacy — Data Collection to Data Analysis
- Explain the importance of data literacy in AI
- Identify different data collection methods
- Apply basic statistical analysis techniques
- Visualize data using Python (matplotlib)
- Understand matrices and data pre-processing
5.1 What is Data Literacy?
Data Literacy is the ability to read, understand, create and communicate data as information. It is a key skill in AI as all AI systems depend on data to learn and make decisions.
5.2 Statistical Analysis — Key Terms
5.3 Data Visualization with Matplotlib
import matplotlib.pyplot as plt
# Line Graph
plt.plot([1,2,3,4], [10,20,15,30])
plt.title("Line Graph")
plt.show()
# Bar Graph
plt.bar(["A","B","C"], [5,10,7])
plt.title("Bar Graph")
plt.show()
# Histogram
plt.hist([1,2,2,3,3,3,4,4,4,4], bins=4)
plt.title("Histogram")
plt.show()
Machine Learning Algorithms
- Differentiate between types of Machine Learning
- Understand Linear Regression, kNN and k-Means algorithms
- Apply ML methods to solve day-to-day problems
6.1 Types of Machine Learning
Trained on labelled data. Machine learns from examples with correct answers.
Examples: Linear Regression, Classification
Trained on unlabelled data. Machine finds patterns on its own.
Examples: k-Means Clustering
Learns through reward and penalty system — trial and error.
Examples: Game playing AI, robots
6.2 Key Algorithms
Leveraging Linguistics and Computer Science (NLP)
- Understand complexities of human language and NLP challenges
- Learn techniques and algorithms for NLP tasks
- Create a simple chatbot using online platforms
7.1 What is NLP?
Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and generate human language in a way that is both meaningful and useful.
7.2 Applications of NLP
Customer support automation
Google Translate, DeepL
Detecting emotions in text
Auto-summarizing articles
7.3 Phases of NLP
AI Ethics and Values
- Understand fundamental principles of ethics in AI
- Develop understanding of AI bias and its sources
- Identify strategies for mitigating bias in AI systems
- Recognize the significance of AI policies
8.1 The Five Pillars of AI Ethics
AI must treat all people equally without discrimination
AI decisions must be explainable and understandable
User data must be protected and used responsibly
Developers must be responsible for AI outcomes
AI systems must not cause harm to humans
8.2 AI Bias
AI Bias occurs when an AI system produces unfair outcomes due to flawed assumptions in its training data or algorithm design. Biased AI can lead to discrimination in hiring, lending, healthcare and more.
Comments
Post a Comment