Artificial Intelligence

Lesson 1: Introduction to AI (a must for all students)

Objective: To understand the basics of Artificial Intelligence (AI), its types, applications and its impact on society.

1.1 What is AI?

Definition: Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn.

Examples:

1. Virtual Assistants: Siri, Alexa, Google Assistant.

2. Recommendation Systems: Netflix, YouTube, Amazon.

Platforms like Netflix, YouTube and Amazon use sophisticated algorithms to analyse user preferences, viewing or purchase history and behavioral patterns. Based on this data, they suggest personalised content such as movies, videos or products tailored to individual tastes. For example:

• Netflix recommends shows and movies by analysing genres, actors and ratings users tend to engage with.
• YouTube curates a feed of videos based on watch history, likes and even comments to keep users engaged.
• Amazon provides tailored product recommendations, such as complementary items to past purchases or trending products that align with users' interests.

These systems enhance user experience by offering relevant suggestions, increasing engagement and driving customer satisfaction.

3. Applied AI: Self-driving cars, fraud detection and medical diagnosis.

1.2 Types of AI

Narrow AI:

Focused on specific tasks. Example: Spam filters in email, facial recognition.

General AI:

Hypothetical ability to perform any intellectual task a human can. Example: No real-world example yet; this is a research goal.

Super AI:

AI surpassing human intelligence in all areas. Example: Still theoretical.

1.3 Applications of AI

Daily Life Examples:

• Social Media: AI powers personalised content recommendations on platforms like Facebook and Instagram by analysing user behavior and preferences to boost engagement.
• Healthcare: AI predicts diseases and enables personalised medicine by analysing medical data and patient histories.
• Finance: AI supports stock trading and credit risk assessment through predictive models and data analysis.

Industry Examples:

• Transportation: AI drives autonomous vehicles for safer, more efficient travel.
• Retail: AI enables personalised shopping experiences by analysing customer preferences.
• Education: AI provides adaptive learning and tutoring tailored to individual needs.

1.4 Benefits and Challenges of AI

Benefits:

• Automation of repetitive tasks.
• Improved efficiency and accuracy.
• Innovation in various fields.

Challenges:

• Ethical concerns: Bias, privacy.
• Job displacement.
• Dependence on technology.

1.5 Exercise

Task 1: Identify AI in Daily Life

List 5 examples of AI systems you use or encounter in your daily routine.

Classify each example as Narrow AI, General AI or Super AI.

Task 2: Quick Quiz

What type of AI focuses on specific tasks?

• a) Narrow AI
• b) General AI
• c) Super AI

Which of the following is a real-world application of AI?

• a) Time travel
• b) Spam filters in email
• c) Mind reading

1.6 Discussion Point

How do you think AI could positively or negatively impact your job or daily life in the future?

1.7 Additional Resources

Video: "What is AI?"

Article: "A Beginner’s Guide to AI" by OpenAI.

Tool: Explore Google’s Teachable Machine.

Outcome: By the end of this lesson, participants will have a clear understanding of what AI is, its types and its applications in various fields.

Lesson 2: Setting Up the Environment (advanced learning)

Objective: To set up the tools and software required for AI programming and familiarise participants with basic Python concepts.

2.1 Installing Python and Jupyter Notebook

1. Download and install Python:

   • Go to Python’s official website.
   • Download the latest version suitable for your operating system.
   • Follow the installation instructions.

2. Install Jupyter Notebook:

   pip install notebook

3. Verify installation:

   • Open a terminal/command prompt and type: jupyter notebook.
   • A web interface should open.

2.2 Overview of Python Libraries for AI

• Numpy: For numerical operations.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualisation.
• Scikit-learn: For basic machine learning tasks.

Key Point: These libraries simplify tasks like handling data, building models and visualising results.

2.3 Basic Python Concepts

• Variables:

  Containers for storing data values.

  x = 5

• Data Types:

  Common types: int, float, str, list, dict.

• Loops:

  Used for iterating over a sequence.

  for i in range(5):
      print(i)
  

• Functions:

  Reusable blocks of code.

  def greet(name):
      return f"Hello, {name}!"
  print(greet("AI Enthusiast"))

2.4 Exercise

Task 1: Python Basics

1. Write a Python script to:
   • Store a list of numbers.
   • Calculate the average of the numbers.
   • Print the result.
2. Create a bar chart using Matplotlib for the following data:
   • Categories: A, B, C, D.
   • Values: 5, 7, 3, 8.

Task 2: Discussion

Why is Python a preferred language for AI programming?

2.5 Additional Resources

• Official Python Documentation
• Numpy Tutorial
• Matplotlib Guide

Outcome: By the end of this lesson, participants will have a functional Python environment for AI programming and understand basic Python concepts essential for building AI models.

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