Beginner Theory

Introduction to Artificial Intelligence

Fundamentals of AI and Machine Learning

Learning Objectives

  • Understand the definition and scope of Artificial Intelligence
  • Differentiate between AI, Machine Learning, and Deep Learning
  • Explore real-world applications of AI in various domains
  • Discuss ethical considerations in AI development

Lecture Outline

Part 1: What is AI? (15 min)

  • • Definition and history of AI
  • • Types of AI: Narrow vs. General AI
  • • AI vs. Machine Learning vs. Deep Learning

Part 2: AI Applications (15 min)

  • • Healthcare: diagnosis, drug discovery, personalized medicine
  • • Business: recommendation systems, fraud detection, automation
  • • Other domains: autonomous vehicles, NLP, computer vision

Part 3: How AI Works (10 min)

  • • Data → Model → Predictions
  • • Training and inference
  • • Common algorithms overview

Part 4: Ethics & Future (5 min)

  • • Bias and fairness in AI
  • • Privacy and security concerns
  • • Future trends and implications

Key Concepts

Artificial Intelligence

The simulation of human intelligence in machines that are programmed to think and learn.

Machine Learning

A subset of AI that enables systems to learn from data without explicit programming.

Deep Learning

A subset of ML using neural networks with multiple layers to model complex patterns.

Neural Networks

Computing systems inspired by biological neural networks in the human brain.

Resources & Tools

Generate Slides with Gemini

Create presentation slides for this lecture

Enhance Content with ChatGPT

Generate examples and discussion questions

Code Demos with Cursor

Build simple AI demonstrations

Student Activities

  • Discussion: What AI applications do you use daily without realizing it?
  • Group Work: Brainstorm potential AI solutions for healthcare challenges
  • Homework: Write a 1-page reflection on ethical considerations in AI