Fundamentals of Artificial Intelligence and Machine Learning

📋 Course Outline

1. Introduction to AI
2. Machine Learning Basics
3. Supervised Learning
4. Unsupervised Learning
5. Deep Learning Fundamentals
6. Neural Networks
7. Model Evaluation
8. AI Applications

📖 1. Introduction to AI

🔑 Key Concepts & Definitions

• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. Russell and Norvig (2010): "AI is the study of agents that perceive their environment and take actions to maximize their chances of success."
• History and Evolution of AI: The development of AI has progressed through several phases, starting from symbolic AI in the 1950s to modern machine learning and deep learning approaches, reflecting advancements in computational power and data availability. McCarthy (1956): Coined the term "Artificial Intelligence" at the Dartmouth Conference, marking the birth of AI as a field.
• Types of AI:
  • Narrow AI: AI systems designed for specific tasks, such as voice assistants or image recognition. Author unknown: "Narrow AI operates within a limited context and cannot perform beyond its programming."
  • General AI: Hypothetical AI with human-like cognitive abilities, capable of understanding, learning, and applying knowledge across diverse domains. Author unknown: "General AI would possess consciousness and reasoning comparable to humans."

📝 Essential Points