Artificial Intelligence (AI)

• The science of building software that can perform tasks that normally require human intelligence.
• Human Intelligence → AI mimics → Decision / Prediction / Action

AI Learning

• Machine learning:
  • Teaches machines to learn from data.
• Deep Learning:
  • Uses multi-layer neural networks to learn complex patterns.

AI
 ├─ Machine Learning (ML)
 │    ├─ Supervised
 │    ├─ Unsupervised
 │    └─ Reinforcement Learning (RL)
 └─ Deep Learning (DL) → subset of ML using neural networks
      ├─ CNN (images)
      ├─ RNN/LSTM (sequences)
      └─ Transformers (NLP, LLMs)

AI Techniques

1. Symbolic / Rule-Based AI
   • Uses rules & logic to make decisions
   • Pros: Explainable, deterministic
   • Cons: Doesn’t scale for complex tasks
2. Statistical / ML-Based AI
   • Learns from data patterns
   • Pros: Flexible, powerful
   • Cons: Needs lots of data, may be a black box
3. Neural Networks / Deep Learning
   • Inspired by human brain
   • Handles complex patterns in images, text, speech
4. Reinforcement Learning (RL)
   • Learns by trial & error, maximizing rewards
   • Used in games, robotics, autonomous driving