Artificial Intelligence: Machine Learning

Artificial Intelligence

Machine Learning

• A subset of AI that involves training machines to perform tasks without being explicitly programmed
• Types:
  • Supervised Learning: Training data is labeled and the machine learns to map inputs to outputs
  • Unsupervised Learning: Training data is unlabeled and the machine discovers patterns and relationships
  • Reinforcement Learning: Machine learns through trial and error by receiving rewards or penalties for its actions
• Applications:
  • Image and speech recognition
  • Natural Language Processing (NLP)
  • Predictive modeling and decision-making

Natural Language Processing (NLP)

• A subset of AI that focuses on the interaction between computers and human language
• Goals:
  • Language understanding: enabling computers to comprehend human language
  • Language generation: enabling computers to produce human-like language
• Applications:
  • Sentiment analysis and opinion mining
  • Language translation and localization
  • Chatbots and virtual assistants

Deep Learning

• A subset of Machine Learning that uses neural networks with multiple layers
• Characteristics:
  • Ability to learn complex patterns and relationships in large datasets
  • Improved performance with increasing dataset size
• Applications:
  • Image and speech recognition
  • Natural Language Processing (NLP)
  • Game playing and decision-making

Neural Networks

• A type of Machine Learning model inspired by the structure and function of the human brain
• Components:
  • Artificial neurons ( nodes or perceptrons)
  • Connections between neurons (edges or synapses)
• Types:
  • Feedforward Networks: Information flows only in one direction
  • Recurrent Neural Networks (RNNs): Information flows in a loop, allowing for feedback
  • Convolutional Neural Networks (CNNs): Used for image and signal processing

Artificial Intelligence

• Machine Learning is a subset of AI that involves training machines to perform tasks without being explicitly programmed
• Types of Machine Learning include:

  Supervised Learning

  • Training data is labeled and the machine learns to map inputs to outputs

  Unsupervised Learning

  • Training data is unlabeled and the machine discovers patterns and relationships

  Reinforcement Learning

  • Machine learns through trial and error by receiving rewards or penalties for its actions
• Applications of Machine Learning include:
  • Image and speech recognition
  • Natural Language Processing (NLP)
  • Predictive modeling and decision-making

Natural Language Processing (NLP)

• NLP is a subset of AI that focuses on the interaction between computers and human language
• Goals of NLP include:
  • Language understanding: enabling computers to comprehend human language
  • Language generation: enabling computers to produce human-like language
• Applications of NLP include:
  • Sentiment analysis and opinion mining
  • Language translation and localization
  • Chatbots and virtual assistants

Deep Learning

• Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers
• Characteristics of Deep Learning include:
  • Ability to learn complex patterns and relationships in large datasets
  • Improved performance with increasing dataset size
• Applications of Deep Learning include:
  • Image and speech recognition
  • Natural Language Processing (NLP)
  • Game playing and decision-making

Neural Networks

• Neural Networks are a type of Machine Learning model inspired by the structure and function of the human brain
• Components of Neural Networks include:
  • Artificial neurons (nodes or perceptrons)
  • Connections between neurons (edges or synapses)
• Types of Neural Networks include:

  Feedforward Networks

  • Information flows only in one direction

  Recurrent Neural Networks (RNNs)

  • Information flows in a loop, allowing for feedback

  Convolutional Neural Networks (CNNs)

  • Used for image and signal processing