Artificial Intelligence Concepts

Machine Learning

• A subset of artificial intelligence that involves training machines to learn from data and make predictions or decisions without being explicitly programmed.
• Types of machine learning:
  • Supervised learning: training data is labeled, and the algorithm learns to map inputs to outputs.
  • Unsupervised learning: training data is unlabeled, and the algorithm learns to identify patterns or relationships.
  • Reinforcement learning: algorithm learns through trial and error by interacting with an environment.

Natural Language Processing

• A subfield of artificial intelligence that focuses on the interaction between computers and humans in natural language.
• Key applications:
  • Language translation
  • Sentiment analysis
  • Text summarization
  • Chatbots
• NLP techniques:
  • Tokenization: breaking down text into individual words or tokens
  • Part-of-speech tagging: identifying the grammatical category of each word
  • Named entity recognition: identifying and extracting specific entities such as names, locations, and organizations

Computer Vision

• A subfield of artificial intelligence that focuses on enabling computers to interpret and understand visual data from the world.
• Key applications:
  • Image recognition and classification
  • Object detection and tracking
  • Image segmentation
  • Facial recognition
• Computer vision techniques:
  • Convolutional neural networks (CNNs): using neural networks to process and analyze images
  • Edge detection: identifying the boundaries between objects in an image

Deep Learning

• A subset of machine learning that involves the use of neural networks with multiple layers to analyze and interpret data.
• Key applications:
  • Image recognition and classification
  • Speech recognition
  • Natural language processing
  • Game playing and decision making
• Deep learning techniques:
  • Convolutional neural networks (CNNs): using neural networks to process and analyze images
  • Recurrent neural networks (RNNs): using neural networks to process and analyze sequential data

Neural Networks

• A machine learning model inspired by the structure and function of the human brain.
• Key components:
  • Artificial neurons: individual units that process and transmit information
  • Connections: the links between artificial neurons
  • Layers: the organization of artificial neurons into hierarchical structures
• Neural network types:
  • Feedforward neural networks: information flows only in one direction, from input to output
  • Recurrent neural networks (RNNs): information flows in a loop, allowing the network to keep track of state