Machine Learning Fundamentals

Machine Learning

Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

Supervised Learning

• Type of machine learning where the algorithm is trained on labeled data
• Goal is to learn a mapping between input data and output labels
• Examples: image classification, speech recognition, sentiment analysis
• Types of supervised learning:
  • Regression: predicts continuous output variable
  • Classification: predicts categorical output variable

Deep Learning

• Subset of machine learning that uses neural networks with multiple layers
• Inspired by structure and function of the human brain
• Can be used for both supervised and unsupervised learning
• Types of deep learning:
  • Convolutional Neural Networks (CNNs): image recognition, object detection
  • Recurrent Neural Networks (RNNs): sequential data, language modeling

Neural Networks

• Model composed of interconnected nodes (neurons) that process inputs
• Each node applies an activation function to the input, producing an output
• Can be used for supervised, unsupervised, and reinforcement learning
• Types of neural networks:
  • Feedforward Networks: no feedback loops
  • Feedback Networks: feedback loops, used for RNNs

Reinforcement Learning

• Type of machine learning where the algorithm learns through trial and error
• Goal is to maximize a reward signal by taking actions in an environment
• Examples: game playing, robotics, autonomous driving
• Key concepts:
  • Agent: takes actions in the environment
  • Environment: responds to the agent's actions
  • Reward: feedback signal for the agent's actions
  • Policy: mapping from state to action
  • Value function: estimates expected reward for each state

Machine Learning

• Subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

Supervised Learning

• Trained on labeled data to learn a mapping between input data and output labels.
• Goal is to make predictions on new, unseen data.
• Examples include image classification, speech recognition, and sentiment analysis.
• Two types of supervised learning:
• Regression: predicts continuous output variable.
• Classification: predicts categorical output variable.

Deep Learning

• Subset of machine learning that uses neural networks with multiple layers.
• Inspired by the structure and function of the human brain.
• Can be used for both supervised and unsupervised learning.
• Examples include:
• Convolutional Neural Networks (CNNs): used for image recognition and object detection.
• Recurrent Neural Networks (RNNs): used for sequential data and language modeling.

Neural Networks

• Model composed of interconnected nodes (neurons) that process inputs.
• Each node applies an activation function to the input, producing an output.
• Can be used for supervised, unsupervised, and reinforcement learning.
• Types of neural networks:
• Feedforward Networks: no feedback loops.
• Feedback Networks: have feedback loops, used for RNNs.

Reinforcement Learning

• Type of machine learning where the algorithm learns through trial and error.
• Goal is to maximize a reward signal by taking actions in an environment.
• Examples include game playing, robotics, and autonomous driving.
• Key concepts:
• Agent: takes actions in the environment.
• Environment: responds to the agent's actions.
• Reward: feedback signal for the agent's actions.
• Policy: mapping from state to action.
• Value function: estimates expected reward for each state.