Machine Learning: Supervised Learning

Machine Learning

Supervised Learning

• Type of machine learning where the model is trained on labeled data
• Goal is to learn a mapping between input data and output labels
• Supervised learning tasks:
  • Regression: predict continuous output variable (e.g. house prices)
  • Classification: predict categorical output variable (e.g. spam/not spam emails)
• Common algorithms:
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVMs)

Deep Learning

• Subfield of machine learning that involves neural networks with multiple layers
• Inspired by structure and function of the human brain
• Capabilities:
  • Image recognition
  • Speech recognition
  • Natural Language Processing (NLP)
  • Game playing (e.g. Go, Poker)
• Common deep learning algorithms:
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory (LSTM) networks
  • Generative Adversarial Networks (GANs)

Natural Language Processing (NLP)

• Subfield of artificial intelligence that deals with human-computer interaction
• Focus on understanding, generating, and processing human language
• NLP tasks:
  • Sentiment Analysis
  • Language Translation
  • Text Summarization
  • Named Entity Recognition
• Common NLP techniques:
  • Tokenization
  • Part-of-Speech (POS) tagging
  • Named Entity Recognition (NER)
  • Dependency Parsing

Unsupervised Learning

• Type of machine learning where the model is trained on unlabeled data
• Goal is to discover patterns or structure in the data
• Unsupervised learning tasks:
  • Clustering: group similar data points together
  • Dimensionality Reduction: reduce number of features in the data
  • Anomaly Detection: identify unusual data points
• Common algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

Reinforcement Learning

• Type of machine learning where the model learns through trial and error
• Goal is to maximize reward signal in a complex, uncertain environment
• Reinforcement learning tasks:
  • Game playing (e.g. Atari games, Go)
  • Robotics: control robots to perform tasks
  • Recommendation systems: personalize recommendations
• Common algorithms:
  • Q-Learning
  • SARSA
  • Deep Q-Networks (DQN)
  • Policy Gradient Methods

Machine Learning

Supervised Learning

• Trained on labeled data, the model learns to map inputs to outputs
• Goal: learn a mapping between input data and output labels
• Tasks:
  • Regression: predict continuous output variable (e.g., house prices, stock prices)
  • Classification: predict categorical output variable (e.g., spam/not spam emails, cancer diagnosis)
• Algorithms:
  • Linear Regression: linear relationship between input and output variables
  • Logistic Regression: binary classification, outputs probability of an event
  • Decision Trees: tree-based model, splits data into subsets based on features
  • Random Forest: ensemble of decision trees, improves accuracy and reduces overfitting
  • Support Vector Machines (SVMs): finds hyperplane that separates classes with maximum margin

Deep Learning

• Involves neural networks with multiple layers, inspired by the human brain
• Capabilities:
  • Image recognition: objects, scenes, and activities
  • Speech recognition: transcribe spoken words into text
  • Natural Language Processing (NLP): language understanding, generation, and processing
  • Game playing: Go, Poker, and other complex games
• Algorithms:
  • Convolutional Neural Networks (CNNs): image recognition, object detection
  • Recurrent Neural Networks (RNNs): sequential data, language modeling, and machine translation
  • Long Short-Term Memory (LSTM) networks: handles vanishing gradients, better for long-term dependencies
  • Generative Adversarial Networks (GANs): generate new data samples, images, and videos

Natural Language Processing (NLP)

• Deals with human-computer interaction, understanding, generating, and processing human language
• Tasks:
  • Sentiment Analysis: determine sentiment (positive, negative, neutral) of text
  • Language Translation: translate text from one language to another
  • Text Summarization: summarize long documents, extracting key points
  • Named Entity Recognition: identify named entities (people, places, organizations)
• Techniques:
  • Tokenization: split text into individual words or tokens
  • Part-of-Speech (POS) tagging: identify grammatical categories (noun, verb, adjective)
  • Named Entity Recognition (NER): identify and classify named entities
  • Dependency Parsing: analyze sentence structure, identify subject, object, and modifiers

Unsupervised Learning

• Trained on unlabeled data, the model discovers patterns or structure
• Goal: identify hidden patterns, group similar data points, or reduce dimensionality
• Tasks:
  • Clustering: group similar data points together (customer segmentation, gene expression)
  • Dimensionality Reduction: reduce number of features in the data (e.g., PCA, t-SNE)
  • Anomaly Detection: identify unusual data points (fraud detection, network intrusion)
• Algorithms:
  • K-Means Clustering: partition data into K clusters based on similarity
  • Hierarchical Clustering: build a hierarchy of clusters, visualize relationships
  • Principal Component Analysis (PCA): reduce dimensionality, retain most information
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): non-linear dimensionality reduction, better for complex data

Reinforcement Learning

• Trained through trial and error, the model learns to maximize a reward signal
• Goal: learn to take actions in a complex, uncertain environment
• Tasks:
  • Game playing: learn to play games like Atari, Go, or Poker
  • Robotics: control robots to perform tasks, learn from experience
  • Recommendation systems: personalize recommendations, maximize user engagement
• Algorithms:
  • Q-Learning: updates action-value function, learns to predict expected rewards
  • SARSA: updates state-value function, learns to predict expected rewards and next state
  • Deep Q-Networks (DQN): combines Q-learning with neural networks, handles large state-action spaces
  • Policy Gradient Methods: learns the optimal policy, directly updates policy parameters

Test your knowledge of supervised learning in machine learning, including regression and classification tasks, and common algorithms such as linear regression and decision trees.