AIPrimer.AI
  • 🚦AI Primer In Transportation
  • CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning in Transportation
    • What is Machine Learning?
    • Types of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised Learning
      • Reinforced Learning
    • Fundamental concepts of machine learning
      • Model Training and Testing
      • Evaluating the Model’s Prediction Accuracy
      • The Underfitting and Overfitting Problems
      • Bias-Variance Tradeoff in Overfitting
      • Model Validation Techniques
      • Hyperparameter Tuning
      • Model Regularization
      • The Curse of Ddimensionality
    • Machine Learning versus Statistics
  • CHAPTER 2 - SUPERVISED METHODS
    • Supervised Learning_Complete Draft
    • K-Nearest Neighbor (KNN) Algorithm
    • Tree-Based Methods
    • Boosting
    • Support Vector Machines (SVMs)
  • CHAPTER 3 - UNSUPERVISED LEARNING
    • Principal Component Analysis
      • How Does It Work?
      • Interpretation of PCA result
      • Applications in Transportation
    • CLUSTERING
      • K-MEANS
      • SPECTRAL CLUSTERING
      • Hierarchical Clustering
    • REFERENCE
  • CHAPTER 4 - NEURAL NETWORK
    • The Basic Paradigm: Multilayer Perceptron
    • Regression and Classification Problems with Neural Networks
    • Advanced Topologies
      • Modular Network
      • Coactive Neuro–Fuzzy Inference System
      • Recurrent Neural Networks
      • Jordan-Elman Network
      • Time-Lagged Feed-Forward Network
      • Deep Neural Networks
  • CHAPTER 5 - DEEP LEARNING
    • Convolutional Neural Networks
      • Introduction
      • Convolution Operation
      • Typical Layer Structure
      • Parameters and Hyperparameters
      • Summary of Key Features
      • Training of CNN
      • Transfer Learning
    • Recurrent Neural Networks
      • Introduction
      • Long Short-Term Memory Neural Network
      • Application in transportation
    • Recent Development
      • AlexNet, ZFNet, VggNet, and GoogLeNet
      • ResNet
      • U-Net: Full Convolutional Network
      • R-CNN, Fast R-CNN, and Faster R-CNN
      • Mask R-CNN
      • SSD and YOLO
      • RetinaNet
      • MobileNets
      • Deformable Convolution Networks
      • CenterNet
      • Exemplar Applications in Transportation
    • Reference
  • CHAPTER 6 - REINFORCEMENT LEARNING
    • Introduction
    • Reinforcement Learning Algorithms
    • Model-free v.s. Model-based Reinforcement Learning
    • Applications of Reinforcement Learning to Transportation and Traffic Engineering
    • REFERENCE
  • CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
    • Data Pipeline for Machine Learning
      • Introduction
      • Problem Definition
      • Data Ingestion
      • Data Preparation
      • Data Segregation
      • Model Training
      • Model Deployment
      • Performance Monitoring
    • Implementation Tools: The Machine Learning Ecosystem
      • Machine Learning Framework
      • Data Ingestion tools
      • Databases
      • Programming Languages
      • Visualization Tools
    • Cloud Computing
      • Types and Services
    • High-Performance Computing
      • Deployment on-premise vs on-cloud
      • Case Study: Data-driven approach for the implementation of Variable Speed Limit
      • Conclusion
  • CHAPTER 8 - RESOURCES
    • Mathematics and Statistics
    • Programming, languages, and software
    • Machine learning environments
    • Tools of the Trade
    • Online Learning Sites
    • Key Math Concepts
  • REFERENCES
  • IMPROVEMENT BACKLOG
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  1. CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING

Types of Machine Learning

PreviousWhat is Machine Learning?NextSupervised Learning

Last updated 1 year ago

There are various ways to classify machine learning algorithms. The most common classification of machine learning algorithms is based on their learning tasks and the problem(s) that they are solving. Figure XX shows the differences between various machine learning algorithms. In general, there are four types of machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforced learning. The following discusses each type of ML algorithm.

Figure XX – Machine learning types (source:)

Machine learning is usually divided into the following types:

  • Supervised learning aims to predict the value of an output from input measures given a set of input-output pairs. Put in more familiar terms, it is learning to map from inputs x to outputs y, given a set of input-output pairs (x, y). In order for this to be feasible, the data must be labeled: i.e., the input data (x) used to train the computer must have an outcome variable y for each case.

  • Unsupervised learning is applied when the data are unlabeled. In this case, we only have input data x and the goal is to find some interesting or underlying patterns in the data by which they may be grouped or clustered. Because labeling data is typically expensive to do, most data are unlabeled. Hence, unsupervised learning is highly important.

  • Reinforcement learning approach is used to learn how to act or behave in an environment where the only information available in the training process is “success” or “failure” (for example, training a robot to navigate).

  • Supervised learning – which sometimes goes under the rubric predictive analytics – can be further subdivided into areas:

  • Classification. In this case, the y value takes on discrete values, or classes. Mode choice, crash severity, vehicle identification are common examples of classification tasks in transportation.

  • Regression. Here, the y value can take on a continuous range of values, for example, traffic flow or travel time.

https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861