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Machine Learning

  • Understanding and building methods that learn that is methods that leverage data to improve performance on some set of tasks.
  • It is seen as a part of artificial intelligence.

  • ML algorithms build a model based on sample data (training data) in order to make predictions or decisions without being explicitly programmed to do so.

  • A subset of ML is closely related to computational statistics

    • focuses on making predictions using computers
    • but not all ML is statistical learning

  • The study of mathematical optimization delivers methods, theory and application domain to the field of machine learning.

  • Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.

  • Some implementations of ML use data and neural networks in a way that mimics the working of biological brain.

  • In its application across business problems, ML is also referred to as predictive analysis.

Outline

  • Classification
    • Supervised learning
      • Decision tree
      • Ensembles
        • Bagging
        • Boosting
        • Random forest
      • k-NN
      • Linear Regression
      • Naive Bayes
      • Artificial Neural Networks
      • Logistic regression
      • Preceptron
      • Relevance vector machine (RVM)
      • Support vector machine (SVM)
    • Semi-supervised learning
    • Unsupervised learning
      • Clustering
        • BIRCH
        • CURE
        • Hierarchial
        • k-means
        • Fuzzy
        • Expectation-maximization (EM)
        • DBSCAN
        • OPTICS
        • Mean shift
      • Dimensionality reduction
        • Factor analysis
        • CCA
        • ICA
        • LDA
        • NMF
        • PCA
        • PGD
        • t-SNE
    • Reinforcement learning
  • Problems
    • Classification
    • Regression
    • Clustering
    • Dimension reduction
    • Density estimation
    • Anomaly detection
      • k-NN
      • Local outlier factor
      • Isolation forest
    • Data Cleaning
    • AutoML
    • Association rules
    • Structured prediction
    • Feature engineering
    • Feature learning
    • Online learning
    • Reinforcement learning
    • Learning to rank
    • Grammar induction
  • Structured Prediction
    • Graphical models
      • Bayes net
      • Conditional random field
      • Hidden Markov
  • Deep Learning
    • Artificial neural network
      • Autoencoder
      • Cognitive computing
      • Deep learning
      • DeepDream
      • Multilayer perceptron
      • RNN
        • LSTM
        • GRU
        • ESN
        • reservoir computing
      • Restricted Boltzmann machine
      • GAN
      • SOM
      • Convolutional neural network (U-Net)
      • Transformer (Vision)
      • Spiking neural network
      • Memtransistor
      • Electrochemical RAM (ECRAM)

Resources