Artificial Intelligence (AI) & Machine Learning (ML) Algorithms Index

Artificial Intelligence (AI) & Machine Learning (ML) Algorithms Index
Artificial Intelligence (AI) & Machine Learning (ML) algorithms

List of AI & ML Algorithms as a quick reference

  1. Deep Learning Algorithms
  2. Reinforcement Learning Algorithms
  3. Supervised Learning Algorithms
  4. Unsupervised Learning Algorithms
  5. Semi-Supervised Learning Algorithms

Deep Learning Algorithms | Read

  1. Neural Networks  Backpropagation algorithm | Read
  2. Feedforward Neural Networks (FNN) | Read
  3. Convolutional Neural Networks (CNN) | Read
  4. Recurrent Neural Networks (RNN) | Read
  5. Recursive Neural Network | Read
  6. AutoEncoders | Read
  7. Deep Belief Networks | Read
  8. Restricted Boltzmann Machines | Read
  9. Generative Adversarial Networks (GAN) | Read | Read
  10. Transformers | Read
  11. Graph Neural Networks | Read
  12. Deep Learning in Natural Language Processing (NLP)
    1. Sequence Modeling | Read
    2. Word Embeddings | Read
  13. Deep Learning in Computer Vision
    1. Localization and Object Detection | Read
    2. Single-shot detectors | Read
    3. Semantic Segmentation | Read
    4. Pose Estimation | Read

Reinforcement Learning Algorithms | Read

  1. Deterministic policy gradient
  2. Learning automata
  3. Proximal policy optimization
  4. Q-learning
  5. Soft actor-critic
  6. State–action–reward–state–action
  7. Temporal difference learning
  8. Trust region policy Optimization

Supervised Learning Algorithms | Read

  1. ANOVA
  2. Averaged one-dependence estimators
  3. Artificial neural network
    1. Convolutional neural network
    2. Extreme learning machine
    3. Feedforward neural network
    4. Logic learning machine
    5. Long short-term memory
    6. Recurrent neural network
    7. Self-organizing map
  4. Bayesian networks
  5. Boosting
  6. Case-based reasoning
  7. Conditional random field
  8. Decision tree algorithms
    1. C4.5 algorithm
    2. C5.0 algorithm
    3. Chi-squared automatic interaction detection
    4. Classification and regression tree
    5. Conditional decision tree
    6. Decision stump
    7. Decision tree
    8. ID3 algorithm
    9. Iterative dichotomiser 3
    10. Random forest
    11. SLIQ
  9. Ensembles of classifiers
    1. Bootstrap aggregating
    2. Boosting
  10. Gaussian process regression
  11. Gene expression programming
  12. Group method of data handling
  13. Inductive logic programming
  14. Information fuzzy networks
  15. Instance-based learning
  16. K-nearest neighbour
  17. Lazy learning
  18. Learning vector quantization
  19. Linear
    1. Elastic-net
    2. Lasso
    3. Linear discriminant analysis
    4. Linear regression
    5. Logistic regression
    6. Multinomial logistic regression
    7. Naive bayes classifier
    8. Ordinary least squares
    9. Passive aggressive algorithms
    10. Perceptron
    11. Polynomial regression
    12. Ridge regression / classification
    13. Support vector machine
  20. Logistic model tree
  21. Minimum message length
    1. Analogical modelling
    2. Nearest neighbour algorithm
  22. Ordinal classification
  23. Probably approximately correct learning
  24. Quadratic classifiers
  25. Random forests
  26. Ripple down rules
  27. Symbolic machine learning

Unsupervised Learning Algorithms | Read

  1. Association rule learning
    1. Apriori algorithm
    2. Eclat algorithm
    3. FP-growth algorithm
  2. Auto-encoders
  3. Cluster analysis
    1. BIRCH
    2. Conceptual clustering
    3. DBSCAN
    4. Expectation-maximization
    5. Fuzzy clustering
    6. Hierarchical clustering
    7. K-means clustering
    8. K-medians
    9. Mean-shift
    10. OPTICS algorithm
    11. Single-linkage clustering
  4. Dimensionality reduction
    1. Canonical correlation analysis
    2. Dynamic mode decomposition
    3. Factor analysis
    4. Feature extraction
    5. Feature selection
    6. Independent component analysis
    7. Linear discriminant analysis
    8. Multidimensional scaling
    9. Non-negative matrix factorization
    10. Partial least squares regression
    11. Principal component analysis
    12. Principal component regression
    13. Projection pursuit
    14. Sammon mapping
    15. T-distributed stochastic neighbour embedding
  5. Expectation-maximization algorithm
  6. Generative topographic map
  7. Information bottleneck method
  8. Manifold learning
  9. Vector quantization

Semi-Supervised Learning Algorithms | Read

  1. Active learning
  2. Co-training
  3. Graph-based methods
  4. Generative models
  5. Low-density separation
  6. Transduction

Note: There are some overlaps so I will have to refactor this list again and make any required corrections whenever I have some free time.

Read more