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

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.