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Machine Learning Domains

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  1. Classification Problems (Supervised Learning)
    1. Discriminant Functions: When you just need accurate classifications and don’t care about probabilities (e.g., image classification for labeling). Example algorithms: Perceptron, Support Vector Machines (SVM), Decision Trees
    2. Discriminative Models: When you need probability estimates for uncertainty or decision-making (e.g., medical diagnosis, ranking). Example algorithms: Logistic Regression, Neural Networks, Conditional Random Fields (CRF)
    3. Generative Models: When you need to generate samples, handle missing data, or have strong prior knowledge about data distribution (e.g., anomaly detection, small datasets). Example algorithms: Naïve Bayes, Gaussian Discriminant Analysis (GDA), Hidden Markov Models (HMM), Variational Autoencoders (VAE)
  2. Regression Problems (Supervised Learning) - Predicting continuous values (house prices, temperature, stock prices). Example algorithms: Linear regression, regression trees, neural networks for regression
  3. Unsupervised Learning - No labels provided
    • Clustering: k-means, hierarchical clustering, DBSCAN
    • Dimensionality reduction: PCA, t-SNE, auto-encoders, manifold learning
    • Density estimation: Finding p(x) without caring about classes
  4. Other Common Tasks:
    • Reinforcement Learning: learning through trial and error with rewards
    • Q-learning, policy gradients, AlphaGo
    • Anomaly detection
    • Recommendation systems
    • Time series forecasting
    • Ranking problems
    • Semi-supervised learning
    • Transfer learning
    • Ensemble methods combine multiple approaches

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