1. Discriminant Functions: When you just need accurate classifications and don’t care about probabilities (e.g., image classification for labeling)
   • 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)
   • 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)
   • Naïve Bayes, Gaussian Discriminant Analysis (GDA), Hidden Markov Models (HMM), Variational Autoencoders (VAE)
4. Regression Problems - Predicting continuous values (house prices, temperature, stock prices)
   • Examples: Linear regression, regression trees, neural networks for regression
5. Unsupervised Learning - No labels provided
   • Clustering: k-means, hierarchical clustering, DBSCAN
   • Dimensionality reduction: PCA, t-SNE, autoencoders
   • Density estimation: Finding p(x) without caring about classes
6. Reinforcement Learning - Learning through trial and error with rewards
   • Q-learning, policy gradients, AlphaGo
7. Other Tasks:
   • Anomaly detection
   • Recommendation systems
   • Time series forecasting
   • Ranking problems
   • Semi-supervised learning
   • Transfer learning
   • Ensemble methods combine multiple approaches