Hypothesis Space Loss Functions Risk and Empirical Risk Learning Problem KL Divergence - Kullback-Leibler Divergence Minimizing KL Divergence is Equivalent to Maximizing Likelihood Bayes Classifier Logistic regression also known as logit regression or binary classification Softmax regression also known multiclass regression Expectation Maximization (EM) Algorithm Linear Regression Generalized Linear Regression Non-parametric Density Estimation Parzen Window Estimate K-Nearest Neighbour (KNN) Linear Discriminant Analysis (LDA) Maximum A Posteriori Estimate (MAP) Bias variance tradeoff Regularization Support Vector Machine (SVM) - Linearly Separable Data Support Vector Machine (SVM) - Non-linearly Separable Data SVM with Kernel Support measure machine (SCM) as Empirical Risk Minimization (ERM) Neural Networks Backpropagation Decision Trees Ensemble Learning Bagging and Random Forest Boosting XGBoost Adaboost Unsupervised learning Mixture model clustering also known as soft clustering Principal Component Analysis (PCA) K-means Clustering Miscellaneous Machine Learning Terms