Machine Learning - Comparison of Different Inference Methods
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Linear Regression
Simple Linear Regression
Multiple Linear Regression
Problems when using Linear Regression
Classification
Logistic Regression
Linear Discriminant Analysis (LDA)
Naive Bayes Classifier
Comparison of Classifiers
Classfication Metrics
Linear Model Selection and Regularization
Subset Selection
Evaluation Metrics
Shrinkage Methods and Regularization
Dimension Reduction Methods
Curse of Dimensionality
Exercises
Moving Beyond Linearity
Polynomial Regression
Step Function
Basis Function
Regression Splines (Polynomials)
Smoothing Splines
Local Regression
Generalized Additive Models
Exercises
Resampling Methods
Cross Validation
Bootstrap
Tree Based Models
Regression Trees
Classification Trees
Patient Rule Induction Method (PRIM)
Bagging
Random Forest
Boosting
Adaboost.M1
Boosting Trees
XGBoost
Support Vector Machines (SVM)
Maximum Margin Classifier
Support Vector Classifier
Support Vector Machine
Bayesian Methods
Maximum Likelihood Estimator (MLE)
Maximum A Priori (MAP)
Expectation Maximization
Variational Inference
Comparison of Different Inference Methods
Latent Dirichlet Allocation (LDA)
Sampling from Distributions
Gaussian Process (GP)
Bayesian Optimization
Clustering
Distances and Dissimilarity Measures
K-Means Clustering
K-Mediods Clustering
Hierarchical Clustering
Gaussian Mixture Models (GMM)
Key Considerations while Clustering
Appendix
Bias-Variance Tradeoff
Matrix Calculus in Logistic Regression
Comparison of Classifiers
Perpendicular distance in Maximum Margin Classifier
Lagrange Multiplier, Primal and Dual
Codes
Predictions using Gaussian Process
Comparison of different inference methods in terms of accuracy and inference speed
Comparison of different inference methods in terms of accuracy and inference speed