Chapter 1

Statistical Learning and Regression

Bias-variance tradeoff and curse of dimensionality

medium • 1 min read

Linear regression, Ridge, and Lasso (L1/L2 regularization)

medium • 2 min read

Logistic regression and generalized linear models

medium • 3 min read

Maximum Likelihood Estimation and MAP

medium • 4 min read

Gradient descent and optimization techniques

medium • 5 min read

Chapter 2

Classification and Support Vector Machines

Decision Trees, Gini index, and Information Gain

medium • 1 min read

K-Nearest Neighbors and naive Bayes classifiers

medium • 2 min read

Linear SVM and margin maximization

medium • 3 min read

The Kernel trick and non-linear SVMs

medium • 4 min read

Evaluation metrics: ROC, AUC, Precision-Recall curves

medium • 5 min read

Chapter 3

Ensemble Methods and Clustering

Bagging, Random Forests, and extra trees

medium • 1 min read

Boosting algorithms (AdaBoost, Gradient Boosting, XGBoost)

medium • 2 min read

K-Means, K-Medoids, and hierarchical clustering

medium • 3 min read

Density-based clustering (DBSCAN)

medium • 4 min read

Gaussian Mixture Models and Expectation-Maximization

medium • 5 min read

Chapter 4

Dimensionality Reduction and Feature Extraction

Principal Component Analysis (PCA) and SVD

medium • 1 min read

Linear Discriminant Analysis (LDA)

medium • 2 min read

t-SNE and UMAP for visualization

medium • 3 min read

Autoencoders and manifold learning

medium • 4 min read

Feature selection techniques (Filter, Wrapper, Embedded)

medium • 5 min read

Chapter 5

Pattern Recognition Principles

Bayesian decision theory and discriminant functions

medium • 1 min read

Hidden Markov Models (HMM) and Viterbi algorithm

medium • 2 min read

Non-parametric techniques and Parzen windows

medium • 3 min read

Syntactic and structural pattern recognition

medium • 4 min read

Applications to speech, image, and text classification

medium • 5 min read