1CHAPTER 1 Introduction to Statistical Learning
627.7 Interpretability and Visualizations
21.1 What is Statistical Learning?
637.8 Handling Missing Values and Categorical Features
31.2 Supervised and Unsupervised Learning
64CHAPTER 8 Unsupervised Learning
41.3 Parametric and Non-parametric Models
658.1 Principal Component Analysis (PCA)
51.4 Bias-Variance Tradeoff
668.2 Clustering Algorithms (K-Means, Hierarchical, DBSCAN)
61.5 Overfitting and Regularization
678.3 Dimensionality Reduction (t-SNE, UMAP)
71.6 Evaluation Metrics
688.4 Anomaly Detection
81.7 The Data Science Process
698.5 Association Rule Mining
9CHAPTER 2 Linear Regression
708.6 Matrix Factorization (SVD, NMF)
102.1 Simple Linear Regression
718.7 Gaussian Mixture Models
112.2 Multiple Linear Regression
728.8 Manifold Learning
122.3 Ordinary Least Squares (OLS) Estimation
73CHAPTER 9 Neural Networks and Deep Learning
132.4 Assumptions of Linear Regression
749.1 Artificial Neurons and Activation Functions
142.5 Interpreting Regression Coefficients
759.2 Feedforward Neural Networks
152.6 Residual Analysis
769.3 Backpropagation Algorithm
162.7 Ridge Regression and Lasso
779.4 Regularization Techniques (Dropout, L1/L2 Regularization)
172.8 Polynomial Regression
789.5 Convolutional Neural Networks (CNNs)
182.9 Logistic Regression
799.6 Recurrent Neural Networks (RNNs)
19CHAPTER 3 Classification
809.7 Long Short-Term Memory (LSTMs)
203.1 Logistic Regression
819.8 Generative Adversarial Networks (GANs)
213.2 Linear Discriminant Analysis (LDA)
829.9 Transfer Learning and Fine-Tuning
223.3 Quadratic Discriminant Analysis (QDA)
83CHAPTER 10 Time Series Analysis
233.4 Naive Bayes Classifier
8410.1 Stationarity and Nonstationarity
243.5 k-Nearest Neighbors (kNN)
8510.2 Autocorrelation and Partial Autocorrelation
253.6 Support Vector Machines (SVMs)
8610.3 ARIMA Models
263.7 Decision Trees
8710.4 Exponential Smoothing Methods
273.8 Ensemble Methods (Bagging, Boosting)
8810.5 Seasonal Decomposition
283.9 Evaluating Classification Models
8910.6 Forecasting Evaluation Metrics
29CHAPTER 4 Model Selection and Regularization
9010.7 State-Space Models
304.1 Bias-Variance Tradeoff
9110.8 Multivariate Time Series
314.2 Cross-Validation
92CHAPTER 11 Bayesian Methods
324.3 Information Criteria (AIC, BIC)
9311.1 Bayes’ Theorem
334.4 Regularization Techniques (Ridge, Lasso, Elastic Net)
9411.2 Prior and Posterior Distributions
344.5 Subset Selection Methods
9511.3 Conjugate Priors
354.6 Shrinkage Methods
9611.4 Markov Chain Monte Carlo (MCMC)
364.7 Dimensionality Reduction Techniques
9711.5 Gibbs Sampling
374.8 Feature Selection Algorithms
9811.6 Metropolis-Hastings Algorithm
38CHAPTER 5 Resampling Methods
9911.7 Bayesian Linear Regression
395.1 Bootstrapping
10011.8 Bayesian Classification
405.2 Cross-Validation
10111.9 Bayesian Networks
415.3 Jackknife
102CHAPTER 12 Survival Analysis
425.4 Permutation Tests
10312.1 Censoring and Truncation
435.5 Bootstrap Confidence Intervals
10412.2 Kaplan-Meier Estimator
445.6 Bias Correction and Acceleration
10512.3 Log-Rank Test
455.7 Out-of-Bag Estimation
10612.4 Cox Proportional Hazards Model
46CHAPTER 6 Kernel Methods
10712.5 Accelerated Failure Time Models
476.1 Kernel Functions
10812.6 Competing Risks
486.2 Support Vector Machines (SVMs)
10912.7 Dynamic Prediction
496.3 Kernel Principal Component Analysis (KPCA)
11012.8 Joint Modeling of Longitudinal and Time-to-Event Data
506.4 Gaussian Processes
111CHAPTER 13 Causal Inference
516.5 Kernel Density Estimation
11213.1 Potential Outcomes and Causal Effects
526.6 Kernel Regression
11313.2 Randomized Controlled Trials
536.7 Reproducing Kernel Hilbert Spaces (RKHS)
11413.3 Observational Studies and Confounding
546.8 Kernel Methods for Structured Data
11513.4 Propensity Score Methods
55CHAPTER 7 Tree-Based Methods
11613.5 Instrumental Variables
567.1 Decision Trees
11713.6 Difference-in-Differences
577.2 Bagging and Random Forests
11813.7 Regression Discontinuity Design
587.3 Boosting (AdaBoost, Gradient Boosting)
11913.8 Mediation Analysis
597.4 Regression Trees
12013.9 Dynamic Treatment Regimes
607.5 Classification Trees
121Glossary
617.6 Variable Importance Measures
122Index
