ML Notes

📊 ESLR

ESLR Notes
Linear Regression
Classification
Kernel Methods
Model Assessment and Selection
Model Inference and Averaging
Additive Models, Trees, and Related Methods
Boosting and Additive Trees
Random Forests

🧠 General

General ML Notes
Basic Statistics
Decision Trees
Boosting
XGBoost
Clustering
Support Vector Machines
Dimensionality Reduction
Regression

💬 Jurafsky

Speech and Language Processing Notes
Regular Expressions and Text Processing
N-Grams and Language Models
Vector Semantics and Word Embeddings
Sequence Architectures: RNNs, LSTMs, and Attention
Encoder-Decoder Models
Transfer Learning and Pre-trained Models

📈 ProbML

Probabilistic Machine Learning Notes
Introduction to Machine Learning
Probability Foundations
Probability: Advanced Topics
Statistics
Decision Theory
Information Theory
Optimization
Discriminant Analysis
Logistic Regression
Linear Regression
Feed-Forward Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks and Transformers
Exemplar-Based Methods
Decision Trees and Ensembles
Self-Supervised and Semi-Supervised Learning
Recommendation Systems

ML Notes

Home / ESLR

Elements of Statistical Learning

Notes from the classic textbook on statistical learning theory and methods

01 ESLR Notes →
02 Linear Regression →
03 Classification →
04 Kernel Methods →
05 Model Assessment and Selection →
06 Model Inference and Averaging →
07 Additive Models, Trees, and Related Methods →
08 Boosting and Additive Trees →
09 Random Forests →