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 / General

General ML Topics

Foundational machine learning concepts, algorithms, and techniques

01 General ML Notes →
02 Basic Statistics →
03 Decision Trees →
04 Boosting →
05 XGBoost →
06 Clustering →
07 Support Vector Machines →
08 Dimensionality Reduction →
09 Regression →