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 / ProbML

Probabilistic Machine Learning

Probability-based approaches to machine learning by Kevin Murphy

  • 01 Probabilistic Machine Learning Notes →
  • 02 Introduction to Machine Learning →
  • 03 Probability Foundations →
  • 04 Probability: Advanced Topics →
  • 05 Statistics →
  • 06 Decision Theory →
  • 07 Information Theory →
  • 08 Optimization →
  • 09 Discriminant Analysis →
  • 10 Logistic Regression →
  • 11 Linear Regression →
  • 12 Feed-Forward Neural Networks →
  • 13 Convolutional Neural Networks →
  • 14 Recurrent Neural Networks and Transformers →
  • 15 Exemplar-Based Methods →
  • 16 Decision Trees and Ensembles →
  • 17 Self-Supervised and Semi-Supervised Learning →
  • 18 Recommendation Systems →