Some Topics on Probability, Statistics, Learning

Probability

• Coming soon
  

Statistics

• Basic Concentration Inequalities
  

Learning

• Introduction to Supervised Learning
• Linear Methods for Regression
• Linear Methods for Classification
• Maximal Margin Methods for Classification
• Beyond Linearity: Basis Expansions, Local Averaging & Kernel Smoothing
• Kernel Methods: Fundamentals and Special Topics
• Model Assessment & Selection, Generalization Theory
• Tree-Based Methods and Ensembling Methods
• Neural Networks: Fundamentals
• Rethinking Generalization: Modern Phenomena in Overparameterized Models
• Rethinking Reliability: Probabilistic Approaches for Reliable Models
• Unsupervised Learning: Clustering and Mixture Models
• Unsupervised Learning: Dimensionality Reduction and Advanced Topics
• More on Kernel Density Estimation

*These notes are work in progress and will be updated from time to time. Please read them at your own risk.