My Notes on Statistics and Machine Learning Books, Courses, Papers, and More

Book Notes

Introduction to Time Series and Forecasting, by Brockwell and Davis [2016]

Flexible Imputation of Missing Data, by Van Buuren [2018]

Statistical Analysis with Missing Data, by Little and Rubin [2019]

Generalized Additive Models: An Introduction with R, by Wood [2017]

Pattern Recognition and Machine Learning, by Bishop [2006]

Neural Network Methods for Natural Language Processing, by Goldberg [2017]

Gaussian Processes for Machine learning, by Rasmussen and Williams [2006]

Computer Age Statistical Inference, by Efron and Hastie [2016]

Course Notes

A Crash Course on Causality, by Roy, on Coursera

Paper notes

Software notes

My Teaching Slides

  • This Github repo contains the slides of the courses I used to teach, including

    • Introduction to Bayesian Statistics (grad)
    • Introduction to Statistical Learning (undergrad & grad)
    • Statistics 101 (undergrad)
    • Probability (undergrad)