Book Notes
Introduction to Time Series and Forecasting, by Brockwell and Davis [2016]
- Ch1 Introduction (pdf)
- Ch2 Stationary Processes (pdf)
- Ch3 ARMA Models (pdf)
- Ch5 ARMA Models Estimation and Forecasting (pdf)
- Ch6 ARIMA Models (pdf)
Flexible Imputation of Missing Data, by Van Buuren [2018]
- Ch1 Introduction (pdf)
- Ch2 Multiple Imputation (pdf)
- Ch3 Univariate Missing Data (pdf)
- Ch4 Multivariate Missing Data (pdf)
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]
- Part1 Supervised Classification and Feed-forward Neural Networks (pdf)
- Part2 Working with Natural Language Data, Ch6-8 (pdf)
- Part2 Working with Natural Language Data, Ch9-11 (pdf)
- Part3 Specialized Architectures, Ch13 CNN (pdf)
- Part3 Specialized Architectures, Ch14-16 RNNs (pdf)
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
- Week 1: Intro to Causal Effects (pdf)
- Week 2: Confounding and Directed Acyclic Graphs (pdf)
- Week 3: Matching and Propensity Scores (pdf)
- Week 4: Inverse Probability of Treatment Weighting (pdf)
- Week 5: Instrumental Variables (pdf)
Paper notes
Software notes
My Teaching Slides
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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)