Teaching

Office hours: Tuesdays. 14:00-15:00 Location: C.121b

Current (Spring 2024/5)

Past

  • Fall 2024/5:

    • Advanced statistical analysis and model-based learning (formerly ‘Advanced Statistics for Data Science’) (CS 3676)

  • Spring 2023/4:

    • Machine Learning from Data (CS 3141)

    • Seminar in Large language Models and Information Theory (CS 3968)

  • Fall 2023/4:

    • Introduction to Information Theory (CS 3798)

  • Spring 2022/3:

    • Machine Learning from Data (CS 3141)

    • Advanced statistics for data science (CS 3676)

  • Fall 2022/3:

    • Information-theoretical analysis of neural language models (CS 3890)

  • Spring 2021/2:

    • Advanced statistics for data science (CS 3676)

Spring 2021/2: (Stanford STATS 285) Massive computational experiments, painlessly

  • with David Donoho and Masha Lofti

Fall 2020/1: (Stanford STATS 207) Introduction to time-series analysis (Fall 2020-2021)

Lecture 1 course outline, examples of time series data, models for time series data
Lecture 2 sample autocorrelation and basic theoretical constructs
Lecture 3 time series regression
Lecture 4 trend models and Data Wrangling
Lecture 5-6 ARMA/ARIMA Modeling I
Lecture 7 ARMA/ARIMA Modeling II
Lecture 8 ARIMA/SARIMA
Lecture 9 regression with autocorrelated errors and lagged regression
Lecture 10 efficient markets hypothesis and GARCH (guest lecture by David Donoho)
Lecture 11 spectral Analysis I
Lecture 12 spectral Analysis II
Lecture 13 spectral regression and principal components
Lecture 14 state-space modeling and the Kalman Filter
Lecture 15 estimation of state-space models
Lecture 16 dynamic linear models with switching
Lecture 17 stochastic volatility, Bayesian analysis of state-space models
Lecture 18 bootstrap reality check and technical trading rules (guest lecture by David Donoho)
Lecture 19 prophet (guest lecture by Sean Taylor)
Lecture 20 high-dimensional data, DeepAR, VEST