Teaching

Texas A&M University

STAT 211: Principles of Statistics I

Introduction to probability and statistics covering basic probability, Bayes rule, parametric distributions, Hypothesis testing, and ANOVA.

STAT 438: Bayesian Statistics

Bayesian modeling and computation covering prior selection, conjugate models, Bayesian hierarchical modeling, Markov Chain Monte Carlo (MCMC), variational inference, and introductory Bayesian nonparametrics.

STAT 335: Principles of Data Science

Introduction to basic Data Science workflows in Python covering various regression, classification, clustering algorithms and tools to evaluate model output including metrics, plotting, EDA, and Statistical learning considersations.

STAT 421: Machine Learning

Covers classical machine learning algorithms and modern deep learning algorithms such as MLPs, CNNs, LSTMS with a focus on implementation, optimization, and applications to real data using Python.

STAT 600: Computational Statistics and reproducible computations

Derivation and implementation of classic statistical compution algorithms including k-Means, multi-target logistic regression, and LASSO with a focus optimized and reproducible code.

University of Illinios at Urbana-Champaign

STAT 400: Statistics and Probability 1

ACCY 571: Data Analytics Foundations for Accountancy