Teaching
Across different years and contexts, I have developed courses, notes, slides, code, and teaching material in statistics, machine learning, Bayesian modeling, probability, and inference. This teaching work has been one of the most consistent parts of my professional life.
Current Teaching Themes
Bayesian Statistics
Course notes, slides, complementary material, and code focused on Bayesian thinking, hierarchical models, and practical statistical modeling.
Applied Learning
Machine Learning
Lecture material and code spanning statistical learning, regression, classification, dimensionality reduction, and computational examples.
Foundations
Probability and Inference
Foundational teaching has been a recurring part of my work over the years, especially through these courses and assistantships:
Bayesian Statistics
Syllabus, evaluation and other information:
Syllabus, evaluation and other information. The notes of the course are now online! Bayesian statistics: Course notes
Codes and data for the course are available at the github repository: https://github.com/IrvingGomez/BayesianStatistics
Complementary material:
Entropy, Kullback-Leibler divergence, and information. Cheatsheet of Bayesian models (to be completed by the students). Bayesian Linear Model: Gory Details.
Actuarial Probability
Syllabus, evaluation and other information:
Syllabus, evaluation and other information. Some usefull references:
Notes of Miguel Nakamura and María Guadalupe Russell (Elements of Prob. and Statistics). Notes of Joaquin Ortega-Sánchez (Elements of Prob. and Statistics). Notes of Joaquin Ortega-Sánchez (Stochastic Models I). Sheldon Ross - A first course in probability. Presentation:
Machine Learning
Syllabus, evaluation and other information:
Syllabus, evaluation and other information. Some usefull references:
Hastie, Tibshirani, Friedman - Elements of Statistical Learning. Presentations:
Statistical and Machine Learning Linear Regression Collinearity Ridge Regression Cross Validation Maximum Likelihood with Restrictions B-Splines EM algorithm, Gaussian mixture and k-means Codes:
Statistical Models 2021
Some usefull references:
Alvin C. Rencher, G. Bruce Schaalje - Linear Models in Statistics (2008) Notes of Rogelio Ramos Quiroga (2011) Interactive Linear Algebra (2019) (to remember concepts that you might have forgotten) Presentation:
Presentation, part 1. Many of the examples were given to me by Rogelio Ramos Quiroga , in many of them I extended explanation and analysis.
Statistical Inference 2019
A reference about distributions: Johnson, Kotz and Balakrishnan - Continuous Univariate Distributions Vol. 1 and 2
August 23 Brief intro to R (how to plot) Brief intro to Julia (how to plot) September 3 ECDF with confidence bands with Python Histograms, ECDF and QQ plots with Julia September 13 Normal inference through maximum likelihood October 3 The negative binomial distribution October 11 Inference of normal distribution through maximum likelihood I added some ideas of Eloisa Diaz-Frances, such as symmetric reparametrization.
Statistical Models 2019
Some useful references:
Alvin C. Rencher, G. Bruce Schaalje - Linear Models in Statistics (2008) Notes of Rogelio Ramos Quiroga (2011) Many of the examples were given to me by Rogelio Ramos Quiroga, and in many of them I extended the explanation and analysis.
I have tried to keep the original references inside the code.
February 15 Cubic Splines with Julia Cubic Splines with R March 1 Usual Regression with R April 5 & 12 Bayesian Regression (Article of Sudipto Banerjee) May 10 Logistic Regression (Coronary) Logistic Regression (British Crime) Many datasets for machine learning can be found at the UCI repository.