Alexander P. Christensen
Assistant Professor
Hobbs Hall, Room 221
alexander.christensen@vanderbilt.edu
Office Hours: by email
# CRAN packages
install.packages(
pkgs = c(
"remotes", "reticulate", "tictoc",
"ggplot2", "caret", "glmnet", "GGally",
"MASS", "ggpubr", "keras", "tensorflow",
"ranger", "cluster", "faraway",
"NbClust", "factoextra", "aricode",
"EGAnet", "textdata", "tm", "tidyverse"
)
)
# GitHub packages
remotes::install_github("AlexChristensen/toolchainR")
remotes::install_github("AlexChristensen/latentFactoR")
remotes::install_github("atomashevic/transforEmotion")
Week 1: Introduction and Retrieval-augmented Generation
Week 2: Regression and Classification (with gradient descent)
-
(linear) regression
-
classification (logistic regression)
-
gradient descent
Week 3: Generalizability
-
data splitting (training/testing)
-
bootstrap
-
(k-folds) cross-validation
Week 4: Ridge and LASSO Regularization
-
ridge (
$\ell_2$ -norm) regression -
lasso (
$\ell_1$ -norm) regression
Week 5-6: (Artificial) Neural Networks
-
artificial neural networks
-
activation functions
-
backpropagation
-
training tips and tricks
Week 7-8: Trees and Forests
-
classification and regression trees (CART)
-
bootstrap aggregation (bagging)
-
random forests
Week 9-10:
-
distances
-
$k$ -means/mediods -
hierarchical clustering
Week 11-12: Exploratory Graph Analysis (EGA)
-
(psychometric) networks
-
network estimation
-
community detection
-
exploratory graph analysis
-
unidimensionality
Week 12-13: EGA Framework
-
bootstrap EGA
-
local dependence detection (Unique Variable Analysis)
-
(network) loadings and scores
-
(metric) invariance
-
hierarchical EGA
Week 14: Final Project
Inter-university Consortium for Political and Social Research