Supplemental Study in Elements of Statistical Learning
STAT 8331
1 hour
Supplemental Study in Elements of Statistical Learning
Analytical Thinking
Course Description
Supplemental study on elements of statistical learning, exploring the material with greater rigor and depth. Problems, background material, and further exploration of statistical learning methods with data examples and simulations.
Athena Title
Suppl Elements Stat Learning
Prerequisite
STAT 6420 and STAT 6421
Corequisite
STAT 8330
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will articulate the strengths and weaknesses of the various methods learned in the course in general and with respect to particular applications.
Students will use simulations to conduct rigorous evaluations of the performance of methods.
Students will summarize the contributions of prominent papers in the primary literature on statistical learning methods and reproduce their results.
Topical Outline
Introduction to Statistical Learning
o Overview of supervised vs. unsupervised learning
o Bias-variance tradeoff and model complexity
o Key concepts in statistical learning theory
Monte Carlo Simulations
o Formulating the assumptions of a simulation.
o Implementing simulations in computer programs.
o Using simulations to assess statistical methods.
Model Assessment and Evaluation
o Cross-validation and resampling methods
o Performance metrics (AUC, F1-score, log-loss)
o Overfitting, regularization, and interpretability
Nonlinear Methods
o Polynomial regression and splines
o Generalized additive models (GAMs)
o Kernel methods and smoothing techniques
o Tree based methods
Dimension Reduction Techniques
o Principal Component Analysis (PCA)
o Lasso
o Manifold learning (t-SNE, UMAP)
Classification Methods
o Logistic regression and discriminant analysis
o Support vector machines (SVM) and kernel methods
o k-Nearest Neighbors (k-NN)
Clustering Methods
o k-Means and hierarchical clustering
o Model-based clustering and Gaussian Mixture Models (GMM)
o Spectral clustering and latent variable models
Ensemble Methods
o Bagging and boosting
o Random forests and gradient boosting machines
o Stacking and blending strategies
Practical Applications
o Applications will be emphasized throughout the course.
o Case studies in healthcare, finance, marketing, and other areas.
o End-to-end model deployment in Python and R
o Ethical considerations and model fairness
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
