Course Logistics
Syllabus
STAT 253: Statistical Machine Learning
Welcome!
Course Logistics
Schedule
Learning Goals
Syllabus
Overview (Unit 0)
Getting Started
1
Introductions
Regression: Model Evaluation (Unit 1)
Motivating Question
2
Model Evaluation
3
Overfitting
4
Cross-Validation
Regression: Model Selection (Unit 2)
Motivating Question
5
Variable Selection
6
LASSO: Shrinkage / Regularization
Regression: Flexible Models (Unit 3)
Motivating Question
7
Nonparametric Models
8
KNN Regression and the Bias-Variance Tradeoff
9
LOESS & Splines
Regression: Review
10
Regression Review
Classification: Model Building (Unit 4)
Motivating Question
11
Logistic Regression
12
Evaluating Classification Models
Classification: Building Flexible Models (Unit 5)
13
KNN and Trees
14
More KNN and Trees
15
Random forests & bagging
Classification: Review
16
Classification Review
Unsupervised Learning: Clustering (Unit 6)
Motivating Question
17
Hierarchical Clustering
18
K-Means Clustering
Unsupervised Learning: Dimension Reduction (Unit 7)
19
Principal Component Analysis
20
Principal Component Regression
Unsupervised Learning: Review
21
Unsupervised Learning Review
Synthesis
22
Learning New Algorithms
Appendices
STAT 155 Review
R and RStudio Setup
R Resources
Course Logistics
Syllabus
Syllabus
Google Slides Version:
[link]
PDF Version: see Moodle!
Learning Goals
Getting Started