---
title: "Welcome"
author: "Prof. Eric A. Suess"
date: "1/21/2026"
format: 
  revealjs:
    embed-resources: true
---

## Welcome to Stat. 652 Statistical Learning

This course will be about Data Science and the use of Statistical Learning/Machine Learning/Artificial Intelligence to analyze data.

Our focus will be on using the setup of Machine Learning with *training data*, *test data*, and *validation data*.

For *classification problems* we will discuss *accuracy* using the *confusion matrix*.

## Welcome to Stat. 652 Statistical Learning

We will also be using modern R packages to fit the models we will be learning about. 

We will be using traditional R packages and the modern [tidyverse](https://www.tidyverse.org/) and [tidymodels](https://www.tidymodels.org/).

We will be using [h2O.ai](https://www.h2o.ai/) and discuss [h2O Driverless AI](https://www.h2o.ai/products/h2o-driverless-ai/).

We will be using [Tensorflow for R](https://tensorflow.rstudio.com/) and [keras for R](https://tensorflow.rstudio.com/keras/).

We may discuss [Spark for R](https://spark.rstudio.com/).

## Welcome to Stat. 652 Statistical Learning

If you are interested in using Python

You can use modern Python packages to fit the models we will be learning about. 

You can use Python packages and the [scikit-learn](https://scikit-learn.org/stable/).

You can use [keras3](https://keras.io/) and [Tensorflow](https://www.tensorflow.org/) or [pytorch](https://pytorch.org/).

## Statistical Topics

- Sampling from a population
- Sample statistics
- Sampling distributions
- Bootstrapping
- Outliers
- Linear Regression - Prediction
- Confounding Variables
- Logistic Regression - Classification
- Problems with p-values

## Statistical Learning, Machine Learning, Artificial Intelligence, and Predictive Analytics

- Supervised Learning
- Unsupervised Learning
- Ensemble Models

## Supervised Learning

**Classifiers and Regression/Prediction**

- k-Nearest Neighbors (kNN)
- Naive Bayes
- Decision Trees
- Boosting
- Bagging
- Random Forests
- Artificial Neural Networks (ANN)
- Deep Learning
- Ensemble Methods
- Forecasting

## Evaluation Models

- Training, Validation, Testing Data
- Cross-validation
- Confusion Matrix
- ROC curves
- Bias-variance Trade-off
- Regularization

## Unsupervised Learning

**Clustering**

- Hierarchical Clustering
- k-Means
- DBSCAN

**Dimension Reduction**

- Singular Value Decomposition (SVD)
- Principal Component Analysis (PCA)
- Factor Analysis
- Multidimensional Scaling (MDS)

## Simulation

- Simulating variability

## References

There are many many excellent references that will be useful for this class.

There are some references provided on the syllabus.

There will be many links given on the website.

My current favorite podcast about data science is [DataFramed](https://www.datacamp.com/community/podcast).












