Welcome to Statistical Learning

Dr. D’Agostino McGowan

👋

Lucy D’Agostino McGowan

  mcgowald@wfu.edu
  Thurs 10a-11a

bit.ly/sta-363-s23

Intros

• Name
• Major
• Fun OR boring fact

Statistical Learning Problems

• Identify risk factors for breast cancer

Statistical Learning Problems

• Identify risk factors for breast cancer
• Customize an email spam detection system

• Data: 4601 labeled emails sent to George who works at HP Labs
• Input features: frequencies of words and punctuation

—	george	you	hp	free	!	edu	remove
spam	0.00	2.26	0.02	0.52	0.51	0.01	0.28
email	2.27	1.27	0.90	0.07	0.11	0.29	0.01

Statistical Learning Problems

• Identify risk factors for breast cancer
  
• Customize an email spam detection system
  
• Identify numbers in handwritten zip code
  

DEMO

Statistical Learning Problems

• Identify risk factors for breast cancer
  
• Customize an email spam detection system
  
• Identify numbers in handwritten zip code
  
• Establish the relationship between variables in population survey data
  

Income survey data for males from the central Atlantic region of US, 2009

Statistical Learning Problems

• Identify risk factors for breast cancer
  
• Customize an email spam detection system
  
• Identify numbers in handwritten zip code
  
• Establish the relationship between variables in population survey data
  
• Classify pixels of an image

Usage \(\in\) {red soil, cotton, vegetation stubble, mixture, gray soil, damp gray soil}

✌️ types of statistical learning

• Supervised Learning
• Unsupervised Learning

Supervised Learning

• outcome variable: \(Y\), (dependent variable, response, target)
• predictors: vector of \(p\) predictors, \(X\), (inputs, regressors, covariates, features, independent variables)
• In the regression problem, \(Y\) is quantitative (e.g price, blood pressure)
• In the classification problem, \(Y\) takes values in a finite, unordered set (survived/died, digit 0-9, cancer class of tissue sample)
• We have training data \((x_1, y_1), \dots, (x_N, y_N)\). These are observations (examples, instances) of these measurements

Supervised Learning

What do you think are some objectives here?

Objectives

• Accurately predict unseen test cases
• Understand which inputs affect the outcome, and how
• Assess the quality of our predictions and inferences

Unsupervised Learning

• No outcome variable, just a set of predictors (features) measured on a set of samples
• objective is more fuzzy – find groups of samples that behave similarly, find features that behave similarly, find linear combinations of features with the most variation
• difficult to know how well your are doing
• different from supervised learning, but can be useful as a pre-processing step for supervised learning

Let’s take a tour - class website

• Concepts introduced:
  • How to find slides
  • How to find assignments
  • How to find RStudio Cloud
  • How to get help
  • How to find policies