Day1 : Introduction

Ch1. Introduction

*슬라이드*

https://lagunita.stanford.edu/c4x/HumanitiesScience/StatLearning/asset/introduction.pdf

*강의*

Opening Remarks and Examples (18:18)

https://www.youtube.com/watch?v=2wLfFB_6SKI&list=PL5-da3qGB5ICcUhueCyu25slvsGp8IDTa

Supervised and Unsupervised Learning (12:12)

https://www.youtube.com/watch?v=LvaTokhYnDw&list=PL5-da3qGB5ICcUhueCyu25slvsGp8IDTa

슬라이드 요약

Statistical Learning Problems

• Identify the risk factors for prostate cancer.
• Classify a recorded phoneme based on a log-periodogram
• Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements
• Customize an email spam detection system.
• Identify the numbers in a handwritten zip code.
• Classify a tissue sample into one of several cancer classes, based on a gene expression profile.
• Establish the relationship between salary and demographic variables in population survey data.
• Classify the pixels in a LANDAST image, by usage.  

The Supervised Learning Problem

• Outcome measurement Y (also called dependent variable, response, target)
• Vector of p predictor measurements X (also called 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 (x1,y1), . . . , (xN,yN). These are observations (examples, instances) of these measurements.

The Unsupervised Learning

• No outcome variable, just a set of predictors (features) measured on a set of samples.
• Objective is more fuzzy(흐린) --- 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.

Statistical Learning Vs Machine Learning

• Machine learning arose as a subfield of A.I.
• Statistical learning arose as a subfield of Statistics.
• There is much overlap - both fields focus on supervised and unsupervised problems:
• Machine learning has a greater emphasis on large scale applications and prediction accuracy.
• Statistical learning emphasizes models and their interpretability, and precision and uncertainty.
• But the distinction has become more and more blurred(희미한), and the is a great deal of "cross-fertilization"(상호수정).
• Machine learning has the upper hand in Marketing!

강의 요약

Opening Remarks and Examples (18:18) - 위의 슬라이드랑 동일 한 내용.

Supervised and Unsupervised Learning (12:12) - 위의 슬라이드랑 동일 한 내용.