Chapter 1: Introduction

Statistical Learning

Statistical learning involves machine learning, focusing on both supervised and unsupervised problems. It combines aspects of statistics and computer science, emphasizing models, interpretability, precision, and uncertainty, while machine learning emphasizes large-scale applications and prediction accuracy.

Statistical Learning Problems

Examples include identifying risk factors for prostate cancer, classifying phonemes, predicting heart attacks, and customizing email spam detection.

Supervised Learning

A learning approach where the goal is to predict an outcome measurement (response or dependent variable) based on a vector of predictor measurements (inputs, independent variables). The response variable can be quantitative (regression) or categorical (classification).

Unsupervised Learning

A learning approach that involves only a set of predictors without an outcome variable, aiming to discover patterns such as similar groups of samples or features with the most variation.

Real-World Applications of Statistical Learning

Includes improving Google’s search engine, IBM’s Watson answering questions, and the Netflix Prize competition.

Statistical Learning vs. Machine Learning

Statistical learning arose from statistics, while machine learning developed as a subfield of artificial intelligence. Both address supervised and unsupervised problems but differ in emphasis.

Course Overview on Statistical Learning

Covers material from An Introduction to Statistical Learning with Applications in R (ISLR) with examples and R labs, also referencing The Elements of Statistical Learning (ESL) for more mathematical depth.

Skills and Philosophy of Statistical Learning

Aims to impart an understanding of statistical learning techniques, the importance of assessing method performance, and its applications in science, industry, and finance.

"Sexy" Job of a Statistician

Statistician is recognized as a highly in-demand profession.