Statistical Learning by Stanford University
offered by Stanford University

Private University
8180 acre campus
Estd. 1885

Statistical Learning by Stanford University
at
Stanford University
Overview

Develop problem-solving skills and critical thinking abilities by analyzing data, formulating research questions, choosing appropriate statistical methods, and interpreting results in context

Mode of learning	Online
Official Website	Go to Website
Course Level	UG Certificate

Statistical Learning by Stanford University
at
Stanford University
Highlights

Earn a certificate from Stanford University
Learn from industry experts

Statistical Learning by Stanford University
at
Stanford University
Course details

Skills you will learn

Regression Bootstrap Software Testing Data Science Statistics Data analysis

Who should do this course?

For individuals who want to enhance their knowledge & skills in the field

What are the course deliverables?

Learn to effectively handle data preprocessing, identifying and addressing missing values, outliers, and inconsistencies
Learn about linear regression, logistic regression, generalized linear models, and other core models used for prediction and analysis
Utilize charts, graphs, and other visual tools to communicate findings effectively
Analyze datasets from various domains like finance, healthcare, or social sciences, drawing meaningful conclusions and interpretations
Learn to approach statistical problems analytically, formulating hypotheses, choosing appropriate models, and interpreting results objectively

More about this course

This is an introductory-level course in supervised learning, with a focus on regression and classification methods
The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical)

Statistical Learning by Stanford University
at
Stanford University
Curriculum

Overview of statistical learning

Linear regression

Classification

Resampling methods

Linear model selection and regularization

Moving beyond linearity

Tree-based methods

Support vector machines

Deep learning

Survival modeling

Unsupervised learning

Multiple testing

Statistical Learning by Stanford University
at
Stanford University
Faculty details

Trevor Hastie

Trevor Hastie is a professor of statistics at Stanford University. His main research contributions have been in the field of applied nonparametric regression and classification, and he has written two books in this area: "Generalized Additive Models" (with R. Tibshirani, Chapman and Hall, 1991), and "Elements of Statistical Learning" (with R. Tibshirani and J. Friedman, Springer 2001). He has also made contributions in statistical computing, co-editing (with J. Chambers) a large software library on modeling tools in the S-plus language ("Statistical Models in S", Wadsworth, 1992). His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems.

Robert Tibshirani

Robert Tibshirani's main interests are in applied statistics, biostatistics and data mining. His current research focuses on problems in biology and genomics, medicine and industry. More specifically, Tibshirani specializes in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis. He is co-author of the books Generalized Additive Models (with T. Hastie), An Introduction to the Bootstrap (with B. Efron), and Elements of Statistical Learning (with T. Hastie and J. Friedman). With collaborator Balasubramanian Narasimhan, Tibshirani also develops software packages for genomic and proteomics.