There is one session available:
There is one session available:
About this courseSkip 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; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the book website.
At a glance
- Institution: StanfordOnline
- Subject: Data Analysis & Statistics
- Level: Introductory
First courses in statistics, linear algebra, and computing.
- Language: English
- Video Transcript: English
- Associated skills: Lasso (Programming Language), Deep Learning, Supervised Learning, K-Means Clustering, Linear Discriminant Analysis, R (Programming Language), Support Vector Machine, Unsupervised Learning, Statistical Learning Theory, Logistic Regression, Random Forest Algorithm, Bootstrap (Front-End Framework), Statistical Modeling, Polynomial Regression, Boosting, Principal Component Analysis, Data Analysis, Artificial Neural Networks, Lecturing, Data Science
What you'll learnSkip What you'll learn
- Overview of statistical learning
- Linear regression
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Deep learning
- Survival modeling
- Unsupervised learning
- Multiple testing
About the instructors
Frequently Asked QuestionsSkip Frequently Asked Questions
Do I need to buy a textbook?
The book is nice to own, and is available on Amazon and other booksellers.. However a free online version of An Introduction to Statistical Learning, with Applications in R (second edition) by James, Witten, Hastie and Tibshirani (Springer, 2021) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribute printed versions of that pdf file.
Is R and RStudio available for free?
Yes. You get R for free from http://cran.us.r-project.org/. Typically it installs with a click. You get RStudio from http://www.rstudio.com/, also for free, and a similarly easy install.
How many hours of effort are expected per week?
We anticipate it will take approximately 3-5 hours per week to go through the materials and exercises in each section.