There is one session available:
Statistical Learning with R
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. We also offer a separate version of the course called Statistical Learning with Python – the chapter lectures are the same, but the lab lectures and computing are done using Python.
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
- Language: English
- Video Transcript: English
- Associated skills: Data Analysis, Random Forest Algorithm, Statistical Modeling, Artificial Neural Networks, Unsupervised Learning, Linear Discriminant Analysis, Polynomial Regression, Supervised Learning, Support Vector Machine, Boosting, Statistical Learning Theory, Lasso (Programming Language), Python (Programming Language), Lecturing, Logistic Regression, Data Science, Bootstrap (Front-End Framework), K-Means Clustering, Deep Learning, R (Programming Language), Statistics, Principal Component Analysis
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
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?
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.