This course is designed for data scientists, machine learning practitioners, and graduate students who want to understand how to evaluate and select models reliably in real-world applications. It is particularly relevant for learners working with predictive models who need to ensure their results generalise beyond the training data.
You’ll learn the statistical foundations behind performance estimation and gain hands-on experience with essential techniques such as cross-validation, model selection, and nested resampling. By the end of the course, you’ll be equipped to design robust evaluation workflows and make confident, evidence-based modeling decisions.
In the first module, the basic concepts of prediction performance evaluation of artificial intelligence based systems on a sample of data are described. It is explained on an intuitive level why and under what conditions the performance evaluation on a sample can be expected to work in the first pl…
In this module, an interpretation of supervised machine learning methods simply as abstract mappings from a sample of data to a predictive hypothesis is presented. As an important special case that covers a surprisingly large portion learning algorithms, we consider methods that select an optimal h…
In this module, resampling techniques for performance evaluation, such as splitting the sample into training and test set parts as well as its averaged variation known as cross-validation, are considered. Moreover, method for model selection, including selection of hyperparameter values, feature su…
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28DIGITAL is in Tier 5: mass-produced or unclear of our institution ranking (40/100).
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
Harvard University
Learn the concepts and techniques that make up the foundation of data science and machine learning.
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 m…
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 m…
Harvard University
Build a foundation in R and learn how to wrangle, analyze, and visualize data.
Harvard University
Learn to use machine learning in Python in this introductory course on artificial intelligence.
