Introduction to Statistical Learning (Data Mining I) - (2 day course)
Event box
A 2 full day course: 22nd - 23rd January 2018
This course introduces the concepts of statistical learning focusing on predictive models.
It covers modern statistical modelling methods, including logistic regression, multinomial regression, and classification and regression trees, as well as assessment methods such as prediction error, deviance and AUC. The focus will be on validation and cross-validation methods, to ensure that selected models are not overfitting the data. Real life examples are used, and there is time for practical use of the methods described using the R package.
Important please note:
If the course is fully booked please do complete the registration, as then you will be placed on a waiting list. You will be allocated a place if more places become available or if people cancel.
We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.
Details of course fees.
Cancellation Policy
Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.
Accommodation Details
Topics covered:
Introduction to statistical learning
The ideas of statistical modelling
Logistic regression.
Calibration and validation samples.
Multinomial regression.
Classification and regression trees.
Cross-validation
Ensemble methods.
Non-attendance
Accessibility Statement |
Legal Notice |
Freedom of Information |
Cookies Notice |
Staff & Student Privacy Notice |
External User Privacy Notice |
©
2022 Lancaster University. All rights reserved.
Privacy Statement
To use this platform, the system writes one or more cookies in your browser. These cookies are not shared with any third parties. In addition, your IP address and browser information is stored in server logs and used to generate anonymized usage statistics. Your institution uses these statistics to gauge the use of library content, and the information is not shared with any third parties.