Next Steps in Statistical Learning (Data Mining II) - (2 day course)
Event box
A 2 day course: 5th & 6th February 2018
This course develops the concepts of statistical learning introduced in the first course on Statistical Learning.
It covers both clustering (unsupervised learning) and advanced prediction (supervised learning) methods.
The focus will be on methods which have a statistical interpretation, so model-based clustering through latent class analysis will be covered along with more heuristic methods. Real life examples are used, and there is time for practical use of the methods described using the R and Latent Gold packages.
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 clustering
Hierarchical and non-hierarchical clustering
Distance metrics and types of linkage
The dendogram
Kmeans
Assessment of clustering performance.
Mixture models
Latent class models.
Longitudinal latent class models.
Neural networks
Other predictive 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.