Tuesday 18 February 2020, 12:00am to 11:59pm
VenuePostgraduate Statistics Centre, LA1 4YW - View Map
Open toPostgraduates, Public, Staff, Undergraduates
RegistrationFree to attend - registration required
Registration InfoRegistration required
Please note: a timetable will be sent out to all participants one week before the event...
Please note: a timetable will be sent out to all participants one week before the event.
In this 1-day course we will introduce some key topics in statistical learning, primarily relating to the specification, estimation and interpretation of linear models. Although these models are very simplistic, they give provide a good baseline when performing predictive modelling and give an opportunity to discuss different aspects of statistical model building.
The course will consist of three short lectures and lab sessions (which will involve coding in R). Ideally attendees will have RStudio (www.rstudio.com) installed on their own laptops.
A brief overview of topics covered is given below:
- Understanding the Bias-Variance trade-off
- Assessing model performance (Cross-Validation, Information Criteria)
- Variable Selection (Forward, Backward, Stepwise)
- Regularised Regression (LASSO, Ridge-Regression, Elastic-Net)
- Generalised Linear Models (Binary Output, Categorical Output)
- Generalised Additive Models (very briefly)
If you have questions regarding the course material, please email Alex Gibberd (email@example.com)
Important please note:
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.
Payment: Once you have registered, please pay at the online shop
Accommodation Details: Can be found here.
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.
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+44 1524 592339