Admission requirements
Students should have passed the course Basic statistical learning.
Students should have at least basic programming skills in R.
Description
Statistical learning refers to a vast set of tools for understanding data. These techniques are being used in wide range of industries and research fields. They have been used for product and movie recommendations, predicting disease status and progression, identifying fraudulent bank transactions, and identifying genes and biomarkers associated with specific diseases, to name just a few examples.
This course will focus on extensions and generalizations of supervised learning techniques taught in the basic statistical learning course.
We will study:
Model selection and the bias-variance trade-off in depth.
Bayesian approaches to predictive modeling.
Extensions for mixed and longitudinal prediction (e.g.: nested, clustered, hierarchical data structures).
The main concepts of neural networks and their connection to other statistical learning techniques.
Uncertainty quantification.
Interpretation: Model-agnostic methods to quantify and visualize predictor variables’ effects.
Specific methods that we will study, apply and evaluate will include: Bayesian additive regression trees, Gaussian process regression, extreme gradient boosting, feed-forward neural networks, Shapley values.
Course Objectives
After the course, the student can:
Reason how the trade-off between bias and variance is affected by model selection and sampling strategy.
Adapt statistical learning techniques to complex data situations, such as nested and longitudinal data structures.
Infer and explain the magnitude and shape of predictor variables' effects in various predictive models using model-agnostic tools.
Construct basic feed-forward neural networks.
Model the uncertainty in various types of prediction problems using Bayesian statistical learning techniques.
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of Instruction
Lectures and workgroups in R.
Assessment method
A written structured assignment earlier in the course and a less structured assignment at the end of the course. The final grade will be a weighted average of the grades for both reports.
Reading list
Various articles and book chapters for which links will be provided on Brightspace.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Julian Karch: j.d.karch@fsw.leidenuniv.nl
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.