Statistical Learning

Leiden University, Autumn 2013, 2nd year master of statistical science

You are reading the homepage http://www.math.leidenuniv.nl/~avdvaart/statlearn/index.html of this course.
Click here for a general course description.
This year's course was adapted from earlier courses by Prof. Dr. Peter Grünwald, and we gratefully acknowledge his permission for using his materials.

General Information

Lecturer: Aad van der Vaart, Leiden University, Mathematical Institute
Assistant:
Contact: Come see me in office R 2.23, or for simple questions, send email.
Course load: 4 ECTS.
Dates: Lectures take place on the dates indicated below.
Hours: First session from 10.00-16.30; later sessions from 11.15 to 15.30.
Location: Room 409 of the Snellius Building, Niels Bohrweg 1, Leiden.
Examination: To pass the course you must obtain a sufficient grade (6 or higher) on both of the following two:
  1. The written open-book examination on January 8, 14-17 hours; or the resit on February 14, 10-13 hours.
  2. Homework Projects. We hand out two homework assignments, possibly in parts (see below). The final homework grade will be determined as an average of the grades for the two assignments.
The final grade will be determined as the average of the homework grades and the final open-book examination.
Literature: Chapters of The Elements of Statistical Learning, 2nd edition, by Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer-Verlag 2009. This book can be downloaded for free at the above link. Alternatively, an electronic version is available as pdf through the university library, where it is also possible to order a paper copy through Springer link for 25 euros.

Homework

Both homework assignments involve setting up some experiments in R, experimenting, and writing a short report about the results. Discussing the problems in the group is encouraged, but every participant must do her or his experiments and write her/his own report. Hand in your solution electronically as a pdf file. Let it be a self-contained, readable report, which includes pictures, tables, computer output, explanations, interpretations, answer in the text.
  1. The first homework assignment on regression can be found on blackboard from November 18 and is due December 8. Email the solution as a single .pdf file to the lecturer (or use blackboard).
  2. The second homework assignment about classification can be found on blackboard from December 9 and is due January 1.

Preliminary Course Schedule

The following preliminary schedule may not be updated. Instead we shall employ the blackboard environment for late changes. Make sure you are enrolled and check the blackboard pages!

If a section is included without mention of subsections (e.g. Section 2.1), this means that all subsections of that section are covered (i.e. you should also be familiar with Sections 2.1.1, 2.1.2, etc.).

  1. November 4: Introduction, Regression Part I
  2. November 11: no lecture, self-study of 2.6-2.8 and linear algebra (see blackboard), read 3.1-3.2.
  3. November 18: Regression, Part II
  4. November 25: Regression Part III and Classification Part I
  5. December 2: Classification Part II
  6. December 9: Classification Part III
  7. December 16: Model Assessment and selection (lecturer: Johannes Schmidt-Hieber)
  8. December 23: Unsupervised Learning

Here you can find an example of examination questions