2ID55: Adaptive Systems

Lecturers: Prof. dr. Paul De Bra, dr. ir. Natalia Stash

This website contains some general information about the Adaptive Systems course.

All concrete course material, such as (slide) presentations, assignments and (mandatory) tests is available through the Sakai learning management system. On the Sakai site you must log in using your regular TU/e (windows) account and then join this course. You should also edit your profile, adding information like the master program you are enrolled in. Please also upload a photo if you have one.

There is a general course schedule and a specific one. The general schedule says that lectures are on Thursdays, 5th and 6th hour, in the lecture hall in W-hoog. (This may be depending on the date, check owinfo for the latest info). The specific schedule for 2011-2012 is somewhat different. Most important to note are:

Part of the course is common between 2ID25 (Information Retrieval) and 2ID55 (Adaptive Systems) and taught at the respective times of both courses, so there are also lectures on Fridays, 7th and 8th hour in Helix collegezaal 2 in weeks 4, 5 and 6. This part is known as the Machine Learning Module.
In the first exam period (after quartile 1) there will be a mandatory test that leads to a partial grade for this course. (15% of the grade)
There is also a mandatory individual assignment about adaptive systems. (15% of the grade)
The lectures end before the end of the semester, to give you enough time to work on the final (group) assignment. (70% of the grade).

Preliminary Schedule

The schedule below is only indicative. Check the Sakai site for up to date information.


Week 1	Introduction (what are adaptive systems, why are they used and how do they work in general)
Week 2	Adaptive Educational Hypermedia, and workshop on creating "prerequisites"
Week 3	User Modeling
Week 4	Adaptation Methods and Techniques
Week 5	Reference Models for Adaptive Systems
Week 6	Introduction to machine learning techniques (2 lectures, joint with IR)
Week 7	Introduction to machine learning techniques (2 lectures, joint with IR)
Week 8	Introduction to machine learning techniques (2 lectures, joint with IR) Deadline for signing up for the project(s). You must commit to continue this course and do the assignments. Not signed up = you are out!
Exam Period	Mandatory partial exam on the Machine Learning Module
Week 9	Examples of adaptive systems (inspiration for the assignment).
Week 10	The GRAPPLE project and authoring environment
Week 11	The Generic Adaptation Language and Environment (GALE)
Week 9 - 11	Individual assignment work
Week 9 - 16	Group work on assignment. There are interim deadlines in week 12, 14 and 16

Below we show the course outline in a graphical way: