Machine Learning for the Quantified Self

2018-2019

Course Objective

The quantified-self refers to large-scale data collection of a user's
behavior and context via a range of sensory devices, including smart
phones, smart watches, ambient sensors, etc. These measurements contain
a wealth of information that can be extracted by means of machine
learning techniques, for instance for the purpose of predictive
modeling. In addition, machine learning techniques can be a driver for
adaptive systems to support users in a personalized way based on the
aforementioned measurements. The type of data does however require
specialized machine learning techniques to fully exploit the information
contained in the data. Examples of challenges include the temporal
nature of the data, the variety in the type of data, the different
granularity of various sensors, noise, etcetera. The main aims of this
course are to:
* Understand the challenges imposed by quantified-self data upon machine
learning techniques.
* Become familiar with machine learning techniques for predictive
modeling that are able to cope with these challenges.
* Become familiar with machine learning techniques that drive adaptive
feedback and support.
* Understand how different machine learning approaches can be united in
a single system.
The student should become familiar with the more theoretical side of the
domain and the current state-of-the-art in research. In addition, the
student will learn how to apply this knowledge in a practical setting.

Course Content

The course will provide an overview of relevant state-of-the-art machine
learning techniques. More in specific, it will address:
• Feature engineering (how do we come from raw data to usable features):
* Removing noise from data
* Handling missing data
* Identifying (temporal) features
• Learning of user patterns:
* Temporal machine learning approaches such as recurrent neural
networks, time series analysis
* Clustering approaches with dedicated distance metrics (including
dynamic time warping)
• Adaptive feedback and support
* Reinforcement learning
• Integration of the various components.
In addition, a number of real-life applications will be discussed. Next
to lectures, there will be an extensive practical part, where students
will learn to work with various algorithms and data sets. As a final
assignment, the students will work on a project they propose themselves.

Teaching Methods

The course will be taught in four weeks. During the first two weeks the
emphasis will be on lectures (l) and assignments associated with the
material covered in the lectures. These assignments will form the basis
for the final assignment, which is a project

Method of Assessment

Written exam (E) (50%) and practical assignments (A) (50%). For both
parts the grade needs to be sufficient to obtain a final grade. For the
practical assignments the final assignment counts for 60% while the
smaller assignments associated with the lectures count for 40% in total.

Literature

Hoogendoorn, M. and Funk, B. Machine Learning for the Quantified Self -
On the Art of Learning from Sensory Data, ISBN 978-3-319-66308-1,
Springer Verlag 2018. The book can be downloaded free of charge through
the UBVU, just follow the link to the book:
http://dx.doi.org/10.1007/978-3-319-66308-1

Target Audience

XM_AI, XM_BA, XM_CS

Additional Information

Lecturer:
Dr.M. Hoogendoorn

Recommended background knowledge

Programming experience. Knowledge of basic machine learning algorithms.

General Information

Course Code XM_40012
Credits 6 EC
Period P6
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. M. Hoogendoorn
Examiner dr. M. Hoogendoorn
Teaching Staff

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

This course is also available as: