The document discusses the integration of machine learning in ubiquitous computing (ubicomp), highlighting the importance of context and sensor data analysis. It emphasizes the gap between theory-driven machine learning research and application-oriented ubicomp research, and outlines the growing interest in addressing this gap through tutorials and practical applications. Key challenges addressed include method selection, evaluation, and the diverse backgrounds of participants in the field.

1. Bridging the Gap: Machine Learning for Ubicomp
Thomas Ploetz, Mayank Goel, Nils Hammerla, Anind Dey
— Promises and Pitfalls—
http://d2z6avstustc5t.cloudfront.net/blog/wp-content/uploads/2014/06/millennials_and_it_bridge.jpg
Tutorial @UbiComp 2015

2. reviewed.c
“Specialized elements of hardware and software, connected by wires, radio waves and
infrared, will be so ubiquitous that no one will notice their presence.”
[Weiser]
2

3. Key to UbiComp: Sensor Data (Analysis)
UbiComp:
• Opportunistic Capturing of activities & context
• Sensor Data processing
• Automatic Analysis
• [Generate situated support]
http://seouldoll.com/wp-content/uploads/2013/04/ivory-
Focus on Real World Applications &
Systems!
• Positive impact on people’s lives
• Social responsibility
• [More challenging / interesting problems!] http://news.bbcimg.co.uk/media/images/49686000/jpg/_49686250_006806569-1.jpg
3

4. It’s all about Context!
4
[Schmidt et al., 1999]

5. How to analyse all this data?
— Machine Learning!
5
• Develop algorithms (“computer programs” [sic!] …) that adapt
(learn!) towards generalisation through analysing sample data
“Machine learning studies computer algorithms for
learning to do stuff”
[Robert Schapire]

6. The Gap!
6
Theory driven research in core ML community
vs
Application oriented research in UbiComp / ISWC community

7. Our Aim: (Start) Bridging the Gap!
7

8. Bridging the Gap: Machine Learning for Ubicomp
— Promises and Pitfalls—
• 2011: (@Gatech)
Discussions identify “gap”
• 2011-14:
More discussions and some
frustration building up
8
• 2014: (@Ubicomp)
“We gotta do something!”
• 2015: Tutorial!
• 07/2015: Overwhelmed by interest
(registration had to be closed two days
after advert went public …)

9. Who we are
9
Mayank Goel
Ubicomp Lab
Univ. Washington
Nils Hammerla
Open Lab
Newcastle Univ.
Thomas Ploetz
Open Lab
Newcastle Univ.
Anind Dey
HCI Institute
CMU

10. Who you are!
10

11. 11
General background (n=50; multiple choices
possible)
0
10
20
30
40
Student
Acad.Researcher
Prof.Researcher
Teacher
Designer
Other
1368
15
32
First time @UbiComp? (n=49)
no
35%
yes
65%
First technical tutorial? (n=50)
no
40%
yes
60%
YourBackground
Proficiency w/ Python
0
2
4
6
8
0 1 2 3 4 5 6 7 8 9 10
201
7
2
5
3
7
88
7

12. Your Interests
12
General application interest? (n=50;
multiple choices possible)
0
5
10
15
20
Mobile
HCI
DataAnalytics
Assist.Techn.
Health
Sports
Service
Other
112
55
7
9
20
ML for which applications? (n=44;
multiple choices possible)
0
10
20
30
40
Movement(wearables)
"bigdata"
location
video,images
text
audio
7
11
14
2627
34

13. 13
Familiarity with ML (n=54)
0
4.5
9
13.5
18
neverusedit
playedaroundwithit
usedforaproject
usedformanyprojects
othersaskmefor
0
17
15
18
4
How have you used ML?
(n=46; multiple choices
possible)
0
10
20
30
40
Researchprojects
publications
classprojects
hobbyprojects
industrial/comm.applications
6
9
15
19
34
Your Background

14. 14
Familiar w/ supervised learning?
(n=50)
yes
76%
no
24%
Experience w/ feature selection?
(n=50)
yes
58%
no
42%
Familiar w/ Lin. Alg? (n=50)
yes
86%
no
14%
Familiar w/ Stats / Prob.? (n=50)
yes
86%
no
14%
YourBackground

15. How you work
15
How do you select the ML methods for your application?? (n=44; multiple
choices possible)
0
10
20
30
40
Availabilty(WEKA...)
RelatedworkinUbicomp
Relatedworkelsewhere
RelatedworkICML/NIPS
Designfromscratch
other
15
10
18
20
33

16. Your problems
16
issues have you come across in applying machine learning? (n=47; multiple
choices possible)
0
7.5
15
22.5
30
Unclearchoiceofmethods
studydesign/datasets
evaluation
methodsunsuitableforappl.
deployment/maintenance
lackofperformance
other
NONE!
34
11
13
16
2223
25

17. Structure of the Tutorial
17
What? Start? End?
Doors 9:00 AM
Help desk 9:00 AM 9:30 AM
Start 9:30 AM 9:30 AM
Welcome + Intro 9:30 AM 9:40 AM
ML primer + ML applications in Ubicomp 9:40 AM 10:10 AM
Applied ML 10:10 AM 10:30 AM
break 10:30 AM 11:00 AM
Study Design 11:00 AM 11:20 AM
Evaluation 11:20 AM 11:40 AM
Tools? 11:40 AM 12:00 PM
lunch break 12:00 PM 1:30 PM
tasks for hands on 1:30 PM 1:40 PM
hands on 1:40 PM 3:30 PM
break 3:30 PM 4:00 PM
Final Panel 4:00 PM 4:30 PM
End 4:30 PM