The document outlines an introduction to a course on machine learning for language technology taught at Uppsala University, focusing on a flipped classroom model where instruction occurs online and lab sessions are held in-person. Key elements include course structure, attendance requirements, examination methods, and learning outcomes for both bachelor and master students. Students are expected to engage with video lectures and complete assignments individually, with a required attendance of 80% for successful course completion.

1. Machine
Learning
for
Language
Technology
2014
Introduc*on
to
the
Course
The
Flipped
Classroom
Marina
San*ni
san$nim@stp.lingfil.uu.se
Department
of
Linguis*cs
and
Philology
Uppsala
University,
Uppsala,
Sweden
Autumn
2014

2. Course
Website
&
Contact
Info:
• hIp://stp.lingfil.uu.se/~san*nim/ml/2014/ml4lt_2014.htm
• Contact
details:
– san$nim@stp.lingfil.uu.se
– marinasan$ni.ms@gmail.com
– marinaromestockholm@gmail.com
Lecture 1: Introduction to the Course
2

3. Outline
• Roll
Call
• Self-­‐Presenta*on
• Structure
of
the
Course
– People
– About
the
Course
– The
Flipped
Classroom
– The
Scalable
Learning
PlaUorm
– Examina*on
• Learning
Outcomes
• Literature
Lecture 1: Introduction to the Course
3

4. Who
you
are
ROLL
CALL
Lecture 1: Introduction to the Course
4

5. Roll
Call
Candidate/Bachelor
• Nicklas.Bergman.1141@student.uu.se
• Nils.Blomqvist.6151@student.uu.se
• Tiger.Friberg.1068@student.uu.se
• Oscar.Garcia_Alvarez.4807@student.uu.se
• Julia.Hall.1615@student.uu.se
• Henrik.Hedlund.8222@student.uu.se
• Odd.Jakobsson.3668@student.uu.se
• Jakob.Joelsson.3724@student.uu.se
• Dennis.Johansson.6164@student.uu.se
• Kim.Johansson.8218@student.uu.se
• Rickard.Lundstedt.8389@student.uu.se
Vincent.Nkongolo_Kayembe_Ilolo.4228@student.uu.se
• Victor.Radmark.2099@student.uu.se
• Oscar.Sagemo.1914@student.uu.se
• Avan.Sha-­‐Mohammed.2034@student.uu.se
• Vivian.Welker.8824@student.uu.se
• Emil.Wes*n.9516@student.uu.se
• cissih75@hotmail.com
Master
• Arvid.Lindahl.6171@student.uu.se
• Qiuyue
Quian
?????????
•
Andre.Smolentzov.1623@student.uu.se
Lecture 1: Introduction to the Course
5

6. Who
I
am
SELF-­‐PRESENTATION
Lecture 1: Introduction to the Course
6

7. Professional
Profile
(LinkedIn)
• Computa*onal
Linguist
– Research
scien*st
(SICS
East
Swedish
ICT)
– Lecturer
(Uppsala
University)
• Research
– Genre/Text
Type
Classifica*on
• WebGenreBlog
–
WebGenre
R&D
Group
–
etc.
– Text
Classifica*on/Sen*ment
Analysis
– Cross-­‐Linguality
– etc.
Lecture 1: Introduction to the Course
7

8. Bio
• I
am
Italian
(from
Rome)
• got
my
PhD
in
the
UK
(Brighton
University)
• married
to
a
Swede
• living
in
Stockholm
Lecture 1: Introduction to the Course
8

9. People,
structure,
design,
mo*va*on,
purpose
COURSE
STRUCTURE
Lecture 1: Introduction to the Course
9

10. People
• Marina
San*ni:
responsible
for
the
course
as
a
whole,
for
the
lab
classes
and
assignments.
•
• Joakim
Nivre:
decided
the
topics
of
the
course
and
will
deliver
the
online
lectures.
•
• Mats
Dallhöf:
responsible
for
all
administra*ve
issues
related
to
this
course.
Lecture 1: Introduction to the Course
10

11. About
the
Course
• Introduc*on
to
Machine
Learning.
• Its
focus
is
on
methods
used
in
Language
Technology
and
NLP
• ML
is
a
vast
field…
Selected
topics
Lecture 1: Introduction to the Course
11

12. What
is
a
”flipped
classroom”?
• Short
answer:
The
flipped
classroom
inverts
tradi*onal
teaching
methods,
delivering
instruc*on
online
outside
of
class
and
moving
exercises
into
the
classroom.
Lecture 1: Introduction to the Course
12

13. Flipping
learning
upside
down
• The
basic
idea
is
to
reverse
the
structure
of
tradi*onal
teaching.
• Tradi*onal
teaching
usually
is
based
on:
– lectures
that
are
delivered
in
a
classroom
by
a
lecturer
– homework
carried
out
by
students
by
themselves,
not
in
the
classroom.
• With
the
flipped
approach,
we
will
do
the
opposite:
– you
will
listen
to
the
online
lectures
at
home,
– you
will
be
in
the
classroom
to
do
your
homework
(that
we
will
call
lab
sessions).
Lecture 1: Introduction to the Course
13

14. The
Flipped
Classroom
Model
• Students
watch
lectures
at
home
at
their
own
pace,
communica*ng
with
peers
and
teachers
• Concept
engagement
takes
place
in
the
classroom
with
the
help
of
instructor.
• Basically,
the
flip
teaching
is
a
form
of
blended
learning
in
which
students
learn
new
content
online
by
watching
video
lectures,
usually
at
home,
and
what
used
to
be
homework
(assigned
problems)
is
now
done
in
class
with
teachers
offering
more
personalized
guidance
and
interac*on
with
students,
instead
of
lecturing.
Lecture 1: Introduction to the Course
14

15. Learning
Process
• Passive
phase:
that
we
can
call
the
recep<ve
phase,
where
the
student/learner
opens
the
mind
by
listening,
reading
and
receiving
new
informa*on.
In
this
phase
the
student
lets
new
knowledge
come
in.
• Ac*ve
phase:
that
we
can
call
the
produc<on
phase,
where
the
student/learner
processes
the
new
knowledge,
constructs
a
personal
concept
map
,
creates
cross-­‐references
with
previous
knowledge.
In
this
phase,
the
student
will
become
able
to
apply
the
new
knowledge
and
to
solve
prac*cal
tasks.
Lecture 1: Introduction to the Course
15

16. Research
says
that
…
…
oren
with
tradi*onal
teaching,
where
the
passive
phase
is
carried
out
in
the
classroom,
learning
outcomes
are
poor.
For
ex:
Lecture 1: Introduction to the Course
16

17. Thanks
to
Technology…
eLearning:
thanks
to
the
availability
and
success
of
online
videos
used
for
pedagogical
purposes,
and
the
increased
access
to
technology,
it
is
now
possible
to
stop
this
nega*ve
trend.
Lecture 1: Introduction to the Course
17

18. The
Big
Advantage
• It
allows
students
to
personalize
the
learning
at
their
own
pace.
• You
can
replay
the
videos
as
many
*me
as
you
like,
you
stop
them
and
resume
them
if
you
need
to
look
up
a
word
in
a
dic*onary,
or
if
you
need
to
brush
up
a
concept,
or
if
you
are
*red
or
hungry,
etc.
• Therefore
there
is
both
a
cogni*ve
and
physical
advantage
in
doing
the
passive
phase
at
home.
Lecture 1: Introduction to the Course
18

19. Success
Story:
Coursera
• The
state
of
the
art
of
online
learning
is
MOOC,
massive
open
online
courses
(wikipedia).
• Coursera
(wikipedia)
courses
are
a
successful
implementa*on
of
this
idea:
“more
than
one
million
people
who
have
enrolled
in
the
site’s
courses
are
expected
to
pay
aIen*on
during
video
lectures
interspersed
with
interac*ve
exercises
and
complete
homework
assignments
in
between
lectures”
(source).
Lecture 1: Introduction to the Course
19

20. The
Scalable
Learning
PlaUorm
• In
this
course,
we
do
not
want
to
replicate
or
emulate
Coursera.
• Our
aim
is
to
deliver
a
course
(1)
that
requires
a
cogni*ve
effort
and
(2)
that
can
be
learnt
successfully
both
by
candidate
students
and
master
students.
• With
the
flip
teaching,
we
would
like
to
allow
students
to
adjust
the
learning
of
the
new
subject
at
their
own
pace
and
background
knowledge.
• We
will
use
plaUorm
that
has
been
developed
in
Sweden
(by
Swedish
Ins*tute
of
Computer
Science
and
Uppsala
University)
and
it
is
called
Scalable
Learning.
Lecture 1: Introduction to the Course
20

21. Scalable
Learning
at
Uppsala
Uni
• The
plaUorm
is
already
successfully
used
at
Uppsala
University.
• David
Black-­‐Schaffer
(Department
of
Informa*on
Technology,
UU)
is
regularly
using
it
for
his
own
courses.
• See
David’s
video
presenta*on
for
mo*va*on,
aims,
and
outcomes.
Lecture 1: Introduction to the Course
21

22. How
are
we
going
to
work
with
the
Scalable
Learning
plaUorm?
You
need
to:
• create
an
account
(only
the
first
*me);
• log
in
to
the
plaUorm
when
you
receive
a
email
sta*ng
that
a
lecture
is
ready.
YOU
WILL
RECEIVE
THIS
EMAIL
2
OR
3
DAYS
BEFORE
THE
RELATED
LAB
SESSION.
• Then:
– Home:
listen
to
the
video
clips,
answer
the
online
quizzes,
study
related
literature;
– Classroom:
aIend
the
lab
sessions
and
complete
the
lab
tasks.
This
requires
physical
aIendance
during
the
scheduled
days
(see
schedule).
Lecture 1: Introduction to the Course
22

23. In
prac*ce…
• My
Student
Account
Lecture 1: Introduction to the Course
23

24. Analy*cs
• The
plaUorm
creates
analy*cs
that
help
the
teacher
to
understand
how
the
learning
is
going.
These
analy*cs
are
anonymous.
• The
aim
of
this
e-­‐learning
plaUorm
is
to
understand
which
concepts
and
topics
are
more
difficult
for
the
students,
thus
enabling
the
teacher
to
provide
the
appropriate
support.
Lecture 1: Introduction to the Course
24

25. Communica*on
and
Interac*on
• The
plaUorm
allows
both
anonymous
and
non-­‐anonymous
communica*on
between
students
and
teachers.
• The
aim
is
to
create
an
interac*on
that
is
smooth,
unproblema*c
and
seamless.
Lecture 1: Introduction to the Course
25

26. IMPORTANT!
• If
you
do
not
aIend
a
video
lecture
on
the
plaUorm
you
will
not
be
able
to
carry
out
the
tasks
during
the
lab
sessions.
AIending
the
video
lecture
is
a
prerequisite
to
the
related
lab
session.
• The
comple*on
of
the
lab
tasks
is
compulsory.
• This
is
the
basic
structure
of
the
course:
1. Home:
Video
Lecture
+
Quizzes
+
Reading
2. Classroom:
Lab
Tasks
related
to
1.
Quizzes
and
Lab
Tasks
are
not
graded
but
must
be
completed
in
order
to
pass
the
course.
Lecture 1: Introduction to the Course
26

27. 3
Graded
Assignments
• The
course
will
be
graded
with
three
home
assignments
to
be
completed
individually
and
submiIed
by
the
due
date
(see
schedule).
• The
idea
is
that
you
complete
the
assignments
in
the
correct
way
but
also
with
a
certain
degree
of
independent
crea*vity.
There
are
usually
several
different
approaches
to
solve
a
problem,
a
task,
or
to
complete
an
assignment.
Choose
the
one
that
is
more
suitable
for
your
mindset.
• Important
always:
– state
and
cri*cally
discuss
methodological
assump*ons;
– apply
state-­‐of-­‐the-­‐art
methods
we
learn
in
this
course;
– present
the
results
in
a
professionally
adequate
manner;
use
English
(scien*fic
lingua
franca)
and
academic
style
when
wri*ng
your
reports.
Lecture 1: Introduction to the Course
27

28. Examina*on
• Quizzes
and
Lab
Tasks:
The
comple*on
of
quizzes
and
lab
tasks
is
mondatory.
Quizzes
and
lab
tasks
are
not
graded.
• Assignments:The
submission
of
each
of
the
three
home
assignments
is
mondatory.
Home
assignments
are
graded
and
the
following
marks
will
be
used:
– Underkänd
(U)
[Fail]
– Godkänt
(G)
[Pass]
– Väl
Godkänt
(VG)
[Dis*nc*on]
• In
order
to
pass
the
course,
three
G
are
required
+
the
comple*on
of
quizzes
and
lab
tasks.
• In
order
to
pass
the
course
with
dis*nc*on
(VG),
a
student
must
pass
at
least
two
home
assignments
with
dis*nc*on
(VG)..
Lecture 1: Introduction to the Course
28

29. AIendance
• The
whole
aIendance
requirement
for
the
course
is
about
80%.
• This
means
that
you
should
aIend
9
out
of
12
online
lectures
and
related
lab
sessions.
• If
a
student
fails
to
fulfill
this
requirement,
an
addi*onal
assignment
will
have
to
be
completed
prior
to
passing
the
course.
The
choice
of
the
topic
will
relate
to
the
missed
material.
Lecture 1: Introduction to the Course
29

30. What
the
student
will
do
that
demonstrates
learning
LEARNING
OUTCOMES
Lecture 1: Introduction to the Course
30

31. What
is
a
learning
outcome?
• Learning
outcomes
describe
what
students
are
able
to
demonstrate
in
terms
of
knowledge,
skills,
and
values
upon
comple*on
of
a
course.
Lecture 1: Introduction to the Course
31

32. Candidate/Bachelor
Arer
the
course,
the
student
will
be
able
to:
• apply
basic
machine
learning
principles
to
the
linguis*c
data;
• apply
methods
to
evaluate
machine
learning
based
systems
performance
within
language
technology;
• apply
probability
theory
and
principles
of
sta*s*cal
inference
to
linguis*c
data;
• use
standard
sorware
for
machine
learning;
• apply
linear
models
for
classifica*on;
• apply
clustering
techniques
to
linguis*c
data.
Lecture 1: Introduction to the Course
32

33. Master
Arer
the
course,
the
student
will
be
able
to:
• apply
basic
machine
learning
principles
to
the
linguis*c
data;
• apply
probability
theory
and
principles
of
sta*s*cal
inference
to
linguis*c
data;
• use
standard
sorware
for
machine
learning;
• implement
linear
models
for
classifica$on;
• apply
clustering
techniques
to
linguis*c
data.
Lecture 1: Introduction to the Course
33

34. Literature
READING
LIST
Lecture 1: Introduction to the Course
34

35. Reading
List
(Required)
• Text
books
– Alpaydin
E.
(2010)
– Daumé
III
H.
2012.
– Jurafsky
D.
&
Mar*n
J.
(2009)
– Mitchell
T.
(1997).
– Schay,
Géza
(2007)
• Papers
– Androutsopoulos
et
al.(2000)
– Collins
(2002)
– Metsis
et
al.
(2006):
only
for
master
students
– Nigam
et
al.(2000)
• For
the
Lab
Sessions
• Ian
H.
WiIen,
Eibe
Frank.
2005.
• RapidMiner
(maybe)
Lecture 1: Introduction to the Course
35

36. Op*onal
Reading
and
Pointers
• See
course
website
Lecture 1: Introduction to the Course
36

37. Keep
yourselves
updated
COURSE
WEBSITE
Lecture 1: Introduction to the Course
37

38. Schedule,
News
and
more
Lecture 1: Introduction to the Course
38

39. The
End
Ques*ons?
Lecture 1: Introduction to the Course
39