Introduction to the Machine Learning for Language Technology course, 2015, Uppsala University

1. Machine Learning for Language Technology 2015
http://stp.lingfil.uu.se/~santinim/ml/2015/ml4lt_2015.htm
Introduction to the Course
The Flipped Classroom Model
Marina Santini
santinim@stp.lingfil.uu.se
Department of Linguistics and Philology
Uppsala University, Uppsala, Sweden
Autumn 2015

2. Course Website & Contact Info:
http://stp.lingfil.uu.se/~santinim/ml/2015/ml4lt_2015.htm
• Contact details:
– santinim@stp.lingfil.uu.se
– marinasantini.ms@gmail.com
– marinaromestockholm@gmail.com
Lecture 1: Introduction to the Course 2

3. Students’ Email addresses
Lecture 1: Introduction to the Course 3
Make sure you send your
email addresses to me.
Send me the email address
that you check daily.
The communication via email
is important for this course.
Once I got your email, you
will receive my reply and the
enrolment key to access the
Scalable Learning platform.

4. Outline
• Attendance
• Examination
• Course Organization
• Course Structure
• The Flipped Classroom
• Learning Outcomes
• Course Website and Summary
Lecture 1: Introduction to the Course 4

5. ATTENDANCE
Lecture 1: Introduction to the Course 5

6. Attendance is Mandatory
• There is a mandatory 80% attendance requirement for
both:
– presentations delivered through the online platform
– in-class lab sessions
• The whole course is made of 11 lectures.
• 80% attendance means that you can miss a couple of
lectures
– ie: 11:100=x:80x=80*11/100x=8.880%=8.89 (rounded
up)
• In other words, you should attend 9 lectures out of 11 to be
eligible to pass the course
Lecture 1: Introduction to the Course 6

7. More than 20% absence may result in
additional assignments
• Plan carefully, if you already have committments.
• Notify me if you already know that you cannot
comply with the 80% attendance requirement
Lecture 1: Introduction to the Course 7

8. Attendance is mandatory because...
• ...this course is quite tough for linguists.
• It contains math and statistics, and requires
practice with a software workbench...
• Regular attendance is helpful to smooth
difficulties.
Lecture 1: Introduction to the Course 8

9. Lectures are paired:
online presentation + lab session = 1 lecture
• 80% virtual attendance to online presentations
(minimum: 9 out of 11 presentations)
• 80% physical attendance to in-class lab sessions
(minimum: 9 out of 11 lab sessions)
• If you do not attend an online presentation,
there is no point in attending the matching lab
session because lectures are paired.
Lecture 1: Introduction to the Course 9

10. Attendance: Paired lectures
• Not valid:
– online presentation=no;
lab=yes;
• Not valid:
– online presentation=yes;
lab=no;
• Valid:
– online presentation=yes; lab=yes.
Lecture 1: Introduction to the Course 10
+ paired lab
+ paired lab
+ paired lab
+ paired lab
+ paired lab
+ paired lab
+ paired lab
+ paired lab
[...]

11. EXAMINATION
Lecture 1: Introduction to the Course 11

12. Examination:
9 graded INDIVIDUAL lab assignments
• The course is examined by means of 9 in-class lab assignments, from lecture 2 to lecture
11.
• All graded lab assignments have equal weight.
• Each lab assignment will be graded with the following marks:
– Underkänd (U) [Fail]
– Godkänt (G) [Pass]
– Väl Godkänt (VG) [Distinction]
• In order to pass the course (ie to receive the passing grade G on the course), a student
must submit all the lab assignments and at least 5 of them need to be a G.
• In order to receive pass with distinction (VG), the majority of all the submitted lab
assignments have to meet the criteria for distinction.
• If a student fails the examination, additional assignments will be required in order to
receive a passing grade on the course.
Lecture 1: Introduction to the Course 12

13. Graded assignments: single and double
• Summary:
– The course is examined by means of INDIVIDUAL graded lab
assignments.
– Students must complete correctly at least 5 lab assignments out of the
total number of submitted lab assignments.
• 8 single assignments: Students will sit for a lab assignment every lab
session. Students can submit immediately (i.e. at the end of the lab
session) OR the next day, if more time for reflection is needed. The
deadline of a single lab assignment is the day after the lab at 1pm.
• 1 double assignment: ”double” means that the last assignment
stretches over 2 lectures. Joakim will prepare a programming lab
assignment with starter code in python. The basic task will be to
implement the basic perceptron. You will work on this programming
assignment during the last two lab sessions of the course.
Lecture 1: Introduction to the Course 13

14. Master Students
• Must fulfil the basic requirements of the
course (at least 5 Gs out of all submitted lab
assignments)
• + a Home Assignment (passing grade=at least
a G) [programming assignment]
Lecture 1: Introduction to the Course 14

15. COURSE ORGANIZATION
Lecture 1: Introduction to the Course 15

16. People
• Marina Santini: delivering some online
presentations and responsible for the lab classes
based on Weka and on statistics.
• Joakim Nivre: decided the topics of the course and
delivering some online presentations. He is
responsible for the programming assignment based
on the perceptron.
• Mats Dallhöf: responsible for all administrative
issues related to this course.
Lecture 1: Introduction to the Course 16

17. Students’ Responsibilities
• Attendance (virtual and physical)
• Reading
• Submission of lab assignments (graded)
Lecture 1: Introduction to the Course 17

18. Students MUST read 
• Listen to online presentations AND read the
pages associated with the lectures (see the
course website).
Lecture 1: Introduction to the Course 18

19. About the Course
• Introduction to Machine Learning applied to
Language Technology.
• The focus of the course is on models that are
commonly used in Language Technology and NLP.
• Teaching is based on the Flipped Classroom
educational strategy.
Lecture 1: Introduction to the Course 19

20. Reading: only chapters specified in the
course website
• Hal Daumé III (2015). A Course in Machine
Learning. Copyright © 2015.
Ian H. Witten, Eibe Frank, Mark A. Hall (2011).
Data Mining: Practical Machine Learning
Tools and Techniques. 3rd Edition. Morgan
Kaufmann Publishers.
Lecture 1: Introduction to the Course 20

21. Lab assignments
• At each lab session, you will be given the lab
assignment of the day.
• You will complete the lab tasks and you will
submit either at the end of the class, or you
will send the lab assignment to me by 1pm of
the next day.
Lecture 1: Introduction to the Course 21

22. Computers
• Either use your laptop (recommended) or use
the computers in the classroom.
• Most of the lab assignments are based on the
Weka software package, which means that:
– You must install the software and deal with any
issues about memory limits or errors or hardware
problems. Choose the computer that you feel
confident about. If you have problems on a
computer, just change to another. We focus on the
software package and basic IT skills.
Lecture 1: Introduction to the Course 22

23. Math
• There will be a fair amount of math and statistics in the ML4LT course.
• My main effort: to simply as much as I could mathematical and statistical
concepts in the online presentations
• Your main effort is to study the theoretical underpinnings berfore engaging in
lab assignments.
• Be prepared to carry out some individual study if you feel that you need to
refresh basic math knowledge that is not provided in this course
• Basic requirements for this course:
– Matematik 2a/2b/2c (områdesbehörighet A7)
OR
– Matematik B (områdesbehörighet 7)
Lecture 1: Introduction to the Course 23

24. Interaction during the lab assignments
• Tricky part 
• Lab assignments are individual (you will get a grade for your work), BUT
you are encouraged to talk to each other and discuss in group.
• Challenge: The effort is to learn as much as you can independently by
interacting in group.
• I will help as LITTLE as I can during the labs: I will not give you hints to
solutions, nor solve your computer-related problems. BUT I will act as a
moderator or a facilitator in a discussion (if needed).
• LEARNING OUTCOMES OF LAB ASSIGNMENTS:
– Cooperating with others to optimize your understanding of the topic
– Fostering independent-thinking
– Enhancing problem-solving skills
– Finding the best way to show that you master the topic of the day both
practically and theoretically.
Lecture 1: Introduction to the Course 24

25. Cheating
• Any assignment that is handed in must be your own work.
• However, talking to one another to understand the material
better is strongly encouraged: recognizing the distinction
between cooperation and cheating is very important!
• COOPERATION with other students IS WARMLY
ENCOURAGED!
• Plagiarism—copying from others—is condemned and
measures will be taken if it happens.
Lecture 1: Introduction to the Course 25

26. COURSE STRUCTURE
Lecture 1: Introduction to the Course 26

27. Hybrid learning approach:
virtual and physical attendance
• Online presentations  Scalable Learning platform
• Lab sessions  Chomsky classroom
Lecture 1: Introduction to the Course 27

28. Scalable Learning
• The platform: www.scalable-learning.com
Lecture 1: Introduction to the Course 28

29. Scalable Learning: your tasks
• Virtual
attendance
• Listen to the
video clips
• Answer the
quizzes
• Read the
associated
chapters
• Each
presentation
has a
deadline! Lecture 1: Introduction to the Course 29

30. Analytics
• The aim of this e-learning platform 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 30

31. Online Presentations, Video Clips and
Quizzes
• A presentation is made of several video clips.
• The length of the presentations and the length
of video clips are variable.
• The number of the quizzes per presentation is
variable. Quizzes are NOT graded.
Lecture 1: Introduction to the Course 31

32. Communication and Interaction
• The platform allows both anonymous and non-
anonymous communication between students
and teachers.
• The aim is to create an interaction that is smooth,
unproblematic and effective.
• Ask questions through the platform or by email:
either you will receive an individual answer or we
will discuss your questions in class.
Lecture 1: Introduction to the Course 32

33. Lab Sessions: two parts
• First, general comments on your virtual
attendance to the online presentations
(questions, quizzes, etc. )
• Then, you will be give a lab assignment to
complete.
Lecture 1: Introduction to the Course 33

34. Lab Sessions: Lab Assignments
Example of a Lab Assignment...
Lecture 1: Introduction to the Course 34

35. Lab Sessions: Interaction
Lab assignments are individual (you will get a grade)
but cooperation among students is strongly
encouraged:
• We warmly encourage students to help one
another understand the assignments, the tasks,
the theoretical concepts and general issues
relevant to the course.
• Research shows that cooperation among students
combined with individual thinking is an effective
way to acquire and activate new knowledge.
Lecture 1: Introduction to the Course 35

36. FLIPPED CLASSROOM
Lecture 1: Introduction to the Course 36

37. What is a ”flipped classroom”?
• Short answer: The flipped classroom inverts
traditional teaching methods, delivering
theoretical knowledge online outside of
classroom and moving exercises into the
classroom.
Lecture 1: Introduction to the Course 37

38. Flipping learning is upside down
• The basic idea is to reverse the structure of traditional
teaching.
• Traditional 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 presentations at home
– you will be in the classroom to do your homework (that
we will call lab sessions)
Lecture 1: Introduction to the Course 38

39. The Flipped Classroom Model
• Students watch lectures at home at their own
pace, communicating with peers and teachers
via email or via the platform.
Lecture 1: Introduction to the Course 39

40. Learning Process
• Passive phase: that we can call the receptive phase,
where the student/learner opens the mind by listening,
reading and receiving new information. In this phase
the student lets new knowledge come in.
• Active phase: that we can call the production 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 practical tasks.
Lecture 1: Introduction to the Course 40

41. Research says that …
… often with traditional teaching, where the
passive phase is carried out in the classroom,
learning outcomes are poor. For ex:
Lecture 1: Introduction to the Course 41

42. Thanks to Technology and eLearning…
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 negative trend.
Lecture 1: Introduction to the Course 42

43. The benefits
• It allows students to personalize the learning at
their own pace.
• You can replay the videos as many time as you
like, you stop them and resume them if you need
to look up a word in a dictionary, or if you need to
brush up a concept, or if you are tired or hungry,
etc.
• Therefore there is both a cognitive and physical
advantage in doing the passive phase at home.
Lecture 1: Introduction to the Course 43

44. The Scalable Learning Platform
• We will use platform that
has been developed in
Sweden (by Swedish
Institute of Computer
Science and Uppsala
University) and it is called
Scalable Learning.
• Create your own account
and sign up for the
course using the
enrolment key that will
be sent to you.
Lecture 1: Introduction to the Course 44

45. Scalable Learning at Uppsala Uni
• The platform is already successfully used at
Uppsala University.
• David Black-Schaffer (Department of
Information Technology, UU) is regularly using
it for his own courses.
Watch David’s video presentation for
motivation, aims, and outcomes.
Lecture 1: Introduction to the Course 45

46. Online material
• Video clips
• Quizzes
• Lecture transcripts (or Lecture notes)
• Slides (pdf)
• Slides (slideshare)
Lecture 1: Introduction to the Course 46

47. LEARNING OUTCOMES
Lecture 1: Introduction to the Course 47

48. What is a learning outcome?
• Learning outcomes describe what students are
able to demonstrate in terms of knowledge,
skills, and values upon completion of a course.
Lecture 1: Introduction to the Course 48

49. Expected Learning Outcomes
1. apply basic principles of
machine learning to natural
language data;
2. show theoretical and practical
knowledge of the following
machine learning methods:
– decision trees
– naïve bayes classifiers
– logistic regression
– the perceptron
3. use of a standard machine
learning package for practical
classification and evaluation
(the Weka workbench)
Lecture 1: Introduction to the Course 49

50. COURSE WEBSITE & SUMMARY
Lecture 1: Introduction to the Course 50

51. Schedule, News and more
Lecture 1: Introduction to the Course 51
Check the
website
regularly

52. Summary: basic stuff
• 80% attendance requirement
• Paired lectures: 1 lecture = online presentation + matching lab session
• Lab assignments are graded; quizzes are not graded
• Examination: In order to pass the course successfully, 5 Gs is the
minimum requirement (for bachelor students). Master students must
submit a home assignment, in addition to the minimum requirement.
• Cooperation is encouraged, cheating is condemned.
Lecture 1: Introduction to the Course 52

53. Questions?
Lecture 1: Introduction to the Course 53