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MTTTS16 Learning from Multiple Sources

MTTTS16 Learning from Multiple Sources, fall 2018

Course contents

Also available in the Curricula Guide.

Contents. Learning from multiple sources denotes the problem of jointly learning from a set of (partially) related learning problems, views, or tasks. This general concept underlies several topics of research, which differ in terms of the assumptions made about the dependency structure between learning problems. During the course, we will cover a number of different learning tasks for integrating multiple sources and go through recent advances in the field. Examples of topics covered by the course include data fusion, transfer learning, multitask learning, multiview learning, and learning under covariate shift.

Learning Outcomes. After the course the student is familiar with several settings related to learning from multiple sources, and is familiar with a selection of important approaches and methods used for learning in each setting.

Passing the Course

To pass the course, you must pass the exam and complete a sufficient number of exercises from the exercise packs. Exercise packs will be released during the course.

Preliminary grading scheme (note: preliminary information only, may change!): the exercise packs are graded in total either as 0 (fail) or as a fractional number between 1 and 5 (such as 1.34). The exam is similarly graded either as 0 (fail) or as a fractional number between 1 and 5. The total grade of the course is computed as round(0.8*ExamGrade + 0.2*ExercisesGrade), so that e.g. 4.51 rounds up to 5 and 4.49 rounds down to 4.

Teaching schedule

For general schedule information, please see the Teaching Schedule for the Academic Year 2018–2019.

Preliminary Lecture Schedule

The regular lecture time is Tuesdays 14-16 in room Pinni B0016. Some changes in lecture dates may be possible - if changes are made they will be announced here and by email.

4.9. L1: Introduction to the course, preliminaries. Material: Lecture slides
11.9. L2: Basic Canonical Correlation Analysis. Material: Lecture slides
18.9. L3: Basic multitask learning with neural network arrangements. Material: Lecture slides
25.9. L4: Transfer learning.
2.10. L5: Transfer learning, continued.
9.10. L6: Probabilistic CCA and kernel CCA.
16.10. L7: Multitask learning with task clustering or gating.
23.10. continuation of L7.
30.10. L8: Multitask learning with kernel methods and nonparametric models.
6.11. L9: Multi-view learning for classification by Co-training.
13.11. L10: Co-training continued; disagreeing views; semisupervised multi-task learning; self-taught learning.
20.11. L11: Domain adaptation.
27.11. L12: Learning sample correspondence.
4.12. No lecture
11.12. Exam

Informal "home assignments"

Some assignments to help you think about the lecture material will be published here. Return solutions by email to the lecturer.

 

Exercise Packs

Exercise packs released during the course will appear here.

  • Exercise packs, part 1 - return deadline October 26, 2018. Note: link is password protected, username and password have been sent to course participants.


 

 
Ylläpito: mtt-studies@sis.uta.fi
Muutettu: 25.9.2018 12.15 Muokkaa

Tampereen yliopisto

Tampereen yliopisto
03 355 111
kirjaamo@uta.fi


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