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MTTTS17 Dimensionality Reduction and Visualization

MTTTS17 Dimensionality Reduction and Visualization, spring 2018

Basic Course Information

The basic course description is available in the Curriculum Guide. The basic teaching schedule is available in the Teaching Schedule for the Academic Year 2016–2017.

Lectures are given weekly Tuesdays 14-16 in Pinni B0020 starting Jan 9, see the preliminary lecture schedule below. Lecturer: associate professor Jaakko Peltonen.

Course Contents

Preliminary contents: Properties of high-dimensional data; Feature Selection; Linear feature extraction methods such as principal component analysis and linear discriminant analysis; Graphical excellence; Human perception; Nonlinear dimensionality reduction methods such as the self-organizing map and Laplacian embedding; Neighbor embedding methods such as stochastic neighbor embedding and the neighbor retrieval visualizer; Graph visualization; Graph layout methods such as LinLog.

Course Material

The course is based on the lecture slides. However, a related book is Nonlinear Dimensionality Reduction (John Lee, Michel Verleysen). For lecture 4 (graphical excellence) a related book is The Visual Display of Quantitative Information (Edward R. Tufte). For lecture 5 (human perception) a related book is Information Visualization: Perception for Design (Colin Ware).

Learning Outcomes

After the course, the student will be aware of main approaches and issues in dimensionality reduction and visualization, will be aware of a variety of methods applicable to the tasks, and will be able to apply some of the basic techniques.

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.

Preliminary Schedule

The preliminary schedule below may change as the course progresses. Lecture slides for each lecture will be added to the schedule as the course progresses.

Jan 9 Lecture 1: Introduction, properties of high-dimensional data. Lecture material: lecture slides
Jan 16 Lecture 2: Feature selection. Lecture material: lecture slides
Jan 23 Lecture 3: Linear dimensionality reduction. Lecture material: lecture slides
Jan 30 Lecture on linear dimensionality reduction continued
Feb 6 Lecture 4: Graphical excellence. Lecture material: lecture slides (updated Feb 27)
Feb 13 Lecture 5: Human perception. Lecture material: lecture slides
Feb 20  lecture on human perception continued
Feb 27
Lecture 6: Nonlinear dimensionality reduction, part 1. Lecture material: lecture slides
Mar 6 continuetion of lecture 6.
Mar 13 Lecture 7: Nonlinear dimensionality reduction, part 2. Lecture material: lecture slides
Mar 20 Lecture 8: Nonlinear dimensionality reduction, part 3, and Exercise meeting. Lecture material: lecture slides
Mar 27 Lecture 9: Metric learning. Lecture material: lecture slides
Apr 3 Lecture 10: Neighbor embedding, part 1. Lecture material: lecture slides
Apr 10 Lecture 11: Neighbor embedding, part 2. Lecture material: lecture slides
Apr 17 Lecture 12: Graph visualization. Lecture material: lecture slides
Apr 24 Lectures 11-12 continued
May 2 at 12-14 in room B1083 Lecture 13: Dimensionality reduction for graph layout. Lecture material: lecture slides
May 7 at 12-14 in room B0020 Extra lecture: recap for course material, discussion of exercise packs
May 15 at 14-18 in room B0020 First exam

Exercise Packs

Exercise packs will be released during the course. They can be completed using e.g. Octave, Matlab, or R.

  • Exercise pack 1: get the files here (username and password required, they have been sent to course participants). Return deadline: by end of March 26, 2018, Finland time.
  • Exercise pack 2: get the files here (username and password required, they have been sent to course participants). Return deadline: by end of June 6, 2018, Finland time.

About Octave, Matlab, and R:

Octave (GNU Octave) is a free software that is very similar in operation to Matlab, and is available for several systems including Windows, Linux, and Mac OS X. For Linux it is likely available in the software repository of your distribution such as Ubuntu Software Center; for Windows download it through the download page; for Mac OS X there are various alternatives, the easiest is a slightly older version at SourceForge.

Several tutorials are available online about programming in Matlab and programming in Octave. If you are familiar with R, Prof. David Hiebeler (University of Maine) has written a useful Matlab/R reference that tells how the same operations are done in both languages.

R is a software for statistical computing, also available for Windows, Linux, and Mac OS X. For Linux it might be already installed (check with "which R") or is likely available in the software repository of your distribution such as Ubuntu Software Center. For Windows and Mac OS X download it through one of the many CRAN mirror sites.

There are a large amount of R tutorials available online (e.g. this one). If you are familiar with Matlab or Octave, Prof. David Hiebeler (University of Maine) has written a useful Matlab/R reference that tells how the same operations are done in both languages.

 
Ylläpito: mtt-studies@sis.uta.fi
Muutettu: 11.5.2018 7.44 Muokkaa

Tampereen yliopisto

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kirjaamo@uta.fi


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