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

Lectures are given Tuesdays 12-14 in Pinni B2077 starting Jan 10 (note the first lecture is in B0020 instead), see the preliminary lecture schedule below. Lecturer: associate professor Jaakko Peltonen.

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.

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).

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.

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.

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 10 in B0020 | Lecture 1: Introduction, properties of high-dimensional data. Lecture material: lecture slides |

Jan 17 | Lecture 2: Feature selection. Lecture material (preview version): lecture slides |

Jan 24 | Lecture on feature selection continued, using the above slides. |

Jan 31 | Lecture 3: Linear dimensionality reduction. Lecture material (preview version): lecture slides |

Feb 7 | lecture on linear dimensionality reduction continued |

Feb 14 | Lecture 4: Graphical excellence. Lecture material (preview version): lecture slides |

Feb 21 | Lecture 5: Human perception. Lecture material (preview version): lecture slides |

Wed Feb 29, 16-18 in room B1065 |
Lecture 6: Nonlinear dimensionality reduction, part 1. Note the new date and time! Lecture material: lecture slides |

Mar 7 | Lecture 7: Nonlinear dimensionality reduction, part 2. Lecture material: lecture slides |

Mar 14 | continuation of lecture 7 |

Mar 21 | Lecture 8: Nonlinear dimensionality reduction, part 3, and Exercise meeting. Lecture material: lecture slides |

Mar 28 | Lecture 9: Metric learning. Lecture material: lecture slides |

Apr 4 | Lecture 10: Neighbor embedding, part 1. Lecture material: lecture slides |

Apr 11 | Lecture 11: Neighbor embedding, part 2. Lecture material: lecture slides |

Apr 18 | Lecture 12: Graph visualization. Lecture material: lecture slides |

Apr 25 | Lectures 11-12 continued |

May 2 | Lecture 13: Dimensionality reduction for graph layout; recap and further details for previous lectures. Lecture material: lecture slides |

May 19, 14-18, in B2077 | First exam |

September 1, 10-14, in D10AB | Second exam. Note: this is a "regular exam", you must enroll using NettiOpsu. |

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

- Exercise pack 1, original deadline March 31, 2017.
**Updated deadline: April 6, 2017, except problem B3 can be returned until end of April 11, 2017.** - Update March 30, 2017: the template file used in problem B3 is available here for the Matlab/Octave language and for the R language.
- Update March 30, 2017: in problem B5, "Note that for OlivettiFaces you need the multi-class case of LDA" should read "Note that for the white wines data you need the multi-class case of LDA"
- Exercise pack 2,
**extended deadline June 29, 2017 at 12 noon**.

**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.

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