max planck institut

informatik

informatik

Dr. Pauli Miettinen and Dr. Jilles Vreeken

- Please inform the lecturers (tada14@...) whether you plan to attend the first exam
- Deadline for assignment 4 extended to 31 July.
- Final exam will be held on 11 September, re-exam on 1 October
- 4th assignment is out, DL
**31**July 1600 hours - 3rd assignment is out, DL 3 July 1600 hours
- 2nd assignment is out, DL 5 June 1600 hours
- 1st assignment is out, DL 8 May 1600 hours
- The schedule has been added

Thursdays, 14–16 o'clock in Room 014, Building E1 3.

- Tensor factorizations methods in data mining
- Information theory in data mining
- Graph mining

Month | Day | Lecture topic | Slides | Assignments |
---|---|---|---|---|

April | 17 | Intro | ||

24 | Practicalities & where DM is used | 1st assignment given out | ||

May | 1 | No lecture (First of May)
| ||

8 | Intro to tensors | 1st assignment DL, 2nd assignment given out | ||

15 | Tensors in data analysis | |||

22 | Special topics in tensors | |||

29 | No lecture (Ascension day)
| |||

June | 5 | MDL for pattern mining | 2nd assignment DL, 3rd assignment given out | |

12 | Information and Correlation | |||

19 | No lecture (Corpus Christi)
| |||

26 | Iterative Data Mining | |||

July | 3 | Culprits and Islands in Graphs | 3rd assignment DL, 4th assignment given out | |

10 | Redescription Mining | |||

17 | Mining Data that Changes | |||

24 | Wrap-up with Ask-Us-Anything | |||

31 | <no class, just deadline>
| 4th assignment DL | ||

September | 11 | Final exam (oral, E1.3 016) | ||

October | 1 | Re-exam (oral, E1.3 001) |

- 1st assignment, DL 8 May
- 2nd assignment, DL 5 June
- 3rd assignment, DL 3 July
- 4th assignment, DL
**31**July

Students should know the basic ideas of data mining and machine learning, e.g., by successfully taking Information Retrieval and Data Mining or Machine Learning core lectures.

The course has two hours of lectures per week. There are no weekly tutorial group meetings. Instead, the students have to write essays based on the material covered on the lectures and scientific articles assigned to them by the lecturers.

The four assignments are graded in scale of Fail, Passed, and Excellent. You can fail at most one assignment; two failures mean you fail the course. Any assignment not handed in by the deadline is automatically considered failed.

You can earn at most three bonus points by obtaining excellent grades from the assignments. Each excellent grade gives you one bonus point until you have earned the maximum of three bonus points. Each bonus point improves your final grade by 1/3 assuming you pass the final exam. For example, if you have two bonus points and you receive 2.0 from the final exam, your final grade will be 1.3. You fail the course if you fail the final exam, irrespective of your possible bonus points. Failed assignments do not reduce your final grade, provided you are eligible to sit the final exam.

The final exam will be oral and held in E1 3 room 016 on the 11th of September, the exact times per student will be announced later. The re-exam is scheduled for October 1 in E1 3 room 001. The final exam and re-exam will cover all the material discussed in the lectures and from each assignment one topic of the student's choice.

The PDFs require username and password, that are the same as for the assignments.

- Skillicorn, D., 2007.
*Understanding Complex Datasets: Data Mining with Matrix Decompositions*, Chapman & Hall/CRC, Boca Raton. Chapter 9 (PDF) - Kolda, T.G. & Bader, B.W., 2009. Tensor Decompositions and
Applications.
*SIAM Review*51(3), pp. 455–500 (PDF)

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