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Data Mining II

Important: This is the Web page of the course which took place in FSS 2019. The Web page of the most recent course can be found here.

Building on the Data Mining fundamentals course, this course deepens the theory and practice of advanced data mining topics, such as:

Data Preprocessing
Regression and Forecasting
Dimensionality Reduction
Anomaly Detection
Time Series Analysis
Parameter Tuning
Ensemble Methods
Deep Learning

The course consists of a lecture together with accompanying practical exercises as well as student team projects. In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets.

Like in the previous years, participants will take part in the annual Data Mining Cup (DMC), an international student competition in data mining, as part of the project work. In addition to the DMC submission, the approaches and results of the project have to be compiled into a written project report, and presented in a plenary session.

Time and Location

Lecture:

Tuesday, 13.45 - 15.15, EO 145 (Schloss Ehrenhof Ost / castle) <- changed!

We'll have two alternatives for the exercise:

Exercise: Monday, 10.15 - 11.45, A 5, 6, C012
Exercise: Monday, 12.00 - 13.30, A5, 6, C015

Both of these dates are offered, and you have to decide for one.

Instructors

Final exam

Unlike in the previous years (and unlike, e.g., Data Mining 1), the project is not graded. Your final grade will be based solely and entirely on the final exam.

The exam review for the first exam from FSS2019 will take place on Monday, August 19th, at 8am in B6 C1.01.

The exam review for the second exam from FSS2019 will take place on Thursday, September 19th, at 10am in B6 C1.01.

Slides and Excercises
Slides

19.02.: Organization, Data Preprocessing

26.02.: Regression

05.03.: Anomaly Detection

12.03.: Ensembles

19.03. Time Series

26.03. Neural Networks and Deep Learning

02.04. Parameter Tuning, Introduction to the Data Mining Cup

Exercises

25.02.: Worksheet

04.03.: Worksheet, Data

11.03.: Worksheet, Data

18.03.: Worksheet, Data

25.03.: Worksheet, Data

01.04.: Worksheet, Data
Participation
The course is open to students of the Master Business Informatics, Master Data Science, and Lehramt Informatik.

Registration is done via Portal2.

In case there are more registration than places (64), places will be allocated automatically by Portal2.

Note:Since the introduction to team projects takes place on February 12th during the lecture slot, and some of you may want to attend, we'll start the lecture in the second week, i.e., February, 18th. The exercises will then begin on February, 25th.

Week	Topic
11.2.	No Lecture (due to introduction of team projects)
18.2.	Organization, Preprocessing
25.2.	Regression
4.3.	Anomaly Detection
11.3.	Ensemble Methods
18.3.	Neural Networks
25.3.	Time Series
1.4.	Parameter Tuning
8.4.	DMC intermediate presentation
15.4.	Easter Break
22.4.	Easter Break
29.4.	DMC intermediate presentation
6.5.	DMC intermediate presentation
13.5.	DMC final selection

Timeline Data Mining Cup:

Team registration: from March 5, 2019
Task is announced: April 4, 2019
Deadline for submissions: May 16, 2019
Presentation & award ceremony: July 3, 2019

Literature
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.

Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.

Bing Liu: Web Data Mining, 2nd Edition, Springer.

Further literature on specific topics will be announced in the lecture.
Software
We will use Python with a number of different packages (scikit-learn, etc.), which will be announced during the exercises.

You are invited to work with other tools (RapidMiner, R, etc.) if you like.
Lecture Videos
Video recordings of the Data Mining II lectures are available here (accessible from within the university network or VPN).

Data Mining II

Time and Location

Instructors

Final exam

Slides and Excercises

Participation

Outline

Literature

Software

Lecture Videos

Data and Web Science Group