This course is from a past or future semester. If you are looking for current courses, go to the course overview.

Machine Learning and Data Mining

Winter Term 2015 / 2016

Welcome to the website of Machine Learning and Data Mining course.

On this page, we want to provide you with the most important information and course material for the course. Here, we publish slides for lectures and tutorials, exercises and home assignments. You can simply sync this Github repository for always being up-to-date and having access to all course materials!

Lectures

Lectures take place every week on Wednesdays, 10 a.m. in room H 010. The following agenda is a first layout and may change during the course.

28.10. Introduction & data mining process: Slides (.ppt) - Slides (.pdf)
04.11. Clustering I: task, distance measures, k-means, m-medoids: -
11.11. Clustering II: EM-Algorithm, density-based clustering: -
18.11. Clustering III: hierarchical clustering, other methods: -
25.11. Classification I: task & evaluation, Naive Bayes
02.12. NO COURSE
09.12. Classification II: nearest neighbor, decision trees: Slides
11.12. Classification III: Ensemble learning, random forests (time slot of tutorial): Slides
16.12. Classification IV: Support vector machines: Slides (.pptx) - Slides (.pdf)
13.01. Association rule mining: Slides (.pptx) - Slides (.pdf)
20.01. Subgroup discovery: Slides (.pptx) - Slides (.pdf)
27.01. Matrix factorization (PCA-SVD-Text Mining): Slides (.ppt) - Slides(.pdf) - additional Slides (LSA)
03.02. Sequential data Slides (.odp) - Slides (.pdf)
10.02. Bayesian learning Slides (.odp) - Slides(.pdf)
24.02. Final exam, Room D 239, 10.15 am

Tutorials

Tutorials take place about every two weeks on Friday, 12.00h in room M 001. The exact dates are:

28.10. Basic statistics and data preparation: - -
20.11. Statistics, K-Means, ... Tutorial slides (odp) Tutorial slides (pdf)
27.11. ROC curve Tutorial slides (pptx) Tutorial slides (pdf)
18.12. Tutorial slides (odp) Tutorial slides (pdf) Reddit test Reddit train
15.01. Exercise Task
29.01.

Assignments

Home assignments are mandatory and to be solved in groups of 2-3 participants. You must complete at least 5 of the 6 home assignments to be admitted to the final exam.

1. Home Assignment: Solution
Submit at latest by 10th of November
2. Home Assignment: Solution
Submit at latest by 25th of November
3. Home Assignment: Task Solution
Submit at latest by 16th of December
4. Home Assignment: Task Reddit test Reddit train
Submit at latest by 14th of January
5. Home Assignment: Task
Submit at latest by 27th of January
6. Home Assignment: Task Solution
Submit at latest by 15th of February

Exam

Recommended Literature

T. Mitchell: "Machine Learning", 1997
J. Han, M. Kamber, J. Pei: "Data Mining: Concepts and Techniques", 2011
I. Witten, E. Frank, M. Hall: "Data Mining: Practical Machine Learning Tools and Techniques", 2011
C. Bishop: "Pattern Recognition and Machine Learning", 2008
M. Ester, J. Sander: "Knowledge Discovery in Databases: Techniken und Anwendungen", 2013 (german language)

More literature can be found in the lecture slides