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Machine Learning and Data Mining

Welcome to the Machine Learning and Data Mining course of winter terms 2018/2019!

If you want to participate, (1) register to the lecture and one tutorial in Klips and (2) add yourself into a group for working on the mandatory assignments (information under the assignment headline below). For inter-student communication, please use the newsgroup infko-mldm here.


Lecture and Tutorial Machine Learning and Data Mining (6 ECTS; for Master and Bachelor students in Web Science, Computer Science, Computational Visualistics and Business Informatics). The lecture starts at 8:30 AM. The first tutorial on Thursday starts at 2:15 PM, the second at 4:00 PM. The tutorial on Friday starts at 10:15 AM.

Video Lectures

Lecture recordings are found here.

Course Material

Date Lecturer Lecture Topic Slides
24.10.2018 Staab Introduction PPTX, PDF
31.10.2018 Menges Optional: Python Tutorial PDFsampledata.csv, working CheatSheetLink
07.11.2018 Staab Classification PPTX, PDF
14.11.2018 Staab Classification continued  
21.11.2018 Staab Decision trees PPTX, PDF
28.11.2018 Staab Random forest PDF
05.12.2018 Staab  Support Vector Machines PPTXPDF
12.12.2018 Staab Feed Forward Networks PPTX, PDF
19.12.2018 Menges / Ramadan Optional: Test Exam  
26.12.2018   Vacation
02.01.2019   Vacation
09.01.2019 Staab    
16.01.2019 Staab    
23.01.2019 Staab    
30.01.2019 Staab    
06.02.2019 Staab    


Date Tutorial Topic Slides Lecturer
25./26.10. Tutorial and assignment structure, groups and SVN introduction, first look at assignment 01 tutorial01.pdf Qusai & Raphael
1./2.11. No Tutorial    
08./09.11. Discussion of assignment 01, preview of assignment 02 Blackboard Qusai
15./16.11. Discussion of assignment 02, preview of assignment 03 Blackboard Raphael
22./23.11. Discussion of assignment 03, preview of assignment 04 Blackboard Qusai
29./30.11. Discussion of assignment 04, preview of assignment 05 Hint for Assignment 05 Raphael
06./07.12. Discussion of assignment 05, preview of assignment 06 Blackboard Qusai
13./14.12. Discussion of assignment 06, preview of assignment 07 Blackboard Raphael
20./21.12 Discussion of assignment 07 Blackboard Qusai
10./11.01. Discussion of test exam, preview of assignment 08 Blackboard Raphael
17./18.01. Discussion of assignment 08, preview of assignment 09 Blackboard Qusai
24./25.01. Discussion of assignment 09, preview of assignment 10 Blackboard Raphael
31.01./01.02. Discussion of assignment 10, preview of assignment 11 Blackboard Qusai
07./08.02. Discussion of assignment 11 Blackboard Raphael


Please form groups of three people to work on the assignments here, until 28th of October! The assignments are graded before the next tutorial and it is mandatory to reach 60% of the points in total over all assignments to be allowed to participate in the exam. E.g., if there are 10 assignments each 10 points, you need in total at minimum 60 points in sum over all assignments to participate in the exam.

Release Date Assignment Submission Deadline at 9:00AM Sheets
24th October Pen and Paper: Machine Learning Fundamentals 5th November assignment01.pdf
5th November Programming: Machine Learning Fundamentals 12th November assignment02.pdf
10th November Pen and Paper / Programming: k-Nearest Neighbors 19th November assignment03.pdf
Remarks: For both tasks, use kNN with default rule. In task 2, use micro-average precision.
17th November Pen and Paper: Naive Bayes 26th November assignment04.pdf
25th November Programming: Naive Bayes 3rd December assignment05.pdf
Update of PDF (sharpening terminology and fix of pointers to slides)
1st December Pen and Paper: Decision Tree 10th December assignment06.pdf
8th December Programming: Decision Tree 17th December assignment07.pdf
--- Test Exam --- To be released after text exam. No submission via SVN, not counting into assignment nor final exam grading.
January Pen and Paper: SVM TBD Coming soon.


First Exam: 20.02.2019, 8:15, D028. Registrations open on 23.01.2019, registration deadline is on 13.02.2019, deregistration closes on 18.02.2019.

Information for students from the previous semster about taking the exam of this semester: 
If you had qualified for the exam in the previous semester and participated in an exam that you failed, you may just register for the first exam of this semenster without renewing your qualification.
If you had qualified for the exam and did not participate in an exam in the previous semester, you must renew your qualification to take an exam this semester by participating in an assignment group and gaining a sufficient amout of points.


Below some pointers to related literature:

Prof. Dr. Steffen Staab

Raphael Menges