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Machine Learning and Data Mining

Basic Course Information

Lecture - Machine Learning and Data Mining

Course number: 0432028

Lecturers: Prof. Dr. Markus Strohmaier
Dr. Florian Lemmerich
Dr. Philipp Singer
Dates: Wed 08.30-10.00
Room: KO Gebäude G - G 410
Room changed!

Exercise - Machine Learning and Data Mining

Course number: 0432028

Dozent(in) Prof. Dr. Markus Strohmaier
Dr. Philipp Singer
Dr. Florian Lemmerich
Evgeniy Vasilev
Dates: Wed 10.15-11.45
Room: KO Gebäude G - G 410
Room changed!

Lecture schedule:

This schedule might be subject to changes. Materials are for use in this course only and may not be redistributed.
 

Lecture #

Lecture Topic

Date

Materials

Comment

1

Overview and Motivation

2016-10-26

slides

 

2

Classification (Task, Evaluation, Nearest neighbor)

2016-11-02

slides

 

3

Classification (Naive Bayes)

2016-11-09

slides

 

4

Classification (Decision Trees)

2016-11-16

slides

 

5

Classification (Ensemble Learning, SVM)

2016-11-23

slides

 

6

Clustering (Task, k-means, k-medoids)

2016-12-07

slides

 

7

Clustering (EM-Algorithm, density-based clustering)

2016-12-14

slides

 

8

Clustering (hierarchical clustering, other techniques)

2016-12-14

slides

 

9

Pattern Mining (Association Rules)

2017-01-11

slides

 

10

Pattern Mining (Subgroup Discovery)

2017-01-18

slides

 

11

Preprocessing / Matrix Factorization (PCA - SVD - NMF)

2017-01-18

slides

 

12

Sequential Data

2017-02-01

slides

 

13

Neural Networks and Deep Learning

2017-02-08

slides

 

14

Summary and Outlook

2017-02-15

slides

 

There will be no lecture at the 30th of November 2016. We will give the omitted lecture in the exercise slot on the 14th of December, instead.

Exercises:

Exercise #

Topic Material
1 Introduction to Python notebook
2 Introduction to scikit-learn notebook 1
notebook 2
3 Naive Bayes notebook
4 Decision Tree
Feature Standardization
pen & paper exercise
notebook 4a
notebook 4b
exercise
exercise_solution
5 Deep Learning notebook_5
dataset

 

Home assignments:

Assignments should be performed in the jupyter notebook, saved and sent to vasilev@uni-koblenz.de, florian.lemmerich@gesis.org, AND philipp.singer@gesis.org with Subject: [ML-Assignment]. You create groups on your own, names of all group participants should be mentioned in the letter. Group size: 2-3 persons. Deadlines are strict!

 

Assignment #

Type Deadline Task, material Example Solution
1 programming Nov 8, 12:00h (noon) notebook & dataset solution
2 pen & paper Nov 22, 23:59h
(extended by 12 hours)
task description solution
3 programming Nov 29, 12:00h (noon) notebook solution
G graded programming Jan 15, 23.59h task & dataset solution
4 pen &paper Feb 7, 23.59h task description solution

 

There will be interview talks for the graded home assignment on 25th of January in room A221 (office of Prof. Strohmaier).
You can find the exact schedule for the interview talks here.

Exam:

We hold the exam on February, 22nd at 08.30h in KO Gebäude E - E 011. Please be on time and bring a student id. You are allowed to bring a non-programmable calculator with you, but no other material.

 

Newsgroup:

You can use our newsgroup to ask any question about course content or course organization. Please also try and help out your fellow students with their issues by answering directly.
https://deliver.uni-koblenz.de/webnews/newsgroups.php?group=infko.mldm

 

Beteiligte: 

Prof. Dr. Markus Strohmaier

strohmaier@uni-koblenz.de

Dr. Florian Lemmerich

florian.lemmerich@gesis.org

Dr. Philipp Singer

philipp.singer@gesis.org