Machine Learning and Data Mining

Introduction to Data Mining perspective; Association rules; Decision trees; Instance-based learning (nearest neighbors); Clustering; Support Vector machines (SVM); Evaluation (Train & Test, Cross-validation, Leaving-one-out); Data Streams.
 

Learning Outcomes: 

The main objectives of this course is to introduce the students to the various techniques and strategies that can be used to

  1. to discover pertinent relationships (or correlations) between variables
  2. to evaluate such relationships and machine learning approaches
  3. to know how to conduct a machine learning process based on available data and to interpret the results.

Practical exercises will complete the theoretical presentation.
 

Type: 
Course
Semester: 
A2018
ECTS: 
5
Lecturer: 
Site: 
N
Code: 
32032
62032
Language: 
english
Period: 
weekly
Schedule: 
Monday: 8:15 - 12:00
Location: 
UniNE, Unimail
Room: 
B013
Comment: 

First Lecture
The first lecture will take place on Monday, 24.09.2018 at 08:45 in UniNE, Unimail, room B013.

ILIAS
The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_1340252.html.