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.


Code 32032
Type Course
Site Neuchâtel
Track(s) T3 – Advanced Information Processing
T6 – Data Science
Semester A2022


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.

Lecturer(s) Jacques Savoy
Language english
Course Page

The course page in ILIAS can be found at

Schedules and Rooms

Period Weekly
Schedule Monday, 08:45 - 12:00
Location UniNE, Unimail
Room B013


Evaluation type written exam

Additional information


First Lecture
The first lecture will take place on Monday, 26.09.2022 at 08:45 in UniNE, Unimail, room B013.