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 42032
Type Course
Site Neuchâtel
Track(s) T4 – Theory and Logic
T6 – Data Science
Semester A2023


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) Christos Dimitrakakis
Language english
Course Page

The course page in ILIAS can be found at

Schedules and Rooms

Period Weekly
Schedule Tuesday, 08:45 - 12:00
Location UniNE, Unimail
Room B104

Additional information


First Lecture
The first lecture will take place on Tuesday, 26.09.2023 at 08:45 in UniNE, Unimail, room B104.