Information Retrieval and Data Mining

6 ECTS credits
150 h study time

Offer 1 with catalog number 4020567FNR for all students in the 2nd semester at a (F) Master - specialised level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Taught in

English

Faculty

Faculty of Sciences and Bioengineering Sciences

Department

Computer Science

Educational team

Pieter Libin (course titular)

Activities and contact hours

24 contact hours Lecture
24 contact hours Seminar, Exercises or Practicals
36 contact hours Independent or External Form of Study

Course Content

Expected background knowledge:
For this course, we expect the students to have a decent knowledge of basic machine learning algorithms (e.g., Machine learning course) and an understanding of the principles of scalable data management systems (e.g., Scalabale data management course).

Content:
In this course, we study scalable machine learning methods to analyze big datasets. We aim to give students a large overview of the big data ecosystem, yet also make it concrete so that the students understand the algorithms on a fundamental level. Additionally, we will guide the students in applying such algorithms to simplified instances of real-world problems.
We will cover the following aspects:
- Big Data ecosystem
- Complexity analysis for scalable computations
- Distributed supervised/unsupervised machine learning
- Recommender systems
- Large graph analysis
- Scalable deep learning
- Online learning / active learning
- Frameworks for distributed computing

Additional info

Study material: Scientific papers, course slides and Canvas notes.

Learning Outcomes

General competencies

Understand the big data ecosystem, how to store and query big data sources.
Understand core scalable machine learning algorithms on a fundamental level.
Be capable of adapting existing scalable machine learning algorithms.
Understand and be able to apply frameworks for distributed computing.
Apply the machine learning techniques on simplified instances of real-world problems.
Show independence working on a real-world problem, reporting on the algorithms used and the results obtained.

Grading

The final grade is composed based on the following categories:
Written Exam determines 50% of the final mark.
Practical Exam determines 50% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

written exam with a relative weight of 1 which comprises 50% of the final mark.

Note: Written exam: 50% of the final grade.

Within the Practical Exam category, the following assignments need to be completed:

personel project with a relative weight of 1 which comprises 50% of the final mark.

Note: Personal project: 50% of the final grade.

Additional info regarding evaluation

Written exam: 50% of the final grade.
Personal project: 50% of the final grade.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context