Scalable Analytics

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

Enrollment Requirements

Students must have followed ‘ Machine learning’, before they can enroll for ‘Scalable Analytics'.

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

In this course, we study scalable algorithms to analyze big datasets. We aim to give students a broad 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 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
Frameworks for distributed computing

Additional info

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 a good background on statistics, calculus and linear algebra.

Study material: Scientific papers, course slides, book “Mining of Massive Datasets” (freely available at mmds.org), exercise assignments and solutions (theory and Python code) and Canvas notes.

Learning Outcomes

Algemene competenties

The student can explain the big data ecosystem in his/her own words, and reason about the pro and cons of different solutions in the ecosystem.
The student can implement, explain (in his/her own words) and mathematically analyze core scalable algorithms.
The student can tailor existing scalable algorithms for functional or technical objectives.
The student can independently learn, evaluate, and apply previously unseen algorithms from research literature, with a critical mindset. This will prepare the student to keep up with algorithmic and computational advances, and to prepare for lifelong learning.
The student can apply frameworks for distributed computing and devise architectures based on distributed computing components.
The student can implement and apply scalable algorithms to real-world problems, and derive and report insights from it, in collaboration with peers.
The student can consider and discuss with peers, the many ethical, social, and economical challenges that big data has relevance to, when developing new applications.

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.

To be eligible to take part in the written exam, the student is expected to register for the project.

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