EPN-V2

ACIT4530 Data Mining at Scale: Algorithms and Systems Course description

Course name in Norwegian
Data Mining at Scale: Algorithms and Systems
Study programme
Master's Programme in Applied Computer and Information Technology
Weight
10.0 ECTS
Year of study
2023/2024
Curriculum
SPRING 2024
Schedule
Programme description
Course history

Introduction

The course consists of lectures (theory), labs (practical exercises and computer simulations/experiments), group discussions, Q&As, as well as group projects. The group projects will be assigned from a list of the suggested topics/areas. The students will work in groups and finally submit the project report as well as the code. 

Practical exercises: Lab and Q&A sessions.

Recommended preliminary courses

It is an advantage to have some experience with the following subjects:

  • Mathematical Analysis
  • Basic programming, such as scripting
  • Statistics, specifically probability theory

Required preliminary courses

None

Learning outcomes

The final exam consists of two parts:

  • Part 1 - Group project report with code: A group (2-4 students) project implementation, including a project report (5000 - 7000 words, excluding references) and code as an appendix (counts 50% towards the final grade). Both the code and the report will be evaluated. The comprehensiveness of the code is evaluated under the assumption that each member of the group has worked on the project for 60 hours. 
  • Part 2 - Individual written exam: An individual, closed-book, written exam (3 hours) (counts 50% towards the final grade)

Both parts must be passed in order to pass the course (i.e., if a student fails in one part, he or she would automatically fail the course). 

The exam results can be appealed.

 

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Content

  • Data mining systems
  • Data mining and machine learning algorithms
  • Deep learning and neural networks for datamining
  • Data stream processing methods, such as, but not limited to, anomaly detection, clustering, association rule learning
  • Distributed reinforcement learning for data mining.
  • Data visualization

Teaching and learning methods

All aids are permitted for the group project, provided the rules for plagiarism and source referencing are complied with (Exam - Part 1).

For the closed-book, individual written exam (Exam - Part 2), students will work on a computer in an exam room (with invigilators), can use pen and a simple, non-programmable calculator, but will not have access to Internet, books, notes or other aids.

Course requirements

None.

Assessment

Grade scale: A-F.

Permitted exam materials and equipment

Two examiners. External examiner is used periodically.

Grading scale

Professor Jianhua Zhang

Examiners

It is recommended that students have some background knowledge in:

1) mathematics: calculus, linear algebra, statistics and probability theory, and numeric optimization

2) programming language in Python, Matlab or R

3) machine learning and/or data mining.

Course contact person

Professor Anis Yazidi