EPN-V2

ACIT4530 Data Mining at Scale: Algorithms and Systems Emneplan

Engelsk emnenavn
Data Mining at Scale: Algorithms and Systems
Studieprogram
Master's Programme in Applied Computer and Information Technology
Omfang
10.0 stp.
Studieår
2023/2024
Pensum
VÅR 2024
Timeplan
Programplan
Emnehistorikk

Innledning

  • Data driven dynamic modelling (system identification)
  • State estimation
  • Multivariable control algorithms
  • Predictive control algorithms
  • Simulation and control of dynamic systems

Anbefalte forkunnskaper

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

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

Forkunnskapskrav

No formal requirements over and above the admission requirements.

Læringsutbytte

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

  • has a deep understanding of how data mining can be used to extract knowledge from data sets.
  • has advanced knowledge of the different data mining algorithms

Skills

Upon successful completion of the course, the student:

  • can design and implement data mining algorithms
  • can deploy different data mining systems and configure them
  • can utilize a specialized library for data mining

General competence

Upon successful completion of the course, the student:

  • can use data mining systems to mine data
  • can analyse data mining solutions with regard to robustness and in relation to his/her intended tasks
  • can explain how data mining can be used in different applications areas such as business analytics

Innhold

  • 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

Arbeids- og undervisningsformer

This course is divided into two parts. The first part with focus on covering the principles of data mining and stream processing. Different seminars will be given on the different methodological aspects of data mining and stream processing as well as the programming paradigms and software tools that enable them.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work in a group on the project and submit a final code-base with a report.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

Arbeidskrav og obligatoriske aktiviteter

None.

Vurdering og eksamen

Group project (2-4 students) between 15 000 and 17 500 words

The exam 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.

Hjelpemidler ved eksamen

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Vurderingsuttrykk

Grade scale A-F.

Sensorordning

Two internal examiners. External examiner is used periodically.

Emneansvarlig

Professor Anis Yazidi