Programplaner og emneplaner - Student
ACIT4530 Data Mining at Scale: Algorithms and Systems Course description
- Course name in Norwegian
- Data Mining at Scale: Algorithms and Systems
- Study programme
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Master's Programme in Applied Computer and Information Technology
- Weight
- 10.0 ECTS
- Year of study
- 2025/2026
- Programme description
- Course history
-
Introduction
We are witnessing the era of Big Data where data is generated, collected, and processed at an unprecedented scale and data-driven decisions influence many aspects of modern life.
Data mining is the process of discovering patterns in large data sets involving methods in statistics and database systems. A large number of applications such IoT sensors generate large amounts of data streams. The necessity of data stream mining and learning from the data is increasingly becoming more prevalent and urgent.
Extracting knowledge from data sets requires not only computational power but also programming abstractions as well as analytical skills. In this course, the students will be exposed to the different approaches for data mining and stream processing such as association rules learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. The students will be exposed to concrete data mining and neural network architectures including deep learning models for handling large data streams such as convolutional neural networks, recurrent neural networks, autoencoders, transformers and attentions. In this course, the student will also be exposed to different data mining systems, working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.
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
No formal requirements over and above the admission requirements.
Learning outcomes
Internal sensor. External sensors are used regularly.
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
None beyond general admission requirements
Course requirements
None.
Assessment
Group project (2-4 students) between 15 000 and 17 500 words. The group receives the same grade.
Students can work on a project individually at the discretion of the course coordinator (only in exceptional cases).
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.
Permitted exam materials and equipment
All aids are permitted, provided the rules for plagiarism and source referencing are complied with.
Grading scale
Grade scale A-F.
Examiners
One internal examiner. External examiners are used periodically.
Course contact person
Professor Anis Yazidi