Programplaner og emneplaner - Student
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
- 2024/2025
- Emnehistorikk
-
Innledning
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.
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
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