ACIT4530 Data Mining at Scale: Algorithms and Systems Course description

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.

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

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

The exam in the course is an individual course paper. The paper must have a scope of 10-15 pages (in addition to front page, table of content and list of references). Font and font size: Arial/Calibri 12 points. Line spacing: 1.5.

The exam can be answered in English or in a Scandinavian language (Norwegian, Danish, Swedish).

Students awarded a fail grade are given one opportunity to submit an improved version of the assignment for assessment.

All aids are permitted, as long as the rules for source referencing are complied with.

Grade scale A-F

The exam papers are assessed by one internal and one external examiner.

At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.

Group project (2-4 students) (15 000 - 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 applying 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.

All aids are permitted.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Professor Anis Yazidi