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 associationrule learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. In this course, the student will also be exposed to different data mining systems including the landscape of MapReduce and the ecosystem it spawned, such as Spark and its contemporaries. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

Pass/fail.

No formal requirements over and above the admission requirements.

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.
• should be able to use data mining systems to mine data.

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 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

Two internal examiners. External examiners are used regularly.

This course is a continuation of Research Theory and Method 1. It elucidates research methods and philosophy of science perspectives of relevance to the distinctive nature of the aesthetic field. The course emphasises the student analysing, assessing and using theoretical and practical elements in his/her own research process. In the course, the student will prepare a proposal for his/her own research design as a basis for the further work on the master’s thesis.

Ingen.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student:

• is capable of applying knowledge of relevant philosophy of science perspectives in a delimited aesthetic field
• is capable of analysing and assessing the theoretical and practical elements of the testing and research process
• is capable of using and taking a critical approach to the use of sources and ethical assessments throughout the; research processes

Skills

The student;

• is capable of using philosophy of science and methodology of relevance to a delimited aesthetic field in an independent manner
• is capable of critically assessing research methodology choices in relation to different research questions
• is capable of research methodology argumentation relating to a delimited aesthetic field
• is capable of assessing and using different sources and research ethical standards in the research process
• is capable of independent practical and theoretical work in the testing and research process

Competence

The student

• is capable of working independently in his/her own field and masters the field's forms of expression
• is capable of communicating with various target groups about professional issues and angles of discussion in the aesthetic fields
• is capable of using knowledge and skills in the aesthetic fields to contribute to new ideas and innovation processes

Teaching methods vary between lectures given by both lecturers and guest lecturers. Students must actively participate in teaching and group work. The master’s programme is based on individual study with participation in groups, lectures and thematic workshops. Emphasis is placed on both theoretical and practical work as forms of study. Teaching will be in both Norwegian and English.;

The following coursework is compulsory and must be approved before the student can take the exam:

·;;;;;;;; Minimum 80% attendance at compulsory teaching activities and seminars

Individual oral presentation with written and visual reflection material.

The exam grade cannot be appealed.

All aids are permitted.