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.

Three individual and four group requirements must be passed in order to take the exam. Aesthetics and special needs are should to play an important part in the coursework requirements.

Individual:

1. Storytelling
2. Theory presentation
3. Participation in excursions
4. Participation in International Week

In group:

1. Production of a drama
2. Production of radio theatre
3. Production of stop-motion animation
4. Blog production
5. Practical work in connection with International Week

Coursework requirements must be met by the deadlines. Coursework requirements are evaluated as passed/not passed.

Valid absence documented by a medical certificate or similiar is not an excuse for not meeting the coursework requirements. Students who, due to illness or other valid and documented reasons, do not meet the coursework requirements by the deadlines, may be given longer deadlines. A new deadline for meeting the coursework requirements is agreed upon with the relevant teacher in each case.

Students who meet the coursework requirements by the deadline, but are evaluated as Not Passed, shall be given another attempt to meet the coursework requirements one more time. A new deadline for meeting the coursework requirements is agreed upon with the relevant teacher in each case.

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

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

The exam consists of two parts:

Part 1: Aesthetic production in group for children and young people with special needs

Part 2: Individual reflection paper on the aesthetic production (approx. 3000 words)

New resit or rescheduled exams

If a student fails the exam or is absent at the time of the exam for a valid reason, the student is entitled to resit the exam the following semester. The resit exam will be organised in the same manner as the ordinary exam, but the group exam will be individual. The regulations on resit and rescheduled exams are set out in the Regulations relating to studies and examinations at OsloMet. Students must register for a resit or rescheduled exam.

Assessment criteria for the grade scale A- F

A: Outstanding: The assignment answer documents outstanding subject knowledge and ability to use it in a cross-disciplinary and educational context outstanding degree of independence and ability to relate subject knowledge to what is asked for in the assignment text outstanding ability in written exposition

B: Very good: The assignment answer documents very good subject knowledge and ability to use it in a cross-disciplinary and educational context very high degree of independence and ability to relate subject knowledge to what is asked for in the assignment text very high ability in written exposition

C: Good: The assignment answer documents good subject knowledge and ability to use it in a cross-disciplinary and educational context high degree of independence and ability to relate subject knowledge to what is asked for in the assignment text high ability in written exposition

D: Fairly good: The assignment answer documents fairly good subject knowledge and ability to use it in a cross-disciplinary and educational context reasonable independence and ability to relate subject knowledge to what is asked for in the assignment text reasonable ability in written exposition

E: Adequate:The assignment answer documents adequate subject knowledge and ability to use it in a cross-disciplinary and educational context adequate independence and ability to relate subject knowledge to what is asked for in the assignment text adequate ability in written exposition

F: Not passed: The assignment answer does not satisfy minimum requirements

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.

None.

Group project (2-4 students) between 5 000 and 7 000 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.

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

Aesthetics and Special Needs is one of the courses under Nordic Childhoods. It focuses on Nordic culture where nature plays an important role in society, aesthetics and in work with children. Joint events with the other Nordic Childhoods courses, are integrated as overnight trips and outdoor excursions. We also visit schools, kindergartens and other institutions. We focus on play, learning by doing, experience and workshops.

Aesthetics has not been a common way of approaching children with special needs. This course tries to change this by focusing on the value of aesthetics for children with special needs. The course is interdisciplinary, and takes a holistic approach to the field.

Aesthetics are important for everybody, also including children and young people with special needs. Through literature, storytelling, music, dance, drama and other aesthetic acts we perceive and understand the world and ourselves. Through aesthetics we perform and collaborate with others. For some children and young people lack verbal language; have problems with emotions, communication and interaction or find it hard to take other perspectives; aesthetic communication becomes is particularly important. The aim of this course is

1. to enhance knowledge about and discuss the relationship between children and young people with special needs and aesthetic expressions,
2. to introduce ways of working in practise with different kinds of aesthetic expressions when targeting various kinds of special needs.

The course is interfaculty and explores different disciplines, and ways of conceptualising and practicing aesthetics with respect to a variety of special needs.

After completing the course, the student should have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

The student

• has knowledge of aesthetics and different aesthetic approaches
• has knowledge of historical changes in the views on children and young people, normality and deviance, inclusion and exclusion

Skills

The student is able to

• work creatively with aesthetics and children and young people with special needs
• reflect on work with children and young people with special needs
• communicate with children and young people through aesthetics

General competence

The student

• has an understanding of aesthetics and its value to children and young people with special needs
• has an understanding of the value of perspectives from different subjects, professions, trades, arts and sciences
• is able to analyze and discuss how to understand and respond to children and young people with special needs

• Literature studies/theories
• Lectures and supervision
• Excursions, workshops and seminars
• Individual and group papers/performances
• Self-study
• Project work
• Digital blog
• Dramatisation
• Stop-motion animation
• Radiotheater
• Storytelling
• Visits and practice in school, kindergartens and other institutions
• Interactive learning methods, including digital media