ACIT4020 Robotics and Control Project Course description

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.

Student's scientific background should comply with the the admission requirements of the robotics and control specialization. It is highly recommended that the student has previous knowledge in mathematics, programming, robotics and control. It is recommended that the student has taken the courses ACIT4810 Advanced Methods in Modelling, Simulation, and Control, ACIT4820 Applied Robotics, and Autonomous Systems and ACIT4830 Special Robotics and Control Subject.

None.

Group project (2-4 students);(15 000 - 17 500 words)

The exam can be appealed.

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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.

A handheld calculator that cannot be used for wireless communication or to perform symbolic calculations. If the calculator’s internal memory can store data, the memory must be deleted before the exam. Random checks may be carried out.

Grade scale A-F

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of this course the student

• has advanced knowledge of multimodal user interfaces
• has advanced knowledge of input and output technologies
• can analyse problems and issues in interactions related to context, such as accessibility in public spaces, mobility problems, and the user's affective state
• can use knowledge of interaction technology to address new problems in universal design of ICT

Skills

On successful completion of this course the student

• can independently use appropriate methods of user centred interaction design and evaluation; both heuristic and automatic, in an independent manner
• can analyse and critically deal with the results from relevant research literature, apply these to structure and formulate scientific arguments, and assess the suitability of published results on new problems and issues
• can carry out independent, limited research or development projects under supervision and in accordance with applicable ethical standards
• can present scientific work orally
• can debate and conduct scientific discussions

General competence

On successful completion of this course the student

• can apply knowledge and skills in interaction technology on new problems and issues for carrying out advanced facilitation tasks and projects
• can communicate scientific problems, analysis and conclusions in the field to both specialists and the general public
• can contribute to original thinking and innovation processes

Professor Peyman Mirtaheri

• Two individual oral presentations of research articles (45 min per presentation including questions).
• Being opponent against two student presentations.

• One written grop project report (2000-3000 words) in group of 1-2 students. This part of the examination counts 35 % of the final grade.
• One written group project report (4000-5000 words) in group of minimum 4 students. This part of the examination counts 35 % of the final grade.
• Individual oral examination (20 minutes for each candidate). The oral examination counts 30% of the final grade.

All exams must be passed in order to pass the course.

The oral examination cannot 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.

• 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