ACIT4045 Projects in Human Computer Interaction Course description

The digitalization of medical and health care systems depends on medical devices with perception (i.e., sensing) and control (i.e., actuation) capabilities. To do their work, sensors and actuators require a transduction mechanism, such as that often provided by a mechanical element in the physical sensors. Generally stated, the transduction mechanism converts nonelectrical parameters to electrical ones in a calibrated way.

This course covers the definitions and structure of the sensors and actuators with a focus on medical and health care applications. In addition, the course introduces analytical methods and tools for multivariate calibration and evaluation of sensors and actuators. 

• 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

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

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.

The following coursework must be approved before the student can attend the exam:

• Semester exercise in a group of 3-4 students, resulting in a report between 7500 and 15000 words. The total working load will be approx. 60 hours per student. 

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.

Exam in two parts: 

1)     Group project report (3000-5000 words per student). The project report counts as 80% of the final grade. 

2)     Individual project presentation of student's contribution to the project (10 minutes). The oral examination counts as 20% of the final grade 

Both 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 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.

Professor Anis Yazidi

Two internal examiners. External examiner is used periodically.