BLH3010 Practice and Study Placement at Institutions Outside Norway Course description

Praksis- og studieopphold ved en institusjon utenfor Norge finner sted i sjette semester, og varer i tre måneder fra medio januar. Praksis- og studieoppholdet vil som hovedregel innebære en lengre praksisperiode ved minst to ulike barnehager, et kortere studieopphold ved en barnehagelærerutdanningsinstitusjon i samme land/region, samt én uke til å reise i vertslandet/regionen. Før utreise deltar studenten i faglige forberedelser, og under oppholdet vil det også være faglige arbeidsoppgaver i tilknytning til praksisoppholdet. Et slikt praksis- og studieopphold byr på muligheter for økt bevisstgjøring og handlingskompetanse i forhold til interkulturell kommunikasjon, etisk refleksjon og flerkulturell pedagogikk. Oppholdet bidrar også til personlig og faglig bevisstgjøring om egne verdier og eget pedagogisk ståsted i forhold til syn på barn, lek, læring og voksen-barn relasjoner.

Emnet erstatter kunnskapsområdet Ledelse, samarbeid og utvikling (15 studiepoeng).

Målgruppe

Studenter i barnehagelærerutdanningen som skal ha et praksisopphold utenfor Norge som del av sitt bachelorprogram.

This course provides a broad introduction to machine learning (ML), which includes supervised, unsupervised, and reinforcement learning, and deep learning (DL) that can be used in different application domains. Students will learn both theories and practices in ML and DL. Moreover, students will learn from studying, presenting, and discussing relevant research articles and expose themselves to research by doing a research project.

No formal requirements over and above the admission requirements.

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as CNN, RNN, GAN, RL.
• has a good understanding of both theoretical and practical know how required to use machine and deep learning methods effectively

Skills

The student:

• develop practical skills necessary to build, train, and deploy machine learning and deep learning models
• is able to analyze machine learning methods in regard to their performance and effectiveness
• is able to use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine and deep learning methods
• can discuss relevance, strength and limitations of machine learning and deep learning in solving real world problems
• is able to work on relevant research projects

The course consists of lectures, assignments, presentation seminars, and project work. Students will actively participate in the seminars by presenting papers, listening, and discussing on other presentations. The aim of the course is to provide a research-oriented education in the field. Students will do research projects with the aim of cultivating them towards good future researchers.

Practical training

None.

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

• Two oral presentations (one on a given topic, one on the topic of own choice)
• Participate as a prepared opponent/discussant in two presentations from other students.

The assessment will be based on:

• A group project: implementation and report (about 7000 words).
• Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

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

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 for the project report.

No aids are permitted for the oral exam.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.