BLH3250 Fairytales and creativity - Nordic Childhoods Emneplan

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 has:

• knowledge of supervised, unsupervised, reinforcement learning
• good understanding of the principles of state-of-the-art deep neural networks such as convolutional neural networks, sequential models (RNN, LSTM), Transformers, GenerativeAI (Autoencoder, GAN, Diffusion models), and reinforcement learning.
• a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively.

Skills

The student can:

• build, train, test, and deploy machine learning and deep learning models
• analyze machine learning methods in regard to their performance and effectiveness
• 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 learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• can work on effectively relevant research projects

• Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
• Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.
• Knowledge and skills in cloud containerization technologies such as Docker.

The course consists of lectures, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

The course is organised as a full-time programme of study for one semester. The students are expected to work approximately 40 hours per week. The course has the following content:

Theory

· The differences between myths, fairy tales and legends

· Structural theories, psychological theories, eclectic theories

· Myths and religions

· Psychology of importance and meaning in fairy-tales and traditional stories as cultural expressions

· The student's own academic background, cultural identity and stories

· Hero tales and contemporary mass media narratives

Applications

· Cultural exchanges

· Excursions; museums, schools, kindergartens

Stories as background for various artistic expressions

· Puppet making and performing

· Storytelling

· Stop-motion animation

· Dramatizing

· Stories and music

· Digital mediation of myths, fairy tales and legends

Didactic perspective

· The uses of fairy tales in education and therapy

· Didactic reflections on the use of myths and fairy tales

· Practical work with traditional narratives

· Visits to schools and kindergartens

· Literature studies/theory

· Lectures and supervision

· Excursions, workshops, seminars

· Cultural exchanges/discussions

· Individual and group papers/performances

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

• Two group presentations: one on literature studies, the other on the project.
• Participate as a prepared opponent/discussant in two presentations from other students

There is mandatory attendance in obligatory consultation meetings and a minimum of 80% mandatory attendance in the lectures.

Students who do not meet this requirement will not be allowed to sit the exam.

Exam in two parts:

• A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. Each group member receives an individual grade based on their contribution to the project.
• 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 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 for the project report, provided the rules for plagiarism and source referencing are complied with.

No aids are permitted for the oral exam.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Associate Professor Raju Shrestha

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.