ACIT4630 Advanced Machine Learning and Deep Learning Emneplan

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.

Etter å ha gjennomført dette emnet har studenten følgende læringsutbytte definert i form av kunnskap, ferdigheter og generell kompetanse:

Kunnskap Studenten

• har inngående kunnskap om vitenskapelige teorier, tradisjoner og paradigmer, relevant for fagdidaktisk forskning og de praktisk-estetiske fagene.
• har inngående kunnskap om ulike forskningsmetodiske perspektiv som grunnlag for analyse og refleksjon over fagområdets tradisjoner, egenart, innhold og praksis. har bred kunnskap om forskningsetikk, relevant for fagdidaktisk forskning og de praktiskestetiske fagene.
• har kjennskap til ulike former for kunnskapsinnhenting og kildekritikk i vitenskapelig publisering.

Ferdigheter Studenten

• kan drøfte og sammenstille ulike, relevante vitenskapelige tradisjoner og paradigmer.
• kan anvende forskningsmetoder som er relevant for fagdidaktisk forskning og det praktiskestetiske feltet, på en selvstendig måte.
• kan foreta forskningsmetodiske valg og anvende egen innsikt for å formulere faglige resonnementer.
• kan forholde seg kritisk til ulike informasjonskilder som grunnlag for forskningsprosessen.
• kan vurdere ulike etiske perspektiv knyttet til forskningsprosessen.

Generell kompetanse

Studenten

• kan arbeide selvstendig med eget fagfelt og beherske ulike relevante, forskningsmetoder.
• kan kommunisere med ulike målgrupper om faglige problemstillinger og drøftingsperspektiv innen utdanning og praktisk-estetiske fag.
• kan kritisk analysere relevant forskning og drøfte betydningen den har for skolen og egen profesjonsutøvelse.

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 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

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 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.

Emnet gir oversikt over vitenskapsteori og forskningsmetoder som er relevant for det praktiskestetiske feltet og for fagdidaktisk forskning og utviklingsarbeid. Vitenskapsteori skal gi innsikt i ulike tradisjoner og paradigmer, herunder også praksisbasert og praksisledet forskning. Emnet presenterer en bredde av forskningsmetoder og vitenskapelige posisjoner. Forskningsetikk inngår i emnet. Emnet skal gi grunnlag for arbeid med masteroppgaven.