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.

None

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, Transformer, GAN, RL.
• has 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
• 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 learning 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, group consultations, presentation seminars, and project work. In the seminars, students will 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.

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 presenations from other students

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. 
• 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.

This course is based on PHVIT9100, Health Sciences II: Philosophy of Science, Research Ethics and Research Methodology. The course takes a critical perspective of key methodological traditions in qualitative research, focusing particularly on phenomenology, hermeneutics, and discourse analysis. Topics covered include research design, research interviews and different forms of interviewing methods, and observation as a research method. Emphasis is placed on the application of advanced strategies for analysing complex data material within the respective research traditions.

None

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate:

• is at the forefront of knowledge in selected qualitative research designs and their theoretical basis, and related methodological considerations
• has in-depth knowledge and understanding of interviews and observation as methodological research tools in the phenomenological, hermeneutic, and discourse-analytic research traditions
• can evaluate the usefulness of different forms of analysis, interpretation, and documentation within the relevant traditions

Skills

The PhD candidate can:

• plan a health science research project with relevant qualitative designs and methods
• analyse, interpret, and disseminate the results of qualitative research
• address complex scientific issues and challenge established knowledge and practice in qualitative methodology

General competence

The PhD candidate can:

• argue in favour of particular qualitative approaches on the basis of scientific theory
• identify relevant ethical issues and conduct research based on qualitative methodology with professional integrity

participate in discussions on qualitative methodology

Work and teaching methods consist of lectures, seminars, self-study, practical exercises in pilot interviews, and analysis of authentic interviews and observation notes. The outcomes of the seminars are presented and discussed in plenary sessions.