PENG9560 Topics in Artificial Intelligence and Machine Learning Course description

This course covers advanced topics in artificial intelligence and machine learning, both theory and practice, recent scientific papers and state-of-the-art techniques.The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

Dette kurset tar for seg ulike perspektiver på krise, endring og reform i offentlig sektor. Det legges spesiell vekt på samfunnsvitenskapelige perspektiver hentet fra fagområder som statsvitenskap, offentlig politikk og administrasjon og sosiologi. Kurset vil gi en innføring i grunnleggende begreper om kriser og endring i offentlig sektor. Videre vil kurset sette kriser og endringsprosesser i offentlig sektor i Norge i et flernivåperspektiv (europeisering og globalisering) og i en komparativ nordisk og europeisk ramme.

Politiske, økonomiske, og samfunnsmessige kriser som klimakrisen, covid-19 pandemien, migrasjonskrisen, finanskrisen og Ukraina-krigen påvirker alle nasjonale, regionale og lokale betingelser for politikkutforming og administrativ atferd. Kursets mål er derfor å belyse dette ved at studentene får en innføring i faglige debatter om krise og reformer i offentlig politikk, med utgangspunkt i politisk teori, organisasjonsteorier og teorier om europeisering og globalisering.

Undervisningsspråk er engelsk.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student has:

• an in-depth understanding of machine learning in its main forms: supervised, unsupervised, and reinforcement learning, both theoretical and applied, to solve real- lifeproblems.
• knowledge and understanding of the main concepts of deep learning.
• knowledge and understanding of some major concepts in artificial intelligence, including: complex systems (network models, cellular automata, and agent-based models) and evolutionary computing.

Skills

On successful completion of the course, the student can:

• apply techniques from machine learning to real-life problems.
• analyse data sets with the aid of machine learning algorithms.

General competence

On successful completion of the course, the student can:

• use libraries for programming deep learning algorithms such as TensorFlow.
• deploy models to relevant real-life problems.
• solve computational problem using evolutionary computing.

Students who are admitted to Master studies within the following subject areas are eligible to apply for admission to the course: nutrition and/or health and nursing Sciences.

Upon completion of the course the student will have obtained the following learning outcomes, defined as knowledge, skills and general competencies:

Knowledge The student

• has advanced knowledge on the importance of food culture and how food is used in different cultures to denote identity, social status and gender roles
• can analyse and apply theoretical approaches related to changes in food habits after migration, with a particular focus on dietary acculturation
• has advanced knowledge on the process of dietary transition and how migration may affect this process and health
• can apply knowledge on how changes in food habits after migration can lead to increase of non-communicable diseases
• has advanced knowledge of scientific literature addressing nutrition intervention with multicultural population, with a particular focus on cultural sensitive interventions

Skills The student

• can analyse and deal critically with relevant interventions addressing public health nutritional challenges in multicultural population
• can use relevant methods to assess the need of nutritional intervention in multicultural population
• can use relevant methods to develop, pilot and evaluate culturally sensitive nutrition interventions for multicultural population

General competencies

The student

• can apply knowledge and skills to research and professional activities related to food habits and health in multicultural population
• can communicate extensively with professionals of various discipline

Students are responsible for achieving the outlined learning objectives, and are expected to participate actively during the course and contribute to its success. A variety of learning approaches will be used, including:

Lectures with subsequent discussions, self-study, group work, interactive exchange among students and between students and resource persons, oral presentations, short stages at relevant institutions and organizations working with multicultural population.

Group (3-4 students per group) written assignment ((2000 words (+/- 25 %), followed by an oral presentation on the development of a specific nutrition intervention targeting multicultural population or selected groups.

Written exam (1500 words (+/- 10 %), in English or Norwegian, individual, 48 hours. The exam text is given in English.

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