EPN-V2

ACIT4630 Advanced Machine Learning and Deep Learning Emneplan

Engelsk emnenavn
Advanced Machine Learning and Deep Learning
Studieprogram
Master's Programme in Applied Computer and Information Technology
Omfang
10.0 stp.
Studieår
2024/2025
Timeplan
Emnehistorikk

Innledning

Målgruppe

Målgruppen for studiet er personer med oppgaver eller ansvar knyttet til voksne menneskers læring i et moderne, flerkulturelt samfunn. Studiet er utviklet for å møte behov hos personer som arbeider med undervisning, veiledning eller ledelse og organisering av virksomheter der voksnes læring er sentralt. Studiet vil også passe for personer som ikke står i et fagrelevant arbeidsforhold i studietiden, men som ønsker å utvikle kunnskaper og kompetanse på området.

Opptakskrav

Opptakskrav er generell studiekompetanse eller godkjent realkompetanse.

Anbefalte forkunnskaper

  • 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.

Forkunnskapskrav

Public health work is society's organized effort to maintain, improve and promote the population's health, both locally and globally. Interventions are directed towards factors that contribute to better health and factors that might represent a health risk. Public health work is concerned with reducing health inequalities through work on equity, accessibility and quality of services.

An increase in disease rates, long-term conditions and lifestyle illnesses are expected in the future. This is a consequence of demographic changes and a result of people's health behaviour. Competence in inter-professional and inter-sectoral collaboration in both public and private sectors is crucial to meeting challenges.

Regulations: Lov om universiteter og høyskoler and Forskrift om studier og eksamen ved OsloMet.

ECTS-Distribution

Theory and method

  • Part I: 0,5
  • Part II: 1,0
  • Sum ECTS: 1,5

Ethics

  • Part I: 0,5
  • Part III: 0,5
  • Part IV: 1,0
  • Sum ECTS: 2,0

Governance

  • Part I: 3,0
  • Part II: 3,0
  • Sum ECTS: 6,0

Communication

  • Part I: 0,5
  • Part III: 1,0
  • Part IV: 1,0
  • Sum ECTS: 2,5

Health Promotion & Preventative Work

  • Part I: 0,5
  • Part II: 1,0
  • Part III: 3,5
  • Part IV: 1,0
  • Sum ECTS: 6,0

Sum

  • Part I: 5,0
  • Part II: 5,0
  • Part III: 5,0
  • Part IV: 3,0
  • Sum ECTS: 18

Læringsutbytte

No prerequisites

Innhold

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.

Arbeids- og undervisningsformer

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.

Arbeidskrav og obligatoriske aktiviteter

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

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.

Vurdering og eksamen

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.

Hjelpemidler ved eksamen

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.

Vurderingsuttrykk

No exam aids permitted.

Sensorordning

ECTS grading A-F

Emneansvarlig

Two examiners evaluate all students. One examiner is external on 10 % of the students.