ACIT5910 Master's Thesis, Phase 1 Emneplan

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory. The goal of this class is to provide students with the practical skillset to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

At least two subjects (20 ECTS) from the first semester must have been passed before the student can start work with MT1.

Emnet er en forsterkning i kunnskapsområdet natur, helse og bevegelse. Undervisningen gis i vårsemesteret i andre studieår. Kunnskapsbase bygger på fagene fysisk fostring, naturfag og pedagogikk.

Each student is appointed a supervisor when starting MT1. The work is carried out under the guidance of the appointed supervisor.

This course covers differences in user requirements due to user heterogeneity, situational variations and the wide range of hardware, software and versions of these. Identification of disabling barriers and how these can be eliminated or reduced through universal design of ICT is a central theme in this course. Furthermore relevant guidelines, regulations and legislation will be covered.

The student should have the following outcomes upon completing the course:

Knowledge:

Upon successful completion of the course, the student should:

• have advanced knowledge on robotic and autonomous systems components and architecture
• have advanced knowledge in modeling kinematics and dynamics of robotic systems
• have advanced knowledge in common sensor and actuator technologies used in robotics
• have advanced knowledge of algorithms and methods used in state estimation, navigation, motion planning, and computer vision.
• have a good understanding of the Robot Operating System (ROS) and software architectures used in robotic and autonomous systems
• have a good understanding of ML/AI methods used in robotics and autonomous systems

Skills:

Upon successful completion of the course, the student:

• can analyze a robotic and autonomous systems with regard to its components, architecture, and their purpose
• can model and analyze kinematic and dynamics of robotic systems
• can apply a number of algorithms and methods in state estimation, navigation, motion planning, and computer vision
• can analyze and implement solutions based on Robot Operating System (ROS)

General competence:

Upon successful completion of the course, the student:

• can discuss the role of robotic and autonomous systems in a number of practical applications
• can analyze how robotic and autonomous systems operate and design specific components using ROS and other software tools.

Ingen forkunnskapskrav.

• Individual oral presentation of journal or conference articles in class (15 to 20 minutes).
• Participation: Obligatory participation in 2 excursions is required in order for the student to pass this course. The dates for the excursions will be announced at semester start.

Retten til å avlegge eksamen forutsetter godkjente arbeidskrav, deltakelse i bestemte faglige aktiviteter og tilstedeværelse i undervsining.

Arbeidskrav

• Individuell artsprøve - praktisk prøve i artskunnskap om stein, planter, dyr og sopp.
• Individuelt praktisk arbeid knyttet til observasjon i naturfag.
• Individuelt skriftlig arbeid knyttet til helse, omfang 1000 ord +/- ti prosent.
• Praktisk og skriftlig gruppearbeid knyttet til barns kroppslige lek på utelekeplassen, omfang 2000 ord +/- ti prosent.

Faglig aktivitet med krav om deltakelse

• Deltakelse i barnefestival. De studenter som på gyldig grunnlag ikke kan delta i visningen av gruppens praktiske produksjon til barnefestivalen, skal levere et skriftlig notat, omfang om lag 1000 ord +/- ti prosent.
• Veiledningsseminar knyttet til dokumentasjon av barns lek. Studenter som på gyldig grunnlag ikke kan delta i veiledningsseminaret, skal levere et skriftlig notat på om lag 1000 ord.
• Deltagelse i aktiviteter med barnegrupper i skogen. Studenter som på gyldig grunnlag ikke kan delta, skal levere et skriftlig notat på om lag 1000 ord.

Krav om 80 prosent tilstedeværelse i undervisningen

Det er krav om 80 prosent tilstedeværelse i all undervisning.

For utfyllende informasjon om arbeidskrav, deltakelse i faglige aktiviteter og krav til tilstedeværelse i undervisning se programplanen for bachelorstudium i barnehagelærerutdanning, heltid.

Basic course in control theory, linear systems and system dynamics. Basic understanding of matrix algebra, statistics, and programming using MATLAB or similar.