EPN-V2

ACIT4510 Statistical Learning Course description

Course name in Norwegian
Statistical Learning
Weight
10.0 ECTS
Year of study
2024/2025
Course history
Curriculum
FALL 2024
Schedule
Programme description

  • Introduction

    This course will feature weekly lectures and lab work to provide both theoretical and hands- on experience. Students will work in groups and complete assignments given to them. The student will supplement the lectures and lab with their own reading. The students will also work on a individual project.

  • Recommended preliminary courses

    The participants are expected to know linear algebra, basic functional analysis, and basic concepts in probability theory.

  • Required preliminary courses

    The following coursework must be approved before the student can take the exam:

    Four assignments in groups of 1-3 students (1000 - 2000 words per assignment)

  • Learning outcomes

    The assessment will be based on two part-exams:

    1) Individual project report (4000-6000 words). The project report counts 80% of the final grade.

    2) Individual project presentation (10 minutes). The presentations will be open to public. The oral examination counts 20% of the final grade

    Both exams must be passed in order to pass the course.

    The oral examination cannot be appealed.

    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.

  • Teaching and learning methods

    All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

  • Course requirements

    Grade scale A-F.

  • Assessment

    Two internal examiners. External examiner is used periodically.

  • Permitted exam materials and equipment

    All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

  • Grading scale

    Knowledge of linear algebra, vector calculus and basic statistics and probability. Knowledge of programming in python and basic introductory course on control or dynamical systems is recommended.

  • Examiners

    Topics covered in this course:

    • Configuration space
    • Rigid body motions
    • Robot forward kinematics
    • Velocity kinematics and statics
    • Inverse kinematics
    • Robotics sensors and actuators
    • Navigation, state estimation and filtering algorithms
    • Motion planning
    • ROS Robot Operating System

  • Course contact person

    Professor Pedro Lind