Useful prior knowledge.

Recommended prerequisites are:
Basic knowledge in linear algebra, multivariate calculus, control and systems theory, and programming, as acquired by e.g. the courses:  SF1624, SF1626, EL1110 and DD1310. All programming assignments in the course will be done in Python.

The course covers a wide range of topics, and it is possible for students to cover (limited) gaps in prerequisite knowledge on their own while following the course, but the more of the following topics the student has prior familiarity with, the easier the course should be to follow:

  • Algorithms and programming
  • Python, NumPy, and Linux
  • Coordinate transforms, elementary spectral theory, and homogeneous coordinates
  • Jacobians
  • Laplace transform, difference equations, PID control
  • Basic probability theory, Bayes' theorem
  • Electronics and machine elements
  • Rigid-body mechanics, kinematics, and dynamics
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