Advanced topics in computer vision: image sequence analysis, spatio- temporal filtering, camera calibration and hand-eye coordination, robot navigation, shape representation, physically-based modeling, regularization theory, multi- sensory fusion, biological models, expert vision systems, and other topics selected from recent research papers.
4
UnitsLetter
Grading1, 2, 3
PasstimeNone
Level LimitEngineering
CollegeClass had 3 programming assignments with EC and optional HW assignments. Programing assignments were pretty useless but HW is necessary because they're just like the exams. Exams are super long and focus on very minor details so you must know everything. Prof is really nice and lectures were interesting and easy to follow. Super organized class.
Don't see how anyone could have any complaints about this guy.
A fantastic professor who explains the requirements well. He is knowledgeable in his field and helps students understand and explore outside of lectures. The class is graded mainly on projects and tests. Homework is optional in this class.
This class had multiple choice exams and the programming assignments were easy, and lowest scores are dropped. You get to learn introductory computer vision topics and it is a fun experience overall.
I love professor Beyeler! Hes super nice and his lectures are very clear. His expectations for what you need to know is very clear as well. He made visual neuroscience very fun and interesting!
Great course! Enthusiastic lecturer
Joao Hespanha
4.9
Li-C Wang
4.3
Ramtin Pedarsani
4.0
Andrew Teel
4.3