Extended data for Cognitive Informatics in Human Vision

Stored data
bibliography hu
Compulsory reading list ·         Hoffman, D. D. (1998). Visual intelligence: how we create what we see (1st ed.). New York: W.W. Norton. ·         Marr, D. C. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Freeman. ·         Zeki, S. (1993). A vision of the brain. Oxford ; Boston: Blackwell Scientific Publications. ·         Rieke, F., Warland, D., van Steveninck, R. R., & Bialek, W. (1997). Exploring the Neural Code (Computational Neuroscience). Cambridge MA: Bradford Book - MIT Press. Recommended reading list ·         If any, it shall be specified in the course description for each semester.
bibliography en
Compulsory reading list ·         Hoffman, D. D. (1998). Visual intelligence: how we create what we see (1st ed.). New York: W.W. Norton. ·         Marr, D. C. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York: Freeman. ·         Zeki, S. (1993). A vision of the brain. Oxford ; Boston: Blackwell Scientific Publications. ·         Rieke, F., Warland, D., van Steveninck, R. R., & Bialek, W. (1997). Exploring the Neural Code (Computational Neuroscience). Cambridge MA: Bradford Book - MIT Press. Recommended reading list ·         If any, it shall be specified in the course description for each semester.
courseContent hu
Topics of the course ·         Introduction to Computational Approaches to Visual Processing ·         The Hierarchy of Visual Processing ·         Perceptual Decision Making: Classical approaches ·         Classical approaches II.: Drift Diffusion Model ·         Bayesian Models of Perceptual Decisions ·         Predictive Coding, Confidence ·         Attention, Multisensory integration ·         Reinforcement Learning, Exploration /Exploitation ·         Active Vision and Eye Movements ·         Modeling Sensory Abnormalities in Psychiatric Disorders ·         Visual Recognition in Deep Neural Networks ·         Brain-computer interface
courseContent en
Topics of the course ·         Introduction to Computational Approaches to Visual Processing ·         The Hierarchy of Visual Processing ·         Perceptual Decision Making: Classical approaches ·         Classical approaches II.: Drift Diffusion Model ·         Bayesian Models of Perceptual Decisions ·         Predictive Coding, Confidence ·         Attention, Multisensory integration ·         Reinforcement Learning, Exploration /Exploitation ·         Active Vision and Eye Movements ·         Modeling Sensory Abnormalities in Psychiatric Disorders ·         Visual Recognition in Deep Neural Networks ·         Brain-computer interface
assessmentMethod hu
Learning requirements, mode of evaluation, criteria of evaluation: requirements ·         active participation at the lectures. ·         Programming Tutorials ·         Final Project ·         Lecture Exam, which can be substituted by submitting a short weekly reading summary mode of evaluation: ·         examination and practical course mark, 1-5 grades criteria of evaluation: ·         quality and quantity of knowledge encomppasing the course ·         quality of pracitcal exercises, homework, essays
assessmentMethod en
Learning requirements, mode of evaluation, criteria of evaluation: requirements ·         active participation at the lectures. ·         Programming Tutorials ·         Final Project ·         Lecture Exam, which can be substituted by submitting a short weekly reading summary mode of evaluation: ·         examination and practical course mark, 1-5 grades criteria of evaluation: ·         quality and quantity of knowledge encomppasing the course ·         quality of pracitcal exercises, homework, essays