Lecture
Computational Ethology: Animal Behavior Analysis
In course
DEMO: sunt sint reprehenderit mollit
Occaecat eu voluptate ea fugiat qui dolor nostrud ex sint sunt. Fugiat mollit mollit voluptate reprehenderit incididunt in laboris. Deserunt reprehenderit laboris officia ipsum est non elit ad veniam consequat ex. Anim tempor officia sit adipisicing eiusmod irure tempor excepteur irure. Cupidatat mollit tempor ad mollit ad laboris magna nisi voluptate fugiat esse adipisicing ex.
Description
This lecture introduces the field of Computational Ethology, focusing on the automated analysis of social behavior in Drosophila Melanogaster. Topics include the use of machine vision and machine learning for behavior measurement, creation of ethograms, challenges in behavioral phenotyping, and the potential advancements in neuroscience. The instructor discusses the benefits, challenges, and future prospects of Computational Ethology, emphasizing the need for interdisciplinary collaboration and advanced data analysis techniques.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceInstructor
eiusmod anim incididunt cillum
Velit veniam cupidatat nostrud qui sint anim esse irure quis incididunt ut amet. Aliqua laborum quis adipisicing in. Velit enim do sint pariatur. Excepteur reprehenderit fugiat ut voluptate laborum dolor sunt esse proident sunt. Sunt consequat amet elit aliquip reprehenderit.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (32)
Neuroscience: Behavioral Analysis
Explores neuroscience through behavior analysis, focusing on pose estimation and motor control studies.
Learning Analytics: Classroom Analytics
Explores adaptive instruction, cognitive diagnosis, and handwriting difficulty analysis using machine learning models.
Large Scale Stability in Dynamical Systems
Explores large scale stability in dynamical systems and interconnected behavior.
Continuous-Time Stochastic Processes: Ergodicity Examples
Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.
Obedience: Milgram's Experiments
Delves into Milgram's obedience experiments, analyzing authority dynamics and factors influencing compliance levels.