Course Syllabus

Complete syllabus available here.

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In recent years, a class of machine learning algorithms called deep neural networks have brought about a revolution in the field of artificial intelligence. Deep learning networks have pushed the state of the art on a range of challenging problems that had until now seemed out of reach for machines – from recognizing objects to predicting their physical interactions. At the same time, these neural networks have also led to progress in computational neuroscience with improved models of neural responses in higher visual cortical areas.

The goal of this course is to provide an advanced introduction to deep learning from the joint perspective of machine learning and neuroscience.   


This course will provide truly multi-disciplinary training for students interested in the intersection between biological and artificial intelligence. At the end of this course, students will be able to implement their own deep learning architecture using TensorFlow, a leading open-source software library for machine learning. Students will be exposed to state-of-the-art methods and will gain a solid understanding of the theory behind deep learning as well as its roots in biological learning and primate vision.  

Who should take this course

The course is designed for advanced students from either the computational or the biological sciences with a solid programming background.


Course Summary:

Date Details Due