UB - University at Buffalo, The State University of New York Computer Science and Engineering

CSE 676: Deep Learning

Deep Learning

Deep Learning algorithms learn multi-level representations of data, with each level explaining the data in a hierarchical manner. Such algorithms have been effective at uncovering underlying structure in data. They have been successful in many artificial intelligence problems including image classification, speech recognition and natural language processing. The course, which will be taught through lectures and projects, will cover the underlying theory, the range of applications to which it has been applied, and learning from very large data sets. The course will cover connectionist architectures commonly associated with deep learning, e.g., convolutional neural networks, autoencoders, recurrent neural networks and long-short-term memory. Methods to train and optimize the architectures and methods to perform effective inference with them, will be the main focus. Students will use open source software libraries such as Tensorflow.

Ph.D.:

This course fulfills one Artificial Intelligence Core Area (Depth) requirement.

M.S.:

This course fulfills one Artificial Intelligence Core Area (Depth) requirement.

CSE 4/574

Course Instances
Semester Section Title Instructor Credit Hours Enrolled
Fall 2017 LEC Deep Learning Dr. Sargur (Hari) N. Srihari 3 83/90
Fall 2001 LEC Knowledge Representation Dr. William J. Rapaport 3 9/60
Fall 1999 LEC Knowledge Representation Dr. Stuart C. Shapiro 3 9/30
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