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The Course Covers Wide Range of Deep Learning Algorithms and Their Efficient Implementation On Dedicated Hardware. in Particular, The Course Will Cover Following Topics# Multilayer Perceptron, Convolutional Networks, Recurrent Networks, Unsupervised Networks, Generative Models, Learning On Graphs, Network Compression Etc. The Hardware Section Will Overview Dedicated Architectures For Training And Accelerating Deep Neural Networks. Learning Outcomes# At The End of The Course, The Student Will# 1. Know The Theoretical and Practical Aspects Constructing Deep Neural Networks. 2. Know How to Construct Deep Neural Networks Using Dedicated Software Frameworks. 3. Know Diverse Optimization Techniques Allowing Efficient Implementation of Machine Learning Applications On Computation Accelerators. 4. Perform a Concluding Research Project.

Faculty: Computer Science
|Undergraduate Studies |Graduate Studies

Pre-required courses

44198 - Intro. to Digital Signal Processing or 46195 - Machine Learning or 234125 - Numerical Algorithms or 236200 - Signal or 236201 - Introduction to Data Processing And or 236756 - Introduction to Machine Learning

Course with no extra credit

46211 - Deep Learning 97209 - Machine Learning 2

Course with no extra credit (contained)

336033 - Topics in Deep-learning For Medical Imag