Deep Learning, 6 credits
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Admission to the course requires taken courses in Signals and Systems, 5 credits, and Machine Learning or Applied Machine Learning, 5 credits. English 6.
Deep learning methods are widely applied across various fields in engineering, such as computer vision, signal processing, and remote sensing. The purpose of the course is to introduce students to neural networks and deep learning fundamentals for various applications such as classification, detection, and signal reconstruction. In addition, this course discusses various architectures such as Convolutional Neural Networks (CNNS), Recurrent Neural Networks (RNNS), and Autoencoders.
Admission to the course requires taken courses in Signals and Systems, 5 credits, and Machine Learning or Applied Machine Learning, 5 credits. English 6.
Level
A1N
Course code
ET2638
