This course provides an introduction to key concepts and algorithms for neural networks, with strong emphasis on deep learning and its applications. Topics include classical neural networks, statistical pattern recognition, linear models, learning rules, network topologies, multilayer perceptron and backpropagation, deep learning, supervised, unsupervised and reinforcement learning, convolutional networks, recurrent neural networks, algorithms for training deep networks, deep learning to image recognition, computer vision, language modeling. The course consists of weekly lecture, assignments, selected paper presentation and written exam.
Upon the successful completion of this course, students should be able to:
|Final Examination||70 Marks||70%|