Advanced concepts in machine learning Basic Information M124 (3+2+1) - 8 ECTS credits Introduce students with modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Students will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Students will use these building blocks to define complex modern architectures in PyTorch or TensorFlow frameworks. You can access the course content at the following link: PDF Teachers Basic literature Goodfellow, Y. Bengio, A. Courville, Deep Learning, The MIT Press, Cambridge, 2016. Additional literature M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. Teaching materials The materials are available on the internal Teams channel of the course, through which all internal communication takes place. Students are required to register on the course’s Teams channel. The channel code for joining the course can be found in the schedule.