Introduction / Instructions (15/08) Basic concepts of Machine Learning (ML) (22/08) Neural Networks / Fundamentals (29/08) The modular approach for designing NNs (05/09) Training practices and Tensorflow (12/09) Supervised applications (19/09) Optimization and stability (26/09) Unsupervised Learning (03/10) Generative Modeling I (10/10) Generative Modeling II (17/10) Tensor Flow (24/10)