Here is a brief diary of topics discussed in classes.

Slides and coding scripts are distributed after each lecture via the Google groups mailing-list. Refer to the home page on how to subscribe.

[23.09.2024] Introduction to the course.

[26.09.2024] Data management as input for our machine/deep learning approaches.

[30.09.2024] Recap from Machine Learning: linear classifiers and backpropagation.

[03.10.2024] In class exercises.

[07.10.2024] Introductions to CNNs.

[10.10.2024] In class exercises.

[21.10.2024] Activation functions, data pre-processing, weight initialization,

[24.10.2024] Batch normalization, DropOut and data augmentation.

[28.10.2024] Hyper-parameter tuning; transfer learning, convolutional neural networks for time series.

[31.10.2024]  Recurrent neural networks.

[04.11.2024]  Backprop in RNNs.

[07.11.2024] LSTMs

[11.11.2024] Coding practice with RNNs and LSTMs.

[14.11.2024] Attention and self-attention.

[18.11.2024] Positional encoding and Transformer Networks.

[25.11.2024] Generative networks (Pixel-RNN/CNN, AE, GAN).

[28.11.2024] Exercises on Transformer networks.

[02.12.2024] AI and Ethics.

[05.12.2024] Exercises on Generative Networks.

[16.12.2024] Recap lesson.