Course topics:

• Information processing in the brain, neurons, receptive fields
• Simple classification models, the perceptron, linear discriminant analysis, regression approach to classification
• Generative approaches, k-nearest neighbour classification, Bayes equation
• Model selection and cross-validation
• Logistic regression, binary cross-entropy loss function, gradient descent
• Gradient descent optimization and regularization, multi-layer perceptron and error backpropagation
• Convolutional networks, deep neural networks, receptive fields in deep netoworks
• Reinforcement learning
• Sequence modeling, speech recognition, markov chains, hidden markov model (HMMs)
• Transformer deep networks, large language models (LLMs), from HMMs to LLMs
• Information theory, measuring information, entropy
• Information theory continued, entropy bound for coding, compression

The course language is English or German, with English used by default and German used in case of only German native language speakers taking the course.