The tutorials for the course Neural Networks, Machine Learning, and Randomness are held on odd weeks on Wednesdays from 8:00 to 9:40 in room T-124, or as announced in lectures and tutorials.
study materials
The exercise materials are in the form of Jupyter notebooks using Python. For easy installation of Python, Jupyter, and the required packages, we will use uv . The first exercise is intended to familiarize you with this technology. To replicate the exact environment in which the notebooks were created, you can use the files pyproject.toml and uv.lock.| # | name | topics | assignment | solution |
|---|---|---|---|---|
| 1 | introduction to machine learning | basics of machine learning in Python, supervised, unsupervised, reinforcement, self-supervised learning, linear regression, cost function, visualizing the regression line, evaluating simple models | notebook | notebook |
| 2 | perceptron and logistic regression | logistic regression, binary classification, cost function, implementation and visualizitaion of the decision strategy, perceptron, its learning algorithm and implementation | notebook | notebook |
| 3 | dimensionality reduction and clustering | principal component analysis, dimensionality reduction and its mathematical concepts (SVD), visualizing data with PCA, k-means clustering, clustering metrics | notebook | |
| 4 | decision trees | decision trees, random forrests, their construction, splitting criteria and implementation | ||
| 5 | bayesian methods | bayes theorem, maximum likelihood estimation vs. maximum a-posteriori estimation, naive bayes classifier vs. k-nearest neighbor classifier |