Einführung in Maschinelles Lernen/Introduction to Machine Learning (WS 2022/2023)
- For a general course description please read the corresponding pages from the WIAI module guide.
- You find administrative information at UnivIS.
- Participants should sign up for the course in the virtual campus.
- This course is elegible by students of the master in Survey Statistics (MiSS). The module is an official import module for this degree.
- Peter Flach: The Art and Science of Algorithms that Make Sense of Data, CUP, 2012.
- Tom Mitchell: Machine Learning, McGraw Hill, 1997.
- Christopher Bishop: Pattern Recognition and Machine Learning, Springer, 2006.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning, MIT Press, 2016.
- Claude Sammut, Geoffrey I. Webb (Eds.): Encyclopedia of Machine Learning. Springer, 2017.
- Machine Learning entry at aaai.org
- Machine Learning at AI on the Web
- UCI Machine Learning Repository
- Fernandez-Delgado et al. (2014). Do we Need Hundreds of Classiers to Solve Real World Classication Problems?, Journal of Machine Learning Research, 15, 3133-3181.
- Christoph Molnar: Interpretable Machine Learning, 2018.
- Python ML libraries
- Basic Concepts of Machine Learning [pdf]
- Foundations of Concept Learning [pdf]
- Decision Trees & Random Forests, Training and Evaluating Models [pdf]
- Perceptrons and Multilayer-Perceptrons [pdf]
- Deep Learning (CNNs, LSTNs, Autoencoder) [pdf]
- Inductive Logic Programming [pdf]
- Genetic Algorithms/Genetic Programming [pdf]
- Instance-based Learning [pdf]
- Bayesian Learning/Graphical Models [pdf]
- Human Concept Learning [pdf]
- Kernel Methods, Support Vector Machines [pdf]
- EM-Algorithm, Hidden Markov Models, LSTMs [pdf]
- Reinforcement Learning [pdf]
- Inductive Programming [pdf]
- Unsupervised Learning (Autoencoders, Kohonen nets)
- Explaining blackbox models, Transparency and Interpretability, Fairness and unwanted biases, Ethical and responsible ML
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