This course offers a comprehensive journey through essential mathematical foundations and practical techniques in machine learning and data analysis. Starting from basic mathematical concepts like matrix factorizations and parametric probability distributions, to exploring more advanced topics such as reproducing kernel Hilbert spaces and numerical optimization, students will develop a robust understanding for tackling real-world data challenges. Through hands-on exercises and computational projects, participants will gain proficiency in data embeddings, unsupervised learning, clustering, supervised learning for classification and regression, as well as density estimation. Additionally, the course introduces students to the exciting realm of deep learning, providing a solid foundation for further exploration in this rapidly evolving field.
Literature:
J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004
S. Shalev-Shwartz and S. Ben-David. Understanding Machine Learning. Cambridge Univ. Press, 2014.
M. Hardt and B. Recht. Patterns, Predictions, and Actions: Foundations of Machine Learning. Princeton University Press, 2022.
P. Grohs and G. Kutyniok, editors. Mathematical Aspects of Deep Learning. Cambridge University Press, 2022.
