A topic in the theory of statistics, such as probability theory, Bayesian statistical theory, statistical decision theory, martingales and stochastic integrals. The fourth number of the course code ...

Catalog description: Presents the underlying theory behind machine learning in proofs-based format. Answers fundamental questions about what learning means and what can be learned via formal models of ...

Successful completion of 50% of the homework sets as well as a written exam at the end of the course (format, date and time t.b.a.) ...

In what way can we reduce misclassification bias in statistical learning so that we obtain more accurate classifier-based statistics? There are two conflicting developments that affect the field of ...

This course aims at an introduction of some basic aspects in statistical learning and data science. We plan to cover topics in stochastic approximation, pattern recognition, kernel density estimation, ...

This course provides foundational and advanced concepts in statistical learning theory, essential for analyzing complex data and making informed predictions. Students will delve into both asymptotic ...

Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

Do plants attacked by herbivores produce substances that are most effective against attackers in a targeted manner, or are herbivore-induced changes in a plant metabolism random, which could thwart ...