This blog is mostly meant as an online research notebook in which I write about topics which interest me. Altough I do strive for correctness, please proceed with caution.

The following topics are reoccuring on this blog.

This blog is mostly meant as an online research notebook in which I write about topics which interest me. Altough I do strive for correctness, please proceed with caution.

The following topics are reoccuring on this blog.

Convex optimization deals with the problem of optimizing convex functions over convex sets. Convex optimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling, statistics (optimal design), and finance.

The theory of large deviations concerns the asymptotic behavior of remote tails of sequences of probability distributions. Large deviations theory formalizes the heuristic ideas of concentration of measures and widely generalizes the notion of convergence of probability distributions.

Probability theory is the branch of mathematics concerned with probability, the analysis of random phenomena. As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of large sets of data.

Supervised learning is the machine learning task of inferring a function from a limited amount of labeled training data. Learning algorithm need to generalize from the training data to unseen data as good as possible.