
Nonlinear Dynamics at the Free University Berlin  
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A. López Nieto Adjunct PostDoc Dr. Yuya Tokuta 
Summer 2017
Seminar: Mathematics of Machine Learning: from linear models to neural networksSchedule, Summer 2017
DescriptionMachine learning or, more generally, artificial intelligence is nowadays ubiquitous. Explicitly or implicitly, it surrounds us, hiding behind anything, ranging from smartphones and social networks to selfdriving vehicles. Essentially, machine learning deals with searching for and generating patterns in data. Although it is traditionally considered a branch of computer science, it heavily relies on mathematical foundations. Thus, it is a primary goal of our seminar to understand these mathematical foundations. In doing so, we will mainly follow the classical monograph [1] and combine the two complementary viewpoints: deterministic and probabilistic. In the list of topics below, the numbers in brackets refer to the corresponding sections in [1]. As a complement, the monographs [2] and [3, section 5] are recommended. More sections in [3] will be used in the next semester. Doing exercises (which are present in [1] in abundance) and programming is beyond the seminar’s scope, however, the students are not forbidden and even strongly encouraged to do both. In this semester, we will focus on linear models and their straightforward nonlinear generalizations. In the next semester, we plan to elaborate on genuinely nonlinear models such as neural networks and graphical models. The language of the seminar is English. Interested students are supposed to be acquainted with basics of probability theory, which can be refreshed, e.g., by reading sections 1.2.01.2.4 and section 2 in the book [1]. Topics
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Last change: Apr. 26, 2017 
