Lecturers:
Dr. Yuki Tokushige
Content:
We explore mathematical aspects of data science by discussing topics such as singular value decompositions, principal component analysis, random graphs and so on. In doing so, we will explain and review mathematical tools that arise in the context, such as singular values, eigenvalues, limit theorems etc.
Prerequesits:
The bachelor courses on Introductory Probability Theory and Linear Algebra. (This is a mathematical course. Students need to be familiar with concepts such as random variables, expectations, matrices, eigenvalues...)
Place and time:
Lectures: Tuesday 9:45-11:15 (4206.01.0115 - PK 3.3)
Exercise session: Tuesday 15:00-16.30 (4201.05.512 - PK 14.512 (ehem. PK 14.6))
Exams:
Oral exams, scheduled individually
Literature: (available online)
Foundations of Data Science (Blum, Hopcroft, Kannan)
Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science (Bandeira)
Mathematics of Data Science (Bandeira, Singer, Strohmer)
Remark:
Lectures and exercise sessions are given in english.