Résumé de section

◄Généralités
►Final Report

  • Credits:

    5 ECTS, 2 SWS

    Time & Place:

    Wednesdays, 14:00–15:30, online (Zoom link below)

    Prerequisites:  

    algorithms, linear algebra, analysis, and probability. Prior attendance of the course "Algorithmic Graph Theory" is recommended.

    Target Group:

    Master Computer Science (recommended), Bachelor Computer Science

    Lecturers:

    Joachim Spoerhase and Alexander Wolff and Kamyar Khodamoradi


    This course explores mathematical and algorithmic foundations of data science and provides a basis for further study in the field. The main reference used in this course will be the book "Foundations of Data Science", by Avrim Blum, John Hopcroft, and Ravindran Kannan. You can find a pre-print of the book on Avrim Blum's homepage. The topics we will discuss include (in the order of the chapters):

    • high-dimensional geometry
    • linear algebraic tools and techniques such as Singular Value Decomposition (SVD) 
    • random walks and Markov chains
    • machine learning 
    • algorithms for massive data
    • clustering techniques
    • analysis of random graphs
    • social choice
    • compressed sensing


    Important Note: During the first session on Wednesday, April 14th, we plan to assign topics to participants and schedule the presentations. Please review the list above (by consulting the book) and think about your preference to facilitate the assignment process. Please note that we have 9 topics, each suitable for a group of 1 or 2. As a result, we can accomodate up to 18 students. So, please make sure you attend the first session if you are planning to take the course.

◄Généralités
►Final Report