Intern
    Data Science Chair

    Data Science (ehemals Data Mining)

    General Information

    Organizer: Prof. Dr. Andreas Hotho, Andrzej Dulny, Simon Hentschel

    Contact: datamining[at]informatik.uni-wuerzburg.de

    The lecture provides an overview of knowledge extraction from (structured) data. This includes, among other things

    • Pre-processing techniques
    • OLAP analysis & data warehousing
    • Clustering (k-means, k-medoids, DBSCAN, OPTICS)
    • Classification (k-Nearest-Neighbor, Bayes, Decision Tree, Support Vector Machine; Bagging, Boosting, e.g. Random Forest, AdaBoost)
    • Regression analysis (linear regression, logistic regression)
    • Association rule learning (Aprioiri, FP-Growth)
    • Introduction to Deep Learning

    Organizational matters

    Please Note:

    You will find all current information about the course on WueCampus2.
    Please register early in WueCampus2 via the link above in order to receive access to the course as well as e-mails with important announcements.

    • Lecture
      The lecture will be held Mon, 10:15 - 11:45 in the Turing Lecture Hall.
      In the first lecture on 28.04. we will provide all important organizational information for the course of the semester.
    • Tutorials
      Thu, 08:15 - 09:45, (Room: Seminarraum II, CAIDAS Building 50)
      Thu, 14:15 - 15:45, (Room: Seminarraum II, CAIDAS Building 50)
      Thu 16:15 - 17:45, (Room: Seminarraum II, CAIDAS Building 50)
      The first exercise session will take place on April 24th. The exact exercise format will be announced as usual during the first session.
    • Prüfung
      There will be an exam at the end of the semester. However, the form, procedure and exact time have yet to be worked out and will be announced as soon as possible via WueCampus2. Current details will be shared in the announcement forum of the WueCampus2 course.
    • Further details will be shared in the announcement forum of the WueCampus2 course.

    Literature

    • {Pattern recognition and machine learning}. Bishop, C.M. Vol. 4. Springer, 2006.
    • Einführung in Data Science. Grus, Joel. O’Reilly, 2019.
    • Data Science from Scratch: First Principles with Python. Grus, Joel. O’Reilly, Beijing, 2015.
    • Data Mining - The Textbook. Aggarwal, Charu C. bll 1–693. Springer, 2015.

    Further literature from the field of data science and machine learning

    • Practical Statistics for Data Scientists. Bruce, Peter; Bruce, Andrew; Gedeck, Peter. 2nd ed. O’Reilly Media, Inc., 2020.
    • Introduction to Machine Learning with Python. Müller, Andreas C.; Guido, Sarah. O’Reilly, 2016.
    • Deep Learning. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. 2016.
    • Data Mining for Business Analytics: Concepts, Techniques and Applications in Python. Shmueli, Galit; Bruce, Peter C.; Gedeck, Peter; Patel, Nitin R. 1st ed. Wiley & Sons, Inc., 2020.
    • An introduction to statistical learning. James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert. Springer, 2013.