Blockkurs Feb 27 - March 3, 2023

Enrolment key: fairML23

KursZeit
Ort
Grundkurs
Mo, 27.02., 10.00 - 17.00 Uhr      
Ludwigstraße 28, IuK-Pool (207)
Grundkurs
Di, 28.02., 10.00 - 17.00 Uhr
Ludwigstraße 28, IuK-Pool (207)
Grundkurs
Mi, 01.03., 10.00 - 17.00 Uhr
Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs  
Do, 02.03., 10.00 - 17.00 Uhr
Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs    
Fr, 03.03., 10.00 - 17.00 Uhr
Ludwigstraße 28, IuK-Pool (207)

Schedule:
  • Wednesdays, 10:15-12:00; Schellingstr. 3(R), R 051
  • Thursdays, 16:15-18:00; Schellingstr. 3(S), S 006.
First meeting: Wednesday, 19.10.2022

Enrolment token: csds22/23

More information soon.

Schedule:

date/time location instructor
Initial meeting in Nov (date tbd) tbd Wilhelm
Presentations beginning of Feb (date tbd) tbd Wilhelm

Schedule:

day/time location instructor start date
Lecture Mon, 16:15-18:00 Schellingstr. 3 (S) / S 004 Wilhelm 17.10.22
Lecture/Tutorial Wed, 8:15-10:00 Geschw.-Scholl-Pl. 1 (E) / E 006 Wilhelm 19.10.22

Enrolment key:
 ci2223

Similar to econometrics as a subfield of statistics, which specializes in economic data and issues, financial econometrics is the subfield of econometrics that focuses on financial data and applications. A special feature of financial data are the so-called "stylized facts", which do not exist in a comparable way in any field of empirical economics. These properties are significant in two respects: On the one hand, they have led to the falsification or modification of popular, theoretical models in the past. On the other hand, they have been the impetus for the development of models that have been extremely successful in practice. In this course we will introduce the basics of this field.

Introductory meeting of the seminar

Friday, October 28th, 3pm.



Kick-off meeting

We invite you to attend the kick-off meeting on Friday, October 21st, 8.30am in room C 112.

Enrolment key: rose-diagram

Sie können sich in den Kurs mit dem Schlüssel DHS2223 selbst einschreiben.

Description

We offer a dozen implementation challenges in TensorFlow and Pytorch that you will work on during the course of the semester with extensive supervision by an expert. The goal is to learn one of the two major deep learning languages with hands-on experience and implement an advanced deep learning project. 

Organization

  • Kick-off Session (Zoom): 24.10.2022, 5pm
  • Final Submission Deadline: End of the semester

Enrolment Key

appliedDL2223

Target Audience 

  • Statistics
  • Data Science
ECTS / Module

  • 6 ECTS (e.g., for the module Advanced Deep Learning) 


Der Einschreibeschlüssel lautet: DT2223

The enrollment key is: DT2223

Official statistical agencies are the central information service providers in a democratically organized society, informing politics, business and society about current economic, social and increasingly also ecological developments. In this way, official statistics form an important basis for informed decisions but at the same time act as a control authority, especially for politics, by empirically reflecting the consequences of decisions and actions. To meet this responsibility, official statistics production underlies a strict methodology and high-quality standards. Against this background, there has also been an intensive discussion about the opportunities and challenges complex statistical modelling, machine learning techniques and new data sources deliver. This development has also led to close cooperation of statistical authorities with a number of universities, resulting in an EU-wide certification of particular master's degree programmes (EMOS: European Master in Official Statistics). For students at LMU, it is possible to obtain an EMOS certificate by taking a specific route within the master's program specialisations in machine learning and social statistics and data science.

The course "EMOS A" aims to prepare students for participation in the discourse between academic and official statistics and the numerous opportunities for cooperation with public data producers. It provides an insight into typical questions and modes of argumentation, methodological principles, and the most important products of official statistics. First, we take a look at current research concerning official statistics. Then, the basic principles and legal framework of official statistics, their fundamental structure in Germany/Europe and their most important supporting institutions are discussed as a basis. Further topics will then include the EU system of environmental indicators, a discussion of the role of Big Data in official statistics, selected aspects of result dissemination and statistical literacy, and access to official data sources for (own) secondary analyses, including an introduction to statistical anonymization techniques.

The format of the course is a mix of inverted classroom elements with online or in-person discussion and deepening of the topics and guest lectures. In addition, there is an independent examination of the material in the form of short mini-projects as exercises, in which specific content is worked through and then discussed. Grade bonuses are possible.

The course is compulsory for the EMOS variant; all other students can acquire 6 ECTS credits, which can be used flexibly.



Enrollment Key: emosa


Syllabus

ONLINE COURSE

Teachers: Hanna Brenzel, Hariolf Merkle, Marco Puts, Piet Daas
Runtime: 14. November 2022 -  16. December 2022
Format: Flipped Classroom: Self-learning through online videos and literature, weekly 1-hour online meetings
ExaminationExamination sheets (3 ECTS credits)
Language: English
Prerequisites: Basic R knowledge is required.


Who is this course for? MsC Statistics and Data Science (2021, WP 28+29+40+46), BsC Statistics and Data Science (2021, WP 8+11),Bsc Statistik (2010, WP 6.0.3+6.0.4), Statistik und Data Science als Nebenfach für Bachelor 30 ECTS (2021, WP 4+5), Statistik und Data Science als Nebenfach für Bachelor 60 ECTS (2021, WP 11+12), Statistik und Data Science als Nebenfach 30 ECTS Mathematik (2021, WP 5+6), Statistik Nebenfach 60 ECTS Bachelor Soziologie (2021, WP 10+11), Grundlegende Statistik als Nebenfach für MA 30 ECTS (2011, WP 6), Vertiefte Statistik Master (2011, WP 5)

Self-Enrolment Key: MLMsOS#2223

Termine:
TerminOrtPersonBeginn
VorlesungDi, 12:15-13:45TBANagler18.10.22
Übung Mi, 12:15-13:45TBANagler26.10.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "fmm"

Dieser Kurs wird Studierenden mit NF Statistik eine Einführung in die statistische Software (R) geben.

Der Kurs setzt sich aus einer Mischung aus Flipped-Classroom Lehrvideos, Hands-On Lab-Sessions und Drop-In Hilfe-/Beratungssession zusammen.

Weitere Infos folgen in Kürze.

Sie müssen und können sich NICHT über das lsf für die Veranstaltung anmelden, schreiben Sie sich bitte einfach mit dem Einschreibeschlüssel FortStat1WS22 in diesen Moodle Kurs ein.

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "stat3nf2022"

Inverted classroom style. Weekly meeting (in person): Wednesday, 14:00-16:00 c.t. Seminar Room

Starting date: 19.10.2022

Enrollment Key: automl23

    Termine und Personen:

     Termin Ort Person
    Vorlesung   
     Mo, 12.00 - 14.00
     M018  
     Scheipl  
    Vorlesung   
     Mi, 10.00 - 12.00
     M018  
     Scheipl  
    Übung
     Mo, 14.00 - 16.00
    S002 (Schelling 3)
     Rave
    Übung
     Mo, 16.00 - 18.00
    S002 (Schelling 3)
    Gruber
    Tutorium
     Di, 10.00 - 12.00  
    A214
     NN
    Tutorium
     Do, 14.00 - 16.00
    B006
     Sapargali

    Einschreibeschlüssel: GRM2223

    Themen

    1. Das multiple lineare Regressionsmodell
    2. Statistische Regularisierung
    3. Das verallgemeinerte lineare Modell
    4. Gemischte Modelle
    5. Das generalisierte additive Modell
    6. GAMLSS, Quantilregression, Verteilungsregression
    7. Kategoriale Regression
    8. Lebensdauermodelle
    9. Messfehler, Fehlklassifikation und fehlende Daten

    Schedule: 

    - Lecture: Monday, 16-18 c.t., Geschw.-Scholl-Pl. 1 - A 017 (starting Monday, 17 October 2022)

    - Lecture/Exercise: Wednesday, 10-12 c.t., Geschw.-Scholl-Pl. 1 - A 016 (starting Wednesday, 26 October 2022)

    Enrollment key: 

    life22Time



    Einschreibeschlüssel:
    dskrpt

    Termine:

    Termin Ort Person
    Vorlesung 
    Do, 14.00 - 16.00     
    M118 (Hauptgebäude)
    Fabian Scheipl     
    Vorlesung  
    Fr, 10.00 - 12.00 M118 (Hauptgebäude)
    Fabian Scheipl
    Übung 1 
    Mo, 10.00 - 12.00
    S004  (Schellingstr. 3)
    Eugen Gorich
    Übung 2  
    Do, 12.00 - 14.00
    B106 (Hauptgebäude)
    Michael Kobl
    Tutorium
    Di, 16.00 - 18.00
    S001 (Schellingstr. 3)
    Michael Kobl

    Übung & Tutorium beginnen erst in der zweiten Semesterwoche.

    Diese Veranstaltung vermittelt elementare Wahrscheinlichkeitsrechnung sowie Grundlagen der deskriptiven und explorativen Statistik. Dies umfasst grundlegende Axiome und Rechenregeln für Wahrscheinlichkeiten   (auch: bedingte und gemeinsame Wahrscheinlichkeiten) sowie die Begriffe der stochastischen und empirischen Unabhängigkeit für Ereignisse und Zufallsvariablen bzw. Merkmale. Die Lerninhalte umfassen auch eine erste einfache Begriffsbildung für und Eigenschaften von Zufallsvariablen, ihrer Wahrscheinlichkeitsdichten und Momente und wichtige parametrischer Verteilungsmodelle. Auf empirischer Seite werden entsprechend Skalenniveaus beobachteter Merkmale und einfache Erhebungsformen definiert und Techniken der uni- und multivariaten deskriptiven Statistik eingeübt: zum einen Datenvisualisierung anhand statistischer und wahrnehmungspsychologischer Leitlinien, zum anderen empirische Verteilungen und Kerndichten. Kennzahlen für Lage, Streuung, Schiefe, Wölbung, Konzentration und Assoziation werden eingeführt und ihre Eigenschaften intensiv diskutiert.  Letzteres umfasst auch eine erste Einführung in die Probleme kausaler Interpretation von beobachteten Assoziationen.

    Die Vorlesung entspricht Modul P3.1, die Übung dem Modul P4.1 des BA-Studiengangs Statistik und Data Science (PO 2021)

    Termine:

    Termin Ort Person Beginn
    Vorlesung Di, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) - A 120 Hoffmann 18.10.22
    Vorlesung/Übung Mi, 14:15-15:45 Geschw.-Scholl-Pl. 1 (A) - A 120 Rehms 19.10.22

    Einschreibeschlüssel
    • Der Einschreibeschlüssel lautet: "EinfBiom2223"

    Date Place Person Start
    Lecture Thursday, 10:15-11:45 Geschw.-Scholl-Pl. 1 - B 006 Boulesteix/Hoffmann 20.10.22

    Enrolment key
    • The enrolment key is: "ORS2223"

    Date Place Person Start
    Lecture Tuesday, 16:00-17:45 Geschw.-Scholl-Pl. 1 - A 015 Schomaker/Hoffmann 18.10.22
    Lecture/
    Exercise Session
    Thursday, 16:15-17:45 Geschw.-Scholl-Pl. 1 - A 015 Rehms 27.10.22

    Enrolment key
    • The enrolment key is: "StatMetEpi2223"

    Date Place Person Start
    Lecture Tuesday, 9:15-11:45 Geschw.-Scholl-Pl. 1 - A 014 Boulesteix/Hoffmann 18.10.22
    Exercise Session Monday, 14:15-15:45 Geschw.-Scholl-Pl. 1 - A 120 Rehms 25.10.22

    Enrolment key
    • The enrolment key is: "PCS2223"

    Schedule 

    • Q&A + exercise session: Friday, 10 - 12 
     Enrollment key
    • The enrollment key is OPTIM2022

    Description

    A variety of digital data sources are providing new avenues for empirical social science research. With the emergence of Big Data, especially data from web sources play an increasingly important role in scientific research. In order to effectively utilize these data for answering substantive research questions, a modern methodological toolkit paired with a critical perspective on data quality is needed. This course introduces computational techniques that are suited for collecting and analyzing digital behavioral data, and for exploring, visualizing and finding patterns in (different types of) data from various sources. In addition, aspects of reproducibility, data quality and error frameworks for digital data are discussed.

    Enrolment Key

    CSSws2223

    Der Einschreibeschlüssel lautet CoVorMa22

    Sie müssen und können sich NICHT über das lsf für die Veranstaltung anmelden, schreiben Sie sich bitte einfach mit dem Einschreibeschlüssel in diesen Moodle Kurs ein. Alle erhalten einen Platz und es gibt keine Belegfristen.


    Einschreibeschlüssel: 
    Stat1Soz2022


    Vorlesung 
    Anna-Carolina Haensch (anna-carolina.haensch@stat.uni-muenchen.de)

    Übung Jacob Beck, Leah von der Heyde, Sarah Ball

    Tutor*innen Jonathan Koop


    Description

    The lecture deals with theoretical and practical concepts from the fields of statistical learning and machine learning. The main focus is on predictive modeling / supervised learning. The tutorial applies these concepts and methods to real examples for illustration purposes.

    Organization

    • Class: Friday, 8.30-10 am
    • Location: Geschw.-Scholl-Pl. 1 / A 119

    Enrolment Key

    SL_w2223

    Target Audience 

    • Statistics (Methods/Bio/WISO)
    • Data Science MSc.


    Deep Learning algorithms have made outstanding results in many domains such as computer vision (CV), natural language processing (NLP), recommendation systems, and medical image analysis. However, the outcome of current methods depends on a huge amount of training labeled data, and in many real-world problems such as medical image analysis and autonomous driving, it is not possible to create such an amount of training data. Learning from unlabeled data can reduce the deployment cost/time of deep learning algorithms which requires annotations from experts such as medical professionals and doctors. 

    In this seminar, we will learn about the theory of deep unsupervised learning, the Autoregressive models, Generative models, deep learning methods for density modeling, and Self-supervised learning,  and will review some state-of-the-art methods and applications of unsupervised/self-supervised algorithms. We will offer different topics with different applications (i.e. NLP, CV) for a variety of tasks (i.e. clustering, representation learning, density modeling, etc).  As part of the seminar, you will also apply one of the frameworks to a given real-world problem. This means every participant will be asked to prepare an oral presentation about a current technique and to write up a reproducible case study of actual data analysis in an unsupervised DL framework, in addition to peer-reviewing the (theoretical and practical) work of a colleague.

    Recommended prerequisites: Deep learning; Python, PyTorch, TensorFlow, We would also hold the seminar in English and also allow students from other courses (especially DS students)

    Seminar: Blocked towards the end of the semester, 
    Weekly Meeting: Fridays 8:30- 12:30
    First meeting: Oct. 21st
    Zoom link
    Key: "seminar_udl"

    The "Advanced Programming (R)" course targets students in the Statistics and Data Science Master's programme. The course can also be taken by advanced Bachelor's students that have taken "Programmieren statistischer Software". Advanced Programming can be credited as WP4/WP7 (PO 2021), or WP2/WP8 (PO 2010).

    The first lecture will happen on Thursday, 2022-10-20, 18:00--20:00 c.t., in Ludwigstr. 28 (Y) RG, Room 023.

    The second lecture will be on Thursday 2022-10-27, 18:00--20:00 c.t. at the same place.

    Times and dates for the lectures that follow will be discussed on 2022-10-20, so please attend the first lecture (or notify the lecturer if you can't come) if you care about coming to the following ones.

    Enrollment key: advaprogr2223


    Die Veranstaltung "Programmieren mit Statistischer Software (R)" wendet sich an Studenten im Bachelor Statistik (3. Semester). Sie baut auf die Veranstaltung "Einführung in die Statistische Software" (1. Semester) auf.

    Termine
    Termin Ort Person
    Vorlesung: Fr., 12:00 -- 14:00 c.t.
    Theresienstr. 37/39, B U136
    Binder, Bender
    Sprechstunde:
    t.b.a. Vsl. Online
    t.b.a.

    Einschreibeschlüssel: progr2223

    Einschreibeschlüssel: InfStat12022!

    Termin Ort Person

    Mo., 10 - 12 c.t.
    Geschw.-Scholl-Pl. 1 (A) - A 113
    Fritz

    Fr., 8 - 10 c.t. Geschw.-Scholl-Pl. 1 (A) - A 125
    Fritz

    Einschreibeschlüssel: sample2023

    Schedule

    • Class: Thursday, 12pm - 14 pm
    • Location: Geschw.-Scholl-Pl. 1 (A) - 214

    Enrollment key

    • The enrollment key is I2ML

    Selbsteinschreibung mit dem Passwort WiSo202223

    Instructor: Frauke Kreuter

    The seminar will be held in English on Tuesdays 10:00-11:30 over zoom. Please contact anna-carolina.haensch@stat.uni-muenchen.de with any questions. 

    Key: SoDa2022



    Eine Einschreibung in die Moodle-Seite ersetz nicht die Anmeldung zum Seminar. Zur Anmeldung bitte nur die zentrale Seminaranmeldung nutzen (siehe https://www.statistik.uni-muenchen.de/studium/lehrveranstaltungen/info_anmeldung_seminare.html)

    Selbsteinschreibung: Seminar_Klima

    Die Veranstaltung wendet sich an Studierende im Bachelor Statistik (3. Semester). Das "Grundlegende Praxisprojekt" (BA Statistik und Data Science - PO 2021) bzw. "Anfängerpraktikum" (BA - Statistik PO 2010) ist eine Pflichtveranstaltung. Der Bachelor-Studiengang Statistik (PO 2010) kennt weiterhin noch die Veranstaltung "Praxisprojekt", welches eine Wahlpflichtveranstaltung (Wahlplichtmodul 6) ist.

    Die Veranstaltung wird sowohl während der Vorlesungszeit als auch in den Semesterferien angeboten. Diese Moodle-Seite ist für beide Veranstaltungen.

    Selbsteinschreibungsschlüssel: APR2022


    Die Veranstaltung wendet sich an Studierende mit Hauptfach Statistik und Data Science bzw. Statistik. Das Fortgeschrittende Praxisprojekt (PO 2021) bzw. Statistische Praktikum (PO 2010) ist für Studierende im Bachelor Studiengang Statistik ein Pflichtbestandteil des Studiums. In Gruppen von 3-4 Personen werden Projekte aus der angewandten Statistik bearbeitet. In der Regel besteht ein Projekt aus statistischen Fragestellungen, die sich aus der Zusammenarbeit mit externen Kooperationspartnern ergeben.

    Die Veranstaltung wird sowohl während der Vorlesungszeit als auch in den Semesterferien angeboten. Diese Moodle-Seite ist für beide Veranstaltungen.

    Selbsteinschreibungsschlüssel: StatPrak2022

    Use the registration key (Einschreibeschlüssel) "bigDS22" to read more about the course.
    This course aims to foster the practice of software engineering and project management techniques in the context of data science and machine learning projects. 

    The target audience for this course is master's students from Computer Science, Statistics & Data Science.

    Organization

    • Lecturers: Bernd Bischl, Giuseppe Casalicchio, Susanne Dandl Florian Pfisterer
    • Time: During workshops: Thursday, 8-12 s.t (Only first weeks)
    • Place: CIP-Pool Institute for Statistics (1st Floor)
    • ECTS: 12 ECTS (e.g. as an alternative to the Statistical Consulting or Data Science Practical)

    Eligibility Requirements

    • Good programming skills in Data science-related languages (R, Python, Julia, C++, etc.)
    • Predictive Modelling, KDD, Deep Learning, or comparable Machine Learning courses

    Projects

    • Industry Projects with several Munich-based industry leaders
    • Research / Data Science for Social Good projects 

    Die Veranstaltung "Statistische Software (R)" wendet sich an Studierende im Bachelorstudiengang Statistik (1. Semester).

    Der Kurs wird vollständig online stattfinden.

    Einschreibeschlüssel: statsoft2223

    This course is closely related to the Generalized Regression Models (Generalisierte Regressions/GRM) course, also taught by Prof. Dr. Kuechenhoff. This course is, however, taught in English and designed for students currently attaining their master’s degree in statistics.

    We will take a closer look at generalized models, mixed models, Bayesian approaches, generalized additive models, survival analysis and error models. This is not an exhaustive list, but should provide you with a general idea of the contents of the course.

    If you have questions regarding the course, contact:

    Kuechenhoff, Helmut <kuechenhoff@stat.uni-muenchen.de>

    Rave, Martje <martje.rave@stat.uni-muenchen.de>


    Instructors
    Session
         Instructor
           Schedule
                 Rythm   
       Room                                           
    Lecture Helmut Küchenhoff Tuesday: 12:00 -14:00
    Thursday: 12:00-14:00
          weekly Geschw.-Scholl-Pl. 1 (M) - M 018/
    Geschw.-Scholl-Pl. 1 (A) - A 140
    Videos : https://cast.itunes.uni-muenchen.de/vod/playlists/5wDnYPs55a.html

    Work Shop         
    Martje Rave/
    Henri Funk
    Monday: 12:00-14:00
    Thursday: 10:00-12:00
          weeklySchellingstr. 3 (S) - S 001/
    Schellingstr. 3 (S) - S 001
    MeetingID: 976 3599 7286 Password: GreatStats
    Tutorial  Dielle Syliqi/
    Daniel Schlichting
    Thursday: 08:00-10:00
          weekly
    Geschw.-Scholl-Pl. 1 (A) - A 213


    Enrolment key: Stat_Model_ws_2023

    Syllabus
    Teacher:
    Walter J. Radermacher
    Runtime: 1. October 2022 - 13. October 2022
    Format: Self-learning through online videos in the first week and in-person workshops for practical appliance of use cases in the second week.
    Examination: Oral Exam (3 ECTS credits)
    Language: English


    Who is this course for? MsC Statistics and Data Science (2021, WP 28+29+40+46), BsC Statistics and Data Science (2021, WP 8+11), Statistik und Data Science als Nebenfach für Bachelor 30 ECTS (2021, WP 4+5), Statistik und Data Science als Nebenfach für Bachelor 60 ECTS (2021, WP 11+12), Statistik und Data Science als Nebenfach 30 ECTS Mathematik (2021, WP 5+6), Statistik Nebenfach 60 ECTS Bachelor Soziologie (2021, WP 10+11), Grundlegende Statistik als Nebenfach für MA 30 ECTS (2011, WP 6), Vertiefte Statistik Master (2011, WP 5)

    Self-Enrolment Key: StatsPublicGood#2223

    Schedule

    Time Lecturer Begin

    Lecture

    Monday, 8:30 - 10:00

    Prof. Dr. Heumann   

    24.10.2022
    Exercise course (Group 1)   
    Tuesday, 10:15 - 11:45   
    Sapargali 25.10.2022

    Tutorium

    Tuesday, 16:15 - 17:45

    Eleftheria

    25.10.2022

    Lecture

    Wednesday, 12:15 - 13:45

    Prof. Dr. Heumann   

    19.10.2022
    Exercise course (Group 2)   
    Thursday, 08:15 - 09:45   
    Garces Arias 20.10.2022


    Enrollment Key
    • The enrollment key is "stat_inf_ws2223"

    Schedule

    • Live sessions: tbd
    • Location: tbd

      Enrollment key

      • The enrollment key is "openscience22".


      Schedule

        Enrollment key

        • Enrollment key: sesame_street


        Audience:

        • Master Statistik: Räumliche Statistik (P 6.0.38/39)
        • Master Biostatistik: Räumliche Statistik (P 8.0.24/25)
        • Master Statistik WiSo: Räumliche Statistik (P 7.0.18/19)
        • Master Statistics and Data Science: Spatial Statistics (WP45)
        • Master Data Science ESG: Elective (WP1 to WP5)

        Date/Time:

        • Monday 10–12
        • Wednesday 14–16

        Language:

        • The lecture will be in English, unless all students agree to German

        Enrollment key:

        • Enrollment key is "Matern".

        Termine

        • Vorlesung: Dienstag, 16 - 18 c.t.
        • Übungen (Statistik I): 
          Mittwoch, 12 - 14 c.t. & 14 - 16 c.t.
          Donnerstag, 10 - 12 c.t. (2x), 12 - 14 c.t. (2x), 18 - 20 c.t.
        • Übung (Statistik II):
          Donnerstag, 08 - 10 c.t.

        Einschreibeschlüssel

        • Der Einschreibeschlüssel lautet "22wiwistat23".


        Zielgruppe
        Bachelor Statistik (PO 2010) 3. Semester 

        Die Veranstaltung wird als Inverted Classroom durchgeführt.

        Termine
        • Dienstag 10–12 (Vorlesung)
        • Donnerstag 12–14 (Vorlesung)
        • Donnerstag 14–16 (Übung)
        • Dienstag 18–20 (Tutorium)

        Einschreibung

        Einschreibeschlüssel: Bayes