This course will give an introduction into the topic of Machine Learning Operations, also known as MLOps. It will be based on the open source material from the Technical University of Denmark and cover the machine learning lifecycle from the design to the model development and until operations.

Einschreibeschlüssel: mlops23

Key: dcqd2023 (Einschreibeschlüssel)

General Information

The class will take place as a block seminar from September 18 to 21 and September 25-28 (9am-12pm; 2pm-5pm) and will be taught online over Zoom in English.

To sign up for the class just sign up for this moodle class. The official registration will then be done with the exam registration. At the end of the seminar, students will be required to take an oral exam.

If you have any questions, please contact anna-carolina.haensch@stat.uni-muenchen.de 

Rough course Outline: (more detailled Syllabus in week before seminar starts)

Week 1 - Data Collection (Prof. Sakshaug)

Short Course Description

The social survey is a research tool of fundamental importance across a range of disciplines and is widely used in applied research and as evidence to inform policy making. This course considers the process of conducting a survey, with an emphasis on practical aspects of survey data collection, as well as factors that influence the quality of survey data. The course will also cover key statistical concepts and procedures in sample design and estimation.

Morning session (9-12) and Afternoon session (2-5):

Live lectures, discussion, and readings

Week 2 - Questionnaire Design (Prof. Kreuter)  

Short Course Description

This course introduces students to the stages of questionnaire development. The course reviews the scientific literature on questionnaire construction, the experimental literature on question effects, and the psychological literature on information processing. It also discusses  the relationship between mode of administration and questionnaire design.

Morning session (9-12):

Self-study, including lecture videos and multiple readings per day (will be made accessible via moodle)

Afternoon session (2-5):

Exercises, quizzes, discussion sessions building on the material of the morning session. Students are required to submit questions on the readings.



Entropy is defined as a measurable physical property that is most commonly associated with a state of disorder, randomness, or uncertainty. It is strongly connected with probability distributions and the principle of maximum entropy can be very useful in statistical inference, in particular in Bayes statistics. In this course we will introduce the concept of entropy in the context of information theory as well as apply the concepts on real data sets.

Syllabus

  1. Introduction and Preview
  2. Entropy, Relative Entropy, and Mutual Information
  3. Asymptotic Equipartition Property
  4. Entropy Rates of a Stochastic Process
  5. Differential Entropy
  6. Information Theory and Statistics

Schedule:

day/timelocationinstructor  start date
Lecture/Tutorial            Mon, 12:00-14:00       Geschw.-Scholl-Pl. 1 (B)/ B (106)     Farsani             17.04.23
Lecture/TutorialThurs, 16:00-20:00Geschw.-Scholl-Pl. 1 (M) / M 010Farsani20.04.23


Information Theory and Entropy for Master Statistics and Data Science, Master Data Science, Master Statistik, Master Biostatistik, Master Statistik WiSo.

Enrolment Key: Shannon23

This course will discuss essential research techniques in statistics and data science, also preparing students for successfully participating in seminars and writing a thesis. (5 sessions, Friday afternoon)

The course is part of the Bachelor’s programmes in Statistics and Data Science (150 major or 60 ECTS minors: `Methoden und Techniken des wissenschaftlichen Arbeitens’,  P16.1, and  WP 12.1 / WP 13.1, respectively ). It is also open to all other Bachelor’s students (4th semester or higher) and all Master’s students.

Einschreibeschlüssel: Zitieren

Beginn der Ringvorlesung ist am 4. Mai; ein Termin wird sich jeweils auf knapp zwei Zeitstunden erstrecken (16.05 bis 18.00 Uhr).

Die Ringvorlesung gibt einen Überblick über verschiedene Themengebiete der Statistik, die in den spezifischen Modulen nicht entsprechend behandelt werden können.

Geplant sind

* verschiedene Gastvorträge aus der Berufspraxis

* einige Vorträge zur Geschichte der Statistik und der Künstlichen Intelligenz inklusive ihrer Grundlagen

* Überblicksvorträge über Teilgebiete der aktuellen statistischen Forschung und damit über die verschiedenen Spezialisierungen im Masterstudium

* ein Themenblock zu Kommunikation statistischer Ergebnisse, Datenjournalismus und Open Science

Einschreibeschlüssel: Ringvorlesung

This is an overview page for all courses in official statistics offered this summer semester.

Enrollment Key: EMOS

enrolment key: why?

Person: Dr. Cornelia Oberhauser

SAS-Kurs als 5-tägiger Blockkurs in den Semesterferien

Termine:

Tag

   Uhrzeit
   Raum
Mo 18.09.2023
Vorlesung9:15 - ca. 12:15
online über Zoom

Übung
13:15 - 17:00
online über Zoom
Di 19.09.2023
Vorlesung
9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Do 21.09.2023
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Mo 25.09.2023
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Di 26.09.2023
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom


Gastschlüssel

  • Der Gastschlüssel lautet: "saskurs2023"

Key: reg-cor-dat
Course kick-off  20.04. with an introductory in-person lecture.

Lecture Q&A / Exercise class
(Scheipl/Sapargali)
Thursday
14:15-15:45

 Schellingstr. 3 (S) - S 001


Format:
Inverted classroom: You are expected to come prepared for both the Q&As and exercise classes -- you've watched the lecture videos, you've reviewed the slides/exercise sheets, you've asked ChatGPT to explain parts you found confusing, you've done the self-assessment quizzes, and you've posted questions you would like to see addressed during the session in the Moodle Forum. 


  • Der Einschreibeschlüssel ist StoSta23
  • Vorlesungen aus vorigen Semestern auch als aufgezeichnete Videos bei LMUcast (s.u.) verfügbar. Multiple Choice Quizzes zur Selbstkontrolle auf moodle.
  • Für die Übungen gibt es Musterlösungen als PDF und Fragestunden zur Übung in vorraussichtlich 2 Gruppen  Mittwochs und Donnerstags.


  • Termine
    Vorlesung: 
      Di 12-14 @ S 002 (Schellingstr. 3)  & Do 12-14 @ S 003 (Schellingstr. 3) 

    Übung/Fragestunde: 
      Mi 12-14 (E 004 HGB), Mi 14-16 (E 004 HGB), Do 14-16 (E 004 HGB)

Schedule:

date/time location instructor
Kick-off meeting April 26, 18:00 - 19:00 Old Library, Room 245, Ludwigstr. 33 Wilhelm
Presentations at end of semester (date tbd) tbd Wilhelm
Enrolment key: semml23

Schedule:

day/time location instructor start date
Lecture/Tutorial Mon, 16:15-18:00 Schellingstr. 3 (S) / S 006 Wilhelm 17.10.22
Lecture/Tutorial Wed, 10:15-12:00 Geschw.-Scholl-Pl. 1 (M) / M 010 Wilhelm 19.10.22

Enrolment key:
 ml23

Syllabus:

  1. Overview
  2. Fundamentals and Properties of Stochastic Processes
  3. Univariate ARIMA-Processes
  4. Estimation and Forecasting of ARIMA-Models
  5. Univariate GARCH-Models + Extensions
  6. Selected aspects: Long Memory und Fractional Differencing, Threshold-Models

Intended audience: Advanced bachelor and master students of statistics, mathematics, computer science (Informatik), economics and business administration.

Prerequisites: Solid mathematical foundations (analysis and linear algebra), basic knowledge in econometrics (econometrics 1) or statistics (linear models).

Record of Achievement:

This course grants 6 ECTS and can be credited for Zeitreihen (WP7, BA Statistik, PO 2010),  Time Series (WP 18,  MA Statistics and Data Science, PO 2021), Zeitreihen (P 6.0.27, MA Statistik, PO 2010), Zeitreihen (P 8.0.32, MA Biostatistik, PO 2010), Zeitreihen (P 7.0.5, MA WiSo Statistik, PO 2010) and Time Series (WP18, MA Statistics and Data Science)

Students of other courses of study (mathematics, computer science, etc.) or external students (e.g. TUM) may take part in the final exam and receive a grade as well. However, you have to clarify the crediting modalities on your own with your Prüfungsamt beforehand.

Key: utsa23

Einschreibeschlüssel: Infstat22023!

Dieser Kurs ist eine Wiederholung ausschließlich für die alte Prüfungsordnung 2010 für den Bachelor Statistik. Insofern der Kurs Wahrscheinlichkeitstheorie und Inferenz I noch nicht bestanden worden ist, empfehlen wir dringend auf die neue Veranstaltung zur Inferenz auszuweichen.

Hierfür wird direkt zu Beginn des Semesters Material in Form eines Skriptes, Vorlesungsfolien und Übungsblätter mit deren Lösungsansätzen online gestellt und in einer monatlichen Fragestunde besteht die Möglichkeit auf einzelne Dinge einzugehen und Fragen zu stellen. 

Außerdem wird es in der Woche vor der Klausur ausführlicher Fragen zu stellen und Dinge zu wiederholen. 

Diesen Kurs zu belegen, bedeutet also ein hohes Maß an Eigenverantwortung.

Ich empfehle dringendst zu der Einführungsstunde am Donnerstag, den 20.04. um 10 - 12 Uhr zu kommen, nachdem da das Format von dem Kurs erklärt wird.


Der Einschreibeschlüssel ist: Wiederholungskurs

Schedule

Time Lecturer Begin
Exercise course (Group 1)   
Tuesday, 10:15 - 11:45   
Sapargali, Garces Arias
25.04.2023

Lecture

Tuesday, 12:15 - 13:45

Prof. Dr. Heumann   

18.04.2023

Exercise course (Group 2)

Wednesday, 08:15 - 09:45 
Sapargali, Garces Arias

26.04.2023
Tutorium  
Friday, 08:15 - 09:45   
Eleftheria 21.04.2023

Lecture

Friday, 10:15 - 11:45

Prof. Dr. Heumann   

21.04.2023


Enrollment Key
  • The enrollment key is "stat_inf_s23"

  • This lecture covers the basics of Bayesian statistics and its practical applications
  • The lecture is held in English. It will be held online too. 
  • The course may be taken by students with a minor in "Statistik" or "Statistik und Data Science", as well as a major in "Statistik" (Prüfungsordnung 2010). 


Schedule:

day/timelocationinstructor  start date
Lecture/Tutorial            Fri, 12:00-16:00       Schellingstr. 3 (S) - S 007     Farsani             21.04.23


Introduction to Bayesian Statistics for Bachelor and Minor Statistics and Data Science

Registration key: Bayes23

The course  Analysis of high-dimensional biological data (formerly known as Statistische Methoden in Genomik und Proteomik/Statistical methods for biological high-throughput data) will cover important statistical methods and concepts for the analysis of high-dimensional biological high-throughput data. We will focus on bulk RNA-Seq, single-cell RNA-Seq, proteomic, metabolic, and, in particular, microbiome  such as 16S rRNA and other amplicon data. 


Termine:

TerminOrtPersonBeginn
VorlesungMo, 9:15-11:45Geschw.-Scholl-Pl. 1 (E) / E 004Hoffmann/Boulesteix17.04.23
Übung Di, 10:15-11:45Geschw.-Scholl-Pl. 1 (M) - M 109Rehms18.04.23

    Einschreibeschlüssel
    • Der Einschreibeschlüssel lautet: "DAS2023"
    Attention: In the first two weeks of the semester, the time slot of the exercise session will be used to give lectures. As a consequence, there will be no exercise sessions in the first two weeks, but two additional lectures will take place on the 18th of April and the 25th of April. 

    The enrolment key for the course is: FortStatSoftNF.

    If you have questions please contact statprog@stat.uni-muenchen.de.

    The course will be taught in English, more information can be found in the course itself.

    The enrollment key is: Ganter


    Termine (Beginn jeweils c.t.):



    Mo
     12 - 14
    Schellingstr. 3 (R) / R 312

    Di
     08  - 10    
    Geschw.-Scholl-Pl. 1 (B) / B 006

    Do
     14 - 16
    Geschw.-Scholl-Pl. 1 (M) / M 118



    Enrolment key: StatModSs23

    Session
         Instructor
           Schedule
                      Rythm   
       Room                                           
    LectureHelmut KüchenhoffMonday:
    14:00-16:00
    Thursday: 12:00-14:00
           weeklyMonday: Geschw.-Scholl-Pl. 1 (M) - M 218
    Thursday: Geschw.-Scholl-Pl. 1 (B) - B 101
    Live zoom: Meeting ID:  6846774868 Password: 654336
    Videos : https://cast.itunes.uni-muenchen.de/vod/playlists/5wDnYPs55a.html

    Work Shop         
    Martje Rave

    Thursday: 10:00-12:00
           weeklyThursday: Schellingstr. 3 (S) - S 001 
    Tutorial
    Thursday: 8:00-10:00
          weekly

    Thursday: Geschw.-Scholl-Pl. 1 (A) - A 021


    Persons und Dates


       Time   Place   Lecturers 
     Lecture   Mo 4-6pm   Geschw.-Scholl-Pl. 1 (M) - M 014 / Zoom  Mina Rezeai, David Rügamer 
     Lab Session   Tue 10-12am   Geschw.-Scholl-Pl. 1 (A) - A 120 / Zoom 
     Anil Gündüz



    Enrollment key: 
    learnDL

    Course Details

    The course (timeline) will be project-based with individual meetings between project supervisor and students

    Enrollment key: 
    applyDL

    Personen und Termine


     Vorlesung Mo 10–12 Schellingstr. 3 (S) - S 004  David Rügamer
     Di 10–12 Schellingstr. 3 (S) - S 005
     Übung Gruppe 1 Mo 14–16 Schellingstr. 3 (S) - S 005
     Viet Tran + Dominik Kreiß
     Übung Gruppe 2 Mi 8–10 Geschw.-Scholl-Pl. 1 (E) - E 004
     Tutorium Di 16–18 Geschw.-Scholl-Pl. 1 (M) - M 114 
     Michael Kobl
     Hausübung - . Max Lang

    Einschreibeschlüssel: 
    WahrGrundStoff

    Decision theory deals with rational decisions under uncertainty. It has high interdisciplinary importance, for example, in the analysis and support of decisions in business administration or finance (e.g. investment strategies), economics or sociology (rational choice theory), medicine (e.g. expert systems) or engineering (e.g. autonomous control). Moreover, statistical decision theory can be seen as a formal framework for choosing analysis methods (optimal tests or estimators, best classification algorithms, etc.). This general view, understanding statistics and machine learning as special cases of decision theory, plays a fundamental role in the critical analysis and problem-adequate generalization of any data-based learning procedure.

    The course first discusses the general structure of decision problems, including fundamental decision principles. Then it analyzes and characterizes the Bayes and minimax criteria as extreme poles to deal with (state) uncertainty and develops modern alternatives in the context of complex uncertainty (ambiguity). In the second part, an overview of other decision-theoretic topics is given, also introducing to current research in decision theory.




    The enrollment key is: A/DT23

    Enrolment Key

    SDS-Methods-23

    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: Wednesday, 12:00-14:00 c.t.
    • Location: Geschw.-Scholl-Pl. 1 / A 119

    Enrolment Key

    SL_s23

    Target Audience 

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


    Participants in this course will be automatically added based on the seminar assignments done by Dr. Schollmeyer.

    If you have questions, write to ruegamer@stat.uni-muenchen.de

    Bachelor Seminar "Introduction to Causal Inference"

    Enrolment key: ci_seminar_2023

    In this seminar, we’ll learn about the core statistical and philosophical concepts related to causal inference and explore some of the techniques that have been developed to answer causal questions based on data.

    Seminar degree module

    The important thing is that students have some experience in spatial statistics, so preference will be given to:
    Master Statistics and Data Science PO 2021 Module: Methodology and Modelling
    (If there is capacity, we can include students who follow other modules, but please email me and I will add you to the waiting list: martje.rave@stat.uni-muenchen.de)

    Enrolment Key

    ArealData23

    Content

    Areal data is a data format in which point observations are aggregated over subregions of a predefined space. These subregions are non-overlapping and make up the entire space. This format is predominately common in medical research and one of the central formats in which, for example, data on Corona infections and hospitalizations were available. Due to the loss of information on the specific point of observation, the estimation of the spatial correlation can become a bit trickier than in spatial point processes. In recent years, especially during the pandemic, working with this type of data has become more relevant for data scientists and statisticians not only due to the relevance of their context but also due to somewhat recent advancements in research on network or graph theory, which allowed research on the statistical methods designed to work with this data more diverse.


    We will start of by going over the basic concepts in areal data analysis from Moran’s I over to a brief gloss over Markov Random Fields. With this we can then explore more classical methods on areal data analysis, such as Bayesian MCMC methods in the Besag model framework, up to models which have been developed in recent years and include more theoretical concepts of graph theory. It is also possible to explore some machine learning models designed to work with areal data as well as taking a closer look at integrated nested Laplace approximation (INLA).

    Format

    • Language In English. English by default.
    • Attendance is mandatory for every session. If you miss more than one session without providing a reasonable excuse in time, you won’t pass.
    • Where: Probably offline, possible to do go hybrid, if needed.
    • Schedule:
    • Kick off (within the first two weeks of the semester) we will talk about the basics of spatial statistics
    • Phase 1: Foundations (2 weeks)
      • In the first 2 weeks after the kick off, we’ll meet weekly (!) to discuss a specific section of Spatial Statistics and Modeling (Gaetan) We’ll work through chapter 5.2 and 5.3 as well as some excerpts of Applied Spatial Data
      • Analysis with R (Bivand) at first to establish a foundation for us to work with.
    • Phase 2: Book club (6 weeks)
      • You will be asked to present a paper out of a selection of papers to the other students (45 mins to 1 hour) thereafter we will discuss the content of the presentation (30%= 20% presentation+ 10% discussion)
    • Phase 3: Implementation (3 weeks)
      • You will be asked to pick a dataset out of the ones provided or find your own in which you will apply one of the methods you found interesting in the book club phase.
    • Phase 4: Presentations (1 week)
      • You will hold a short presentation 10-15 mins about your project. (30%=20% presentation+ 10% discussion)
    • Phase 5:
      • You will write and hand in a final report on your implementation; this does not have to be handed in before the end of the semester, but should be within 2 or 3 weeks of the last day of lectures. (Not more than 10 pages) (40%)

    Grading

    • Presentation paper+ discussion: 30%
    • Presentation work+ discussion: 30%
    • Thesis: 40%

    References

    Phase II:

    • Applied Spatial Data Analysis with R (Roger S. Bivand)
    • Spatial Statistics and Modeling (Gaetan)

    Phase III:

    Tbd

    Phase IV:

    Tbd


    Termine und Personen:

    Termin   Ort Person
    Vorlesung  
    Mo, 12.00 - 14.00
      Geschw.-Scholl-Pl. 1 - E 216    
    Küchenhoff / Bender
    Vorlesung
    Do, 08.30 - 10.00
      Schellingstr. 3 - S 004
    Küchenhoff / Bender
    Übung
    Di, 10.00 - 12.00 
      Geschw.-Scholl-Pl. 1 - F 007
    Rave / Weigert
    Übung
    Do, 14.00 - 16.00
      Geschw.-Scholl-Pl. 1 - F 007
    Rave / Weigert
    Tutorium
    Di, 12.00 - 14.00  
      Geschw.-Scholl-Pl. 1 - A 014
    Alber

    Einschreibeschlüssel: LiMo23



    Schedule

               Type          

              Date        

          Location        

      
    Initial Meeting

       May 2nd

    9:00-12:30

               Seminar Room 144.     

    Ludwigstr. 33



    Der Einschreibeschlüssel für den Kurs ist Stat2SozNF. Bei Fragen wenden Sie sich bitte an sarah.ball@stat.uni-muenchen.de. Eine Einschreibung übers LSF ist nicht notwendig.

    Grundkurs  
    Mo, 05.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Grundkurs  
    Di, 06.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Grundkurs  
    Mo, 12.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Grundkurs 
    Di, 13.06., 16:00 - 19:00
    Ludwigstraße 28 RG, Raum III (023)
    Grundkurs 
    Mo, 19.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Grundkurs 
    Di, 20.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Aufbaukurs  
    Mo, 26.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    Aufbaukurs  
    Di, 27.06., 16:00 - 19:00Ludwigstraße 28 RG, Raum III (023)
    AufbaukursMo, 03.07., 16.00 - 19.00
     Ludwigstraße 28 RG, Raum III (023)
    Aufbaukurs Di, 04.07., 16.00 - 19.00
     Ludwigstraße 28 RG, Raum III (023)

    Class + Exercise
    • Time: Wednesday, 12:15-13:45
    • Location: Geschw.-Scholl-Pl. 1 (D) - D 209
    Tutorial
    • Time: Tuesday, 14:15-15:45
    • Location: Schellingstr. 3 (S) - S 004

    Enrollment key
    • The enrollment key is I2ML

    Dates:

    DatePlacePersonStart
    Lecture Wed, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) / A 021 Nagler18.04.23
    Lecture/Exercise Thu,  10:15-11:45Geschw.-Scholl-Pl. 1 (B) / B 106 Nagler/Palm 19.04.23

    Enrolment
    • The enrolment key is: "rademacher"

    Schedule (Q&A + exercise session):

    Wednesday, 10:15 - 11:45

    Enrollment key (please only enroll if you want to ACTIVELY participate, you will be automatically unenrolled after 3 weeks of inactivity):

    s23_advml


    Schedule

                    Type                        Date             Location              Start     
    Lecture

        Wednesdays

     14:00-15:45

        Geschw.-Scholl-Pl. 1       

    (M 203)

     04.19.2023
    Lecture/Tutorial

        Fridays

       08:15-10:00

       Geschw.-Scholl-Pl. 1        

     (B 015)

     28.19.2023

    Enrolment Key:  ectheory23



    See this document for a description of the seminar.

    All other information TBA.

    Termine:

    Termin Ort Person Beginn
    Vorlesung Di, 08:15-09:45 tbd
    Nagler 18.04.23
    Vorlesung Mi, 10:15-11:45 tbd
    Nagler 19.04.23
    Übung  Mo, 16:15-17:45 tbd
    Schiele
    24.04.23

    Einschreibeschlüssel
    • Der Einschreibeschlüssel lautet: "linalg"

    Inferring large-scale networks from measurement data has become a standard task in many areas of science, ranging from social psychology to computational biology. In particular,  when analyzing so-called “omics” data, e.g., genomics, proteomics, and metabolomics, network construction based on penalized covariance and inverse covariance matrix estimation (also known as graphical models) play a pivotal role. This stems from the fact that omics (and other high-throughput) data are typically high dimensional where the number of variables p (genes, species, metabolites) far exceeds the number of samples n.  The seminar will start with basic methods for estimating large-scale covariance matrices in the high-dimensional regime before looking at advanced estimation techniques that lead, for instance, to improvements in performance and speed. We also discuss approaches for model selection, which are needed to make a graph sparse and thus interpretable. Standard techniques such as K-fold cross-validation or the AIC work well for low-dimensional data but are not suitable in the high-dimensional setting. Finally, we consider extensions of the former models, including latent variable graphical models, or methods designed for compositional data such as microbiome data obtained from high-throughput sequencing.  A first meeting will take place in May 2023 (hybrid; will be scheduled in agreement with the participants), where the seminar topics are briefly introduced and assigned to the participants. The main part of the seminar (2-3 days) with the presentations and discussion will be at the end of the semester. The participants will have to give a 45 minute presentation and write a summary of their topic. 

    Seminar type: Block-type and in-person
    Language: The seminar will be held in English
    Target group: Master in Statistics
    Recognition possibilities: Biostatistics, Machine Learning, Methodology and Modeling

    Self enrollment: glasso23

    Schedule
    Dates / Time
    Location
    Initial Meeting
    May (TBD)
    TBD
    Seminar / Presentations
    July (TBD)
    TBD


    Termine

    • Vorlesung & Übung: Freitag, 09 - 12 c.t.

    Einschreibeschlüssel

    • Der Einschreibeschlüssel lautet "miniconda3".


    Termine

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

    Einschreibeschlüssel

    • Der Einschreibeschlüssel lautet "20wiwistat23".


    Termine:

    Termin Ort Person Beginn
    Vorlesung Di, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) - A 021 Hoffmann/Kümpel/Garces Arias 18.04.23
    Vorlesung Mi, 10:15-11:45 Geschw.-Scholl-Pl. 1 (A) - A 125 Hoffmann/Kümpel/Garces Arias 19.04.23
    Übung Do, 10:15-11:45 Geschw.-Scholl-Pl. 1 (A) - A 022 Kümpel/Garces Arias 27.04.23
    Tutorium Mo, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) - A 016 Kraft 24.04.23
    Achtung: In den ersten beiden Vorlesungswochen werden die möglichen Übungstermine am Donnerstag (10:15 - 11:45 und 12:15 - 13:45) für die Vorlesung genutzt. Es finden also keine Übungen in den ersten beiden Vorlesungswochen statt, aber dafür doppelt so viele Vorlesungen (also insgesamt 8 Vorlesungen in den ersten zwei Wochen)!

    Einschreibeschlüssel:
     multi_verfahren_s23