[s22] Introduction to Python For Financen

In this course, we will introduce basic python concepts and explore their usage in financial econometrics. Statistical modelling of any kind always requires data. Thus, being able to handle raw data sets, i.e. data cleaning, data structuring, etc., is an essential task which has to be done at the beginning of every project. The first part of the course will focus on the most popular tools that python offers (pandas, numpy, matplotlib, datetime, etc.) in order to tackle the aforementioned tasks .

The second part of the course introduces financial time series. Here, we will discuss their unique characteristics also known as "stylized facts" and different approaches on how to model them, in particular ARMA and GARCH processes will be of interest. If time permits, we will also peek into quantitative risk management and portfolio optimization.

The course starts on the 10th of October.



Zusatzprüfung Einführung in die Statistische Software (R)

Zusatzprüfung "Einführung in die statistische Software (R)" für Studierende nach PO 2010

Einschreibeschlüssel: statsoftRPO2010

Ausgewählte Themen der nichtparametrischen und der robusten Statistik

Ausgewählte Themen der nichtparametrischen und der robusten Statistik

Das Seminar behandelt ausgewählte Themen der nichtparametrischen bzw. verteilungsfreien Statistik (beispielsweise statistische Tests) sowie Methoden der robusten Statistik (z.B. Ausreißererkennung bzw. Verallgemeinerungen des Medians mit Hilfe des Konzepts der Datentiefe oder robuste L-Momente)

Seminar geblockt am Ende des Semesters (nach Ende der Vorlesungszeit)


[s22] Analysis of high-dimensional biological data



[s22] Data Collection and Questionnaire Design

Blockveranstaltung: 15.-18. August und 22.-25. August (9am-12pm; 2pm-5pm)

Einschreibeschlüssel: DCQD2022

[s22] Wissenschaftliches Arbeiten in der Statistik

Ziel dieses Kurses ist es, Studierenden elementare Techniken des wissenschaftlichen Arbeitens in der Statistik näher zu bringen.

Zielgruppe:
Bachelorstudierende der Statistik ab dem vierten Fachsemester und interessierte Masterstudierende der Statistik.

Dozentin: Cornelia Fütterer

Erster Termin:
27.04.2022 von 08:30-10:00 Uhr

Weitere Termine nach Absprache zu Kursbeginn. 

ECTS-Punkte:
Dieser Kurs ist ein Angebot zur Vorbereitung auf Seminare und Abschlussarbeiten. Es können daher keine ECTS-Punkte erworben werden.


Einschreibeschlüssel: wissArb22


[s22] Stochastik und Statistik (Scheipl)

  • Der Einschreibeschlüssel ist StoSta22
  • 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 & Do 12-14 @ M010 (Hgb)

    Übung/Fragestunde:
      Mi 12-14 (A 014 HGB), Mi 14-16 (A 014 HGB)

[SS 22] Deep Statistical Metric Learning (Rezaei)

General Information

Overview

Deep Metric learning aims to learn effective distance or similarity measures between arbitrary objects with the success of deep learning. The statistical deep metric learning goal is to learn statistical representation based on data distribution, density function and maps objects into an embedded space with more statistical information.  It’s an important topic in both natural language processing and computer vision and has been applied to a variety of tasks, including Grammar correction, and fine-grained image retrieval, object ranking, etc.

In this seminar, we will learn about the theory of deep metric learning and will review some state-of-the-art methods. We will offer different topics with different applications (i.e. NLP, CV, bioinformatics) for a variety of tasks (i.e. clustering, representation learning, density modeling, ranking, information retrieval, etc).  We plan to work on the extension of three categories:

  1. Contrastive Approaches: Contrastive Loss, Triplet Loss, Improving the Triplet Loss 
  2. Moving Away from Contrastive Approaches: Center Loss, Sphere Face 
  3. State-of-the-art Approaches: CosFace, ArcFace, AdaCos Sub-Center ArcFace, ArcFace with Dynamic Margin.

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 a deep metric learning 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)

Key: Seminar_DML

Seminar: Blocked towards the end of the semester, 

Kick-off and Lecture:29.04.2022,  9:00- 11:00

Weekly Meeting: Fridays 9:00- 11:00


Zoom: 

Virtual Room 





[s22] Einführung in die statistische Datenanalyse (Bauer, Weigert)

KursZeit
Ort
Grundkurs        
Mo, 16.05., 16:00 - 19:00       
Ludwigstraße 28, IuK-Pool (207)           
Grundkurs  
Di, 17.05., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Grundkurs  
Mo, 23.05., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Grundkurs  
Di, 24.05., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Grundkurs  
Mo, 30.05., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Grundkurs 
Di, 31.05., 16:00 - 19:00
Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs 
Mo, 13.06., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs 
Di, 14.06., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs  
Mo, 20.06., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)
Aufbaukurs  
Di, 21.06., 16:00 - 19:00Ludwigstraße 28, IuK-Pool (207)

[s22] Statistical pitfalls in biomedical research (Hoffmann/Boulesteix)

Termine:

Termin Ort Person Beginn
Vorlesung Di, 14:15-15:45 Geschw.-Scholl-Pl. 1 (D) - D 209 Hoffmann/Boulesteix 26.04.22
Übung Fr, 14:15-15:45 Geschw.-Scholl-Pl. 1 (D) - D 209 Rehms 06.05.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "StatisticalPitfalls22"

[s22] Diagnostic accuracy studies (Hoffmann/Boulesteix)

Termine:

Termin Ort Person Beginn
Vorlesung Mo, 9:15-11:45 Geschw.-Scholl-Pl. 1 (E) / E 004 Hoffmann/Boulesteix 25.04.22
Übung Di, 10:15-11:45 Geschw.-Scholl-Pl. 1 (M) - M 109 Rehms 03.05.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "DAS22"

[s22] Multivariate Verfahren (Hoffmann)

Termine:

Termin Ort Person Beginn
Vorlesung Mo, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) / A 125 Hoffmann 25.04.22
Vorlesung Di, 10:15-11:45 Geschw.-Scholl-Pl. 1 (A) / A 021 Hoffmann 26.04.22
Übung Do, 12:15-13:45 Geschw.-Scholl-Pl. 1 (A) - A 022 Garces Arias 28.04.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "Multi2022"

[s22] Seminar: Introduction to Causal Inference (Scheipl)

Login: please contact Fabian Scheipl


Dates & Overview

Kickoff: tba

Target groups: Statistics B.A.

Course Description

"In many applications of statistics, a large proportion of the questions of interest are fundamentally questions of causality rather than simply questions of description or association. For example, a medical researcher may wish to find out whether a new drug is effective against a disease. An economist may be interested in uncovering the effects of a job-training program on an individual’s employment prospects, or the effects of a new tax or regulation on economic activity. A sociologist may be concerned about the effects of divorce on children’s subsequent education."
G. Imbens

So, classical statistics mostly answers questions about associations or correlations between measured data. However, many – if not most – important questions are not about mere associations (“Does B occur more or less frequently together with A?”), but about actual causes (“Is B caused by A?”).

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.


[s22] Econometric Theory (Czudaj)

Termine

  • Vorlesung: Mittwoch, 14:00 - 16:00 Uhr,
  • Übungen:  Freitag, 8:00 - 10:00 Uhr

Einschreibeschlüssel

  • Der Einschreibeschlüssel lautet "oek_s22".

[s22] Programmieren mit statistischer Software (R) (Bender/Burk/Niebisch)

Die Veranstaltung "Programmieren mit Statistischer Software (R)" wendet sich an Studierende im Bachelor Statistik. Sie baut auf den Veranstaltungen "Einführung in die Statistische Software" (1. Semester) und "Statistische Software" (2. Semester) auf.

Die Veranstaltung findet vom 25.04.2022 ausschließlich online statt und verläuft nach dem Inverted Classroom Prinzip.

Einschreibeschlüssel: progr2022

[s22] Multivariate Time Series Analysis (Czudaj)

Syllabus

  1. Introduction to Stochastic Processes
  2. Autoregressive Moving Average Processes
  3. Estimation of Vector ARMA Models
  4. Prediction
  5. Testing for Causality
  6. Innovations Accounting
  7. Structural VAR

Intended audience: Advanced students and PhD students in econometrics, statistics, VWL, BWL, mathematics or computer science.

Prerequisites: Profound knowledge in matrix-algebra and econometrics (econometrics I) or statistics (linear models). Basic knowledge in univariate time series analysis is not demanded but of advantage.


Time Schedule

The lectures and tutorials take place between 07.06.2022 (first lecture) and 25.07.2022.


Enrollment key: MTSA2022SoSe



[s22] Regression for Correlated Data (Scheipl)

Key: reg-cor-dat
Course kick-off  on 19.04. with an introductory in-person lecture.
The remainder of the semester will be inverted classroom style.

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

 Schellingstr. 3 (S) - S 001


Format:

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 the 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.


[s22] Statistical Modeling of Political Networks (Fritz, De Nicola, Mehrl)

[s22] Einführung in die Wahrscheinlichkeitsrechnung und induktive Statistik (Schollmeyer, Windmann)

Termine:

TerminOrtPersonBeginn
Vorlesung (14 tägig)Mo, 14:15-15:45Geschw.-Scholl-Pl. 1 (M) - M 018Schollmeyer, Windmann25.04.22
VorlesungDo, 12:15-13:45Geschw.-Scholl-Pl. 1 (E) - E 004Schollmeyer, Windmann28.04.22
Vorlesung Do, 10:15-11:45Geschw.-Scholl-Pl. 1 (E) - E 004Schollmeyer, Windmann28.04.22
Übung 1Di, 08:15-09:45Schellingstr. 3 (S) - S 005Wicht03.05.22
Übung 1 (14 tägig)Di, 12:15-13:45Schellingstr. 3 (S) - S 005
Wicht03.05.22
Übung 2Mi, 10:15-11:45
Geschw.-Scholl-Pl. 1 (A) - A 016
Blocher04.05.22
Übung 2 (14 tägig)Mi, 08:15-09:45
Geschw.-Scholl-Pl. 1 (A) - A 016
Blocher04.05.22
 TutoriumDi, 14:15-15:45Schellingstr. 3 (S) - S 005 Musiol12.05.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "s22_stat2_PO2010"

[s22] Methoden der linearen Algebra in der Statistik (Nagler)

Termine:

TerminOrtPersonBeginn
VorlesungDi, 08:15-09:45Geschw.-Scholl-Pl. 1 (D) - D 209Nagler26.04.22
VorlesungMi, 10:15-11:45Schellingstr. 3 (S) - S 005Nagler27.04.22
Übung Mo, 16:15-17:45Geschw.-Scholl-Pl. 1 (D) - D 209Blocher
25.04.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "linalg"

[s22] Statistical Inference

Schedule

Time Lecturer Begin

Exercise course   

Wednesday, 08:15 - 9:45   

Sapargali
04.05.2022

Lecture
Tuesday, 12:15 - 13:45
Prof. Dr. Heumann   

26.04.2022

Tutorium
Friday, 08:15 - 09:45
Eleftheria
29.04.2022

Lecture  

Friday, 10:15 - 11:45
Prof. Dr. Heumann
29.04.2022


Enrollment Key
  • The enrollment key is "stat_inf_ss22"

[s22] Fairness in Machine Learning Applications

Key: Fairness2022

[s22] Wahrscheinlichkeitstheorie und Inferenz II (Schollmeyer)

Zielgruppe: Bachelorstudierende der Statistik (4. Semester)

Termine - Vorlesung
Dienstag 16:00 c.t. - 18:00 Uhr Geschw.-Scholl-Pl. 1 (E) - E 004
Mittwoch 12:00 c.t. - 14:00 Uhr Geschw.-Scholl-Pl. 1 (E) - E 004
Start Di., 26.04.2022

Termine - Übungen
Mittwoch 14:00 c.t. - 16:00 Uhr Geschw.-Scholl-Pl. 1 (M) - M 105
Donnerstag 10:00 c.t. - 12:00 Uhr Schellingstr. 3 (S) - S 007
Start Mi., 04.05.2022


Einschreibeschlüssel: expoFam-2022

[s22] Decision Theory (Augustin, Jansen)

Der Einschreibeschlüssel lautet DT2022

[s22] Mathematik II: Mathematische Techniken in Statistik und Data Science für Nebenfachstudierende (Jansen, Schollmeyer)

Der Einschreibeschlüssel lautet Mathematik II

[s22] Data and Privacy? – Theoretical and Empirical Challenges and Strategies

This seminar addresses the balance between the social benefits of data access and use for research, and the interests of individual privacy and data confidentiality. The challenge faced by social science and medical researchers, relative to data users in other contexts, is the need to compute accurate statistics from sensitive databases, share their results broadly, and facilitate scientific review and replication. In this seminar, we will take an interdisciplinary look at privacy and sensitivity, covering privacy attitudes and privacy law in Europe as well as strategies to ensure privacy and the ways statistical agencies have made sensitive data available: tabular data, public use files, and, more recently, synthetic data.

Time: Tuesdays, 04:15-05:45 pm.

Enrollment: Students who wish to attend will be manually enrolled in the seminar by the instructors.

Please contact Leah von der Heyde (leah.vonderheyde@stat.uni-muenchen.de) for questions.

[s22] Statistik IV für Nebenfachstudierende (Rodemann, Schierholz)

Termine:

Termin Ort Beginn
Vorlesung Mi, 14:15-15:45 A 021 27.04.22
Vorlesung Do, 12:15-13:45 (14-tägig)
 A 021 28.04.22
Übung
 Mo, 12:15-13:45 A 021
 02.05.22

Einschreibeschlüssel
  • Der Einschreibeschlüssel lautet: "stat4nf2022"

[s22] Statistical analysis of COVID-19 data (Küchenhoff, Kauermann, Berger)

Over the past two years, the CODAG (COVID-19 Data Analysis Group) at LMU has conducted several data analyses and regularly published them in reports (https://www.covid19.statistik.uni-muenchen.de/newsletter/index.html). In this seminar, selected analyses will be presented and discussed. Since many of them were performed under time pressure in a difficult environment and with incomplete information, it is of interest to look at those analyses again with a critical focus. Furthermore, numerous other data analyses have been disseminated via social media by various actors, which often come to dubious conclusions. Such examples will also be discussed critically in the seminar.

Target group: Bachelor and Master in Statistics, Master in Epidemiology
Seminar type: Block-type and in-person seminar
ECTS: 6 Bachelor, 9 Master, 9 Epidemiology
Language: The seminar will be held in English.
Advisors: Helmut Küchenhoff, Göran Kauermann, Ursula Berger, André Klima, Yeganeh Khazaei, Giacomo De Nicola, Maximilian Weigert

Key: CODAG2022


EMOS B: Official Statistics on Housholds, Enterprises, Economies and Populations

The courses EMOS A and EMOS B provide an overview of central concepts of official statistics from a methodological perspective. Topics discussed in EMOS B include national and international poverty measurement, dynamic indicators of economic statistics, basic concepts and methods of population statistics/demography, special domain statistics (household, cause of death, and business statistics), statistical literacy, linkage and matching of data sets. 

A combination of lecture, tutorial, and inverted classroom elements with discussion sessions will be offered. It is also expected that several representatives of official statistics will again enrich the event with guest lectures.

This course is a compulsory course for all students who want to obtain the EMOS (European Master in Official Statistics) supplementary certificate; all other master's students can flexibly have 6 ECTS credits recognized. Previous attendance of EMOS A (always offered in the winter semester) is not necessary.

Enrollment Key: emosb

[SS 22] Current Data Science Trends in the Social Sciences

Instructor: Frauke Kreuter

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

Key: SoDa2022


Applied Deep Learning with TensorFlow and Pytorch

[s22] Lineare Modelle (Küchenhoff)

Dieser Kurs ist für Bachelor-Student:innen der Statistik vorgesehen und wird auf Deutsch unterrichtet.

Wir bauen Grundlagen in den geläufigen Anwendungen der Regressionsanalysen auf und führen diese in R aus.

Bei weiteren Fragen kontaktieren Sie:

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

Wiegrebe, Simon <simon.wiegrebe@stat.uni-muenchen.de>

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

Stundenplan
Time Type of lesson
 Instructor
 Note
Mi 10:00-12:00
 Vorlesung Prof. Dr. Helmut Kuechenhoff
Pro Woche sollten beide Vorlesungen besucht werden.
Do
08:00-10:00
 Vorlesung Prof. Dr. Helmut KuechenhoffPro Woche sollten beide Vorlesungen besucht werden.
Di
10:00-12:00
 Übung WiegrebePro Woche sollte eine Übung besucht werden.
Do
14:00-16:00
 Übung
 Rave
 Pro Woche sollte eine Übung besucht werden.
Fr
14:00-16:00
 Tutorial Langer
 

Einschreibeschlüssel: LiMo_22




[ss22] Supervised Learning/Predictive Modelling (Bengs)

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, 12:15 - 13:45
  • Location: HGB - A 119

Enrolment Key

SLSS22

Target Audience 

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


[s22] Statistische Software (SAS) - SAS-Kurs (Oberhauser)

Person: Dr. Cornelia Oberhauser

SAS-Kurs als 5-tägiger Blockkurs in den Semesterferien

Termine:

Tag

   Uhrzeit
   Raum
Mo 22.08.2022
Vorlesung9:15 - ca. 12:15
online über Zoom

Übung
13:15 - 17:00
online über Zoom
Di 23.08.2022
Vorlesung
9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Do 25.08.2022
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Mo 29.08.2022
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom
Di 30.08.2022
Vorlesung9:15 - ca. 12:15online über Zoom
Übung13:15 - 17:00online über Zoom


Gastschlüssel

  • Der Gastschlüssel lautet: "saskurs2022"
  • Der Selbsteinschreibeschlüssel lautet: "saskurs2022"

[ss22] Statistical Modelling (Küchenhoff)

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>

Schedule
Time Type of lesson
 Instructor
 Note
Monday 14:00-16:00
 Lecture Prof. Dr. Helmut Kuechenhoff
 Both lectures should be attended (weekly rhythm)
Thursday 12:00-14:00
 Lecture Prof. Dr. Helmut Kuechenhoff 
Monday 10:00-12:00
 Workshop Will not be needed
Friday
14:00-16:00
 Workshop
 Rave
  One work shop per week should be attended (weekly rhythm)
Thursday 08:00-10:00
 Tutorial TBA
 All tutorials should be attended (weekly rhythm)

Enrolment key: Stat_Model_ss_2022



[s22] Introduction to Bayesian Statistics (Amini Farsani)

  • This lecture covers the basics of Bayesian statistics and its practical applications
  • The lecture is held in English. The first part of the lecture will be online, the second part in presence. 
  • 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). 

Einschreibeschlüssel

Thomas

Dates

  • Friday 12-16



[s22] Introduction to Machine Learning (Bothmann)

Schedule

  • Class: Monday, 10 am - 12 pm
  • Location: Geschw.-Scholl-Pl. 1 (D) - D 209

Enrollment key

  • The enrollment key is I2ML


[s22] Advanced Machine Learning (Casalicchio, Bengs)

Description: This course directly builds on the “Introduction to ML” and the “Supervised Learning” lecture. It introduces advanced machine learning concepts for some selected topics that were not covered in the two aforementioned lectures. The topics are organized into two parts:

  • The first part introduces several model-agnostic interpretation techniques that produce local (e.g., observation-wise) or global explanations for ML models fitted on tabular data. 
  • The second part focuses on further advanced ML topics such as imbalanced, multi-label, and cost-sensitive classification, uncertainty quantification, fairness in ML, and online learning.

Time: Monday, 12:15 - 13:45 and Thursday, 10:15 - 11:45

Prerequisites: Supervised Learning or Predictive Modeling (Fortgeschrittene Computerintensive Methoden) or a similar lecture (see here for a list of topics, most of which you should know as a prerequisite for this course).

Enrolment key: AML22

[s22] Deep Learning (Rezaei, Rügamer)

Schedule

  • Lecture: Tuesday, 10 - 12 c.t.
  • Exercise: Friday, 10 - 12 c.t.

    Covid19

    • Due to the pandemic situation, the course will very likely be held via Zoom.

    Enrollment key

    • The enrollment key is learnDL


    [s22] Fortgeschrittene Statistische Software für Nebenfach-Studierende (R)

    Der Einschreibeschlüssel für den Kurs ist FortStatSoftNF. Falls Sie Fragen haben, wenden Sie sich an anna-carolina.haensch@stat.uni-muenchen.de.

    Vorlesung Freitag1416
    ÜbungFreitag1618


    [s22] Statistik II für Studierende der Soziologie und Nebenfachstudierende

    Der Einschreibeschlüssel für den Kurs ist Stat2SozNF. Bei Fragen wenden Sie sich bitte an anna-carolina.haensch@stat.uni-muenchen.de


    VorlesungMontag1214
    Vorlesung
    		Donnerstag1214
    Parallelübung 1Dienstag1618
    Parallelübung  2Mittwoch1416

    [s22] Univariate Timeseries Analysis (Wohlrabe)

    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) and Zeitreihen (P 7.0.5, MA WiSo Statistik, PO 2010).

    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.

    [s22] ML und DL mit Python (Bothmann, Aßenmacher)

    Termine

    • Freitag, 09 - 12 s.t.


    [s22] Multimodal Deep Learning (Heumann, Aßenmacher, Schalk, Goschenhofer, Hvingelby)

    Important Dates:

    Termin Ort
    Preliminary meeting (online) 30.03.22, 10-12 c.t.
    Interim Presentations 09.05.22, 16-18 c.t.
    Presentations 20.07.22, 10-16 s.t.
    Presentations 21.07.22, 10-16 s.t.
    Submission deadline 31.08.22, 23.59 CET

    Enrollment key:
    Will be announced at the preliminary meeting.

    Note:
    This seminar is subject to the regular application process for seminars at the Department of Statistics.

    [s22] Seminar Unsupervised Deep Learning

    In this seminar, we will learn about the theory of deep unsupervised learning and will review some state-of-the-art methods. We will offer different topics with different applications (i.e. NLP, CV, bioinformatics) 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 and recent [live] challenges. 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.

    Enrollment key: seminar_udl

    First meeting: 29.04.2022


    [s22] Wahrscheinlichkeitstheoretische Grundlagen der Statistik (Schmid)

    Termine
    Termin Ort Person
    Vorlesung Mo 12–14 S 004 Volker Schmid
    Vorlesung Do 10–12 A 030 Volker Schmid
    Übung 1 Mo 14–16 S 005 Julian Rodemann/N.N.
    Übung 2 Mi 8–10 E 004 Julian Rodemann/N.N.
    Tutorium Di 16–18 M 105 Michael Kobl



    [s22] Statistik I & II für Studierende der Wirtschaftswissenschaften (Heumann)

    Termine

    • Vorlesung: Dienstag, 16 - 18 c.t., Prof. Dr. Christian Heumann
    • 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 "22wiwistat".