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.
Inscription Key: tba- معلم: Dennis Mao
Kurs | Zeit | Ort |
---|---|---|
Grundkurs | Mo, 21.02., 14.30 - 18.00 | online |
Grundkurs | Di, 22.02., 14.30 - 18.00 | online |
Grundkurs | Mi, 23.02., 14.30 - 18.00 | online |
Aufbaukurs | Do, 24.02., 14.30 - 18.00 | online |
Aufbaukurs | Fr, 25.02., 14.30 - 18.00 | online |
- معلم: Alexander Bauer
- معلم: Maximilian Weigert
Blockseminar: April 4-6, April 8 and April 11-14, 2022
Einschreibeschlüssel: CSDS2021
- معلم: Anna-Carolina Haensch
- معلم: Frauke Kreuter
- معلم: Joseph Sakshaug
Modern Machine Learning (ML) algorithms are considered to be black-boxes we have no epis-temic access to. XAI tackles this assumption by providing methods that allow gaining insights into thebehaviour of ML algorithms. This course introduces the central philosophical concepts and challenges inXAI such as explanation, interpretability, and opacity. Moreover, we discuss state-of-the-art XAI meth-ods and their strengths and weaknesses. We focus particularly on causal explanations, the role of XAI for Science, and model-agnostic interpretation techniques.
The course is held together with students from MCMP.
Fridays 12:15-13:45 pm
Opening Event: Online
Key: xai-philo-21
- معلم: Gunnar König
This class is geared towards first year students in the master's program Data Science and part of the Individual Module (Fundamentals of Data Science). The basic concepts of multivariate statistical methods for data scientists we will be introduced.
Content:
- Probability models
- Random variables and univariate probability distributions
- Joint distributions
- Visualization in R
- Principal component analysis (PCA)
- Multidimensional scaling
- Cluster analysis
Time and Location: Every Tuesday, 12:15 - 13:45 a.m. in Room AU117 in the main buildung of LMU
- معلم: Roman Hornung
Die empirischen Wissenschaften stehen vor wesentlichen Herausforderungen in Bezug auf die Reproduzierbarkeit und Replizierbarkeit ihrer Ergebnisse. Die sogenannte Replikationskrise beschreibt das Phänomen, dass eine Vielzahl an Studienergebnissen nicht auf unabhängigen Daten bestätigt werden können.
Dies liegt unter anderem an der Tatsache, dass in der Regel eine große Anzahl an verschiedenen Analysestrategien für eine bestimmte Forschungsfrage existiert (>>researcher degrees of freedom<<). Überdies werden die Ergebnisse oftmals nur selektiv für die gewählte Strategie veröffentlicht und die Variabilität in Bezug auf verschiedene andere Ansätze verschleiert (>>selective reporting<<).
Schwerpunkt des Seminars ist die Vermittlung von Grundlagen der Reproduzierbarkeit und Replikation in der Statistik. Die Studierenden werden dazu in die entsprechende Literatur und den Umgang mit geeigneten Tools, wie z.B. Git/Github (Versionskontrolle) und R Markdown (Reporting) eingeführt, um anschließend selbstständig an einer vorgegebenen Fragestellung (unter Einbezug der einschlägigen Literatur) zu arbeiten.
Vorbesprechung: Freitag, 22.10.2021 um 14:00-16:00 Uhr (s.t.) via Zoom
Einführungs-Kurs: Freitag 29.10.2021, 14:00-16:00 Uhr (s.t.) via Zoom
Ort: Das Seminar findet vsl. via Zoom statt.
Anrechnung: 6 ECTS oder 9 ECTS (6 ECTS (Seminar) + 3 ECTS (Wahlpflichtbereich))
- معلم: Maximilian Mandl
Inverted classroom style. Weekly meeting (in person): Wednesday, 14:00-16:00 c.t. Geschw.-Scholl-Pl. 1 (A) - A 119
Starting date: 20.10.2021
Enrollment Key: automl22
- معلم: Lennart Schneider
- معلم: Janek Thomas
Sie können sich in den Kurs mit dem Schlüssel DHS2122 selbst einschreiben.
- معلم: Florian Fleischmann
- معلم: Helmut Küchenhoff
- معلم: Iris Wallnöfer
Dates
Kick-off-meeting: October 20th, 2pm (online)Q&A Session: Wednesday, 2pm (online)
Exercise: Wednesday, 4pm (Theresienstr. 41 - C 419)
Enrollment key: kriging
- معلم: Robert Czudaj
- معلم: Julian Rodemann
Termine:
Termin | Ort | Person | |
---|---|---|---|
Vorlesung |
Mo, 12.00 - 14.00 |
M118 |
Fabian Scheipl |
Vorlesung |
Mi, 12.00 - 14.00 | M118 |
Fabian Scheipl |
Übung 1 |
Do, 10.00 - 12.00 |
F007 |
Michael Kobl |
Übung 2 |
Do, 12.00 - 14.00 | M105 |
Fabian Scheipl |
Tutorium |
Di, 16.00 - 18.00 |
M218 | Michael Kobl |
Übung & Tutorium beginnen erst in der zweiten Semesterwoche.
- معلم: Michael Kobl
- معلم: Fabian Scheipl
Beschreibung des Kurses
Formale
Techniken und Argumentationen besitzen auch in den Sozial-, Geistes-
und Wirtschaftswissenschaften eine große, und immer weiter wachsende,
Bedeutung, stellen aber für viele, darauf nicht so gut vorbereitete
Studierende eine sehr große Hürde dar. Im Rahmen eines
fachübergreifenden Propädeutikums sollen nicht-mathematikaffine
Studierende der LMU sanft in die Formalisierung eingeführt und mit
wesentlichen Techniken (wieder) vertraut gemacht werden.
Gerade
unter den Herausforderungen der Corona-Situation ist es uns ein
besonderes Anliegen, nicht-mathematikaffine Studienanfänger*innen in
einem semesterbegleitenden Kurs zu begleiten und zu unterstützen. Wir
möchten das Verständnis für formale Schreibweisen und Argumentationen
stärken und beginnen daher mit einer Heranführung an das Formalisieren
und einer Einführung in die Mengenlehre.
Die Veranstaltung richtet sich sowohl an Studienanfänger als auch an Studierende die im Rahmen Ihres Studiums bereits auf Schwierigkeiten im Umgang mit formalen Methoden gestoßen sind.
- معلم: Thomas Augustin
- معلم: Hannah Blocher
- معلم: Cornelia Fütterer
- معلم: Christoph Heindl
- معلم: Dominik Kreiß
- معلم: Malte Nalenz
- معلم: Yusuf Sale
- معلم: Georg Schollmeyer
Die Veranstaltung "Statistische Software (R)" wendet sich an Studierende im Bachelorstudiengang Statistik (1. Semester).
- معلم: Andreas Bender
- معلم: Philipp Kopper
- معلم: Julia Niebisch
Official statistics are the central information service provider in a democratically organized society. It informs politics, business and society about current economic, social and increasingly also ecological developments. In this way, it forms an important basis for informed decisions, but at the same time it is a supervisory body, especially for politics, by empirically reflecting the consequences of decisions and actions. In order to meet these demands, official statistics has a strict methodology and also methodology oriented to high quality standards, on the basis of which there has been an intensive examination of complex statistical modeling, machine learning techniques and the possibilities of new data sources in recent years. (This development has also led to close cooperation with a number of universities, resulting in an EU-wide certification of partial courses (EMOS: European Master in Official Statistics). At LMU, it is possible to obtain the EMOS certificate within the framework of the master's program in statistics with a focus on economics and social sciences).
The course "EMOS A" aims to prepare students for participation in this discourse and the numerous opportunities for cooperation with public data producers by providing an insight into typical questions and modes of argumentation, methodological and methodological principles, and the most important products of official statistics. First of all, 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 (online and in person) and guest lectures.
The course is compulsory within the EMOS variant; all other students can acquire 6 ECTS credits, which they can have credited flexibly.
Enrollment Key: emosa
- معلم: Dominik Kreiß
- معلم: Anian Rottmüller
Type | Date | Time |
---|---|---|
Lecture | starting on the 19th of October 2021 | Tuesday 9:15 - 11:30 am |
Exercise& Tutorial | starting on the 2nd of November 2021 | Monday 14:15 - 15:45 am |
Enrollment key:
- The enrollment key is preclin_clin_2122
- معلم: Anne-Laure Boulesteix
- معلم: Sabine Hoffmann
- معلم: Daniele Pugno
Schedule:
- Lecture: Monday, 16-18 c.t., Geschw.-Scholl-Pl. 1 - A 022
- Lecture/Exercise: Thursday, 10-12 c.t., Geschw.-Scholl-Pl. 1 - A 022
- Enrollment Key: life21Time
Lecture and exercises will be in English.
COVID-19
Currently we plan Videos + bi-weekly live, in-person sessions for the lecture and bi-weekly live, in-person session for the exercises. Details will be announced on the Moodle page and during the first meeting on Monday, October 18, 16:15, room A 022 - Geschw.-Scholl-Pl. 1.
- معلم: Andreas Bender
- معلم: Benjamin Sischka
- معلم: Simon Wiegrebe
- معلم: Anna-Carolina Haensch
- معلم: Frauke Kreuter
- معلم: Frauke Kreuter
Termine:
Termin | Ort | Person | Beginn | |
---|---|---|---|---|
Vorlesung | Mo, 10:15-11:45 Do, 10:15-11:45 (14-tägig) |
[Sowohl virtuell als auch in Präsenz] Geschw.-Sch.-Pl. 1 - B 106 Theresienstr. 39 - B 134 |
Benjamin Sischka |
18.10.2021 |
Übung |
Mo/Do, 10:15-11:45 (14-tägig) | Virtuell via Zoom |
Malte Nalenz |
28.10.2021 |
Einschreibeschlüssel
- Der Einschreibeschlüssel lautet: Stichproben2122
- معلم: Cornelius Fritz
- معلم: Malte Nalenz
- معلم: Benjamin Sischka
Schedule
- Lecture: Wednesday, 10 - 12
- Lab session: Thursday, 12 - 14
- Location: t.b.d.
- The enrollment key is OPTIM2021
- معلم: Bernd Bischl
- معلم: Julia Moosbauer
- معلم: Tobias Pielok
- معلم: Katharina Röck
Schedule:
Date | Place | Lecturer | Begin | |
---|---|---|---|---|
Lecture | Tuesday, 16:00 - 18:00 | … | Prof. Dr. Annika Hoyer | 19.10.2021 |
Exercise & Tutorial | Thursday, 16:00 - 18:00 | … | Dina Voeltz | 26.10.2021 |
Enrollment key
- معلم: Annika Hoyer
- معلم: Dina Voeltz
Termine und Personen:
Termin | Ort | Person | |
---|---|---|---|
Vorlesung |
Di, 14.00 - 16.00 |
Geschw.-Scholl-Pl. 1 - M018 |
Küchenhoff |
Übung |
Mo, 14.00 - 16.00 |
Geschw.-Scholl-Pl. 1 - E004 |
Weigert |
Übung |
Mo, 16.00 - 18.00 |
Geschw.-Scholl-Pl. 1 - A016 |
Schwaferts |
Tutorium |
Mo, 12.00 - 14.00 |
Ludwigstr. 33 - CIP-Pool 042 |
Musiol |
Tutorium |
Do, 14.00 - 16.00 |
online via Zoom |
Musiol |
- معلم: Helmut Küchenhoff
- معلم: Sarah Musiol
- معلم: Fabian Scheipl
- معلم: Patrick Schwaferts
- معلم: Maximilian Weigert
Schedule
- Lecture: Tuesday, 10 - 12 c.t.
- Exercise: Friday, 10 - 12 c.t.
Covid19
- Due to the pandemic situation, the course will be held via Zoom.
Enrollment key
- The enrollment key is learnDL
- معلم: Martin Binder
- معلم: Emilio Dorigatti
- معلم: Hüseyin Gündüz
- معلم: Chris Kolb
- معلم: Mina Rezaei
- معلم: David Rügamer
- معلم: Emanuel Sommer
- معلم: Tobias Weber
Description
The seminar deals with theoretical and practical concepts of uncertainty quantification in deep learning.
Organization and Dates
Kick-off event: Friday, 15. October, 3 - 5 pm
Due to Corona, all talks will be online!
Enrolment Key
UQDL22
Target Audience
- Statistics MSc. (Methods/Bio/WISO)
- Statistics and Data Science MSc.
- Data Science MSc.
- معلم: Emilio Dorigatti
- معلم: Jann Goschenhofer
- معلم: Florian Karl
- معلم: Chris Kolb
- معلم: Felix Ott
- معلم: Tobias Pielok
- معلم: Raphael Rehms
- معلم: David Rügamer
- معلم: Tobias Weber
- معلم: Lisa Wimmer
Der Einschreibeschlüssel lautet: MathGrund2122
- معلم: Christoph Jansen
- معلم: Georg Schollmeyer
Vorlesung und Übung: Frauke Kreuter, Julian Rodemann
Einschreibeschlüssel: WiSo202122
- معلم: Thomas Augustin
- معلم: Jacob Beck
- معلم: Anna-Carolina Haensch
- معلم: Felix Henninger
- معلم: Frauke Kreuter
- معلم: Frauke Kreuter
- معلم: Julian Rodemann
- معلم: Leah von der Heyde
Vorlesung Anna-Carolina Haensch (anna-carolina.haensch@stat.uni-muenchen.de)
Übung Jacob Beck, Felix Henninger
Tutor*innen Ilija Spasojevic, Alexandra Holzmann
Einschreibeschlüssel: Stat1Soz2021- معلم: Jacob Beck
- معلم: Luyang Chu
- معلم: Anna-Carolina Haensch
- معلم: Felix Henninger
- معلم: Alexandra Holzmann
- معلم: Ilija Spasojevic
Der Einschreibeschlüssel lautet: stat3nf2022
- معلم: Anastasiia Holovchak
- معلم: Annika Hoyer
- معلم: Sevag Kevork
- معلم: Ruben Camilo Wißkott
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: TODO
- Location: TODO
Enrolment Key
TODO
Target Audience
- Statistics (Methods/Bio/WISO)
- Data Science MSc.
- معلم: Ludwig Bothmann
- معلم: Julia Herbinger
- معلم: Yawei Li
- معلم: Christian Scholbeck
- معلم: Lisa Wimmer
Use the registration key (Einschreibeschlüssel) "bigDS" 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: Online in Zoom
- 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
- معلم: Giuseppe Casalicchio
- معلم: Susanne Dandl
- معلم: Florian Pfisterer
Einschreibeschlüssel Selbsteinschreibung/Gast: PRAKT2022
- معلم: Andreas Bender
- معلم: André Klima
- معلم: Helmut Küchenhoff
- معلم: Dina Voeltz
Einschreibeschlüssel Selbsteinschreibung / Gast: APR2022
- معلم: Sabine Hoffmann
- معلم: André Klima
Schedule
Lecture : Thursday 12:15 - 13:45 pm (Start: 21st of October)Enrollment key
The enrollment key is open_rep_sci_w2122
- معلم: Anne-Laure Boulesteix
- معلم: Sabine Hoffmann
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>
Time | Type of lesson |
Instructor | Note |
---|---|---|---|
Friday 12:00-14:00 |
Lecture | Prof. Dr. Helmut Kuechenhoff | Both lectures should be attended (weekly rhythm) |
Monday 08:00-10:00 |
Lecture | Prof. Dr. Helmut Kuechenhoff | |
Friday 14:00-16:00 |
Workshop | Wiegrebe | One work shop per week should be attended (weekly rhythm) |
Thursday 12:00-14:00 |
Workshop | Rave | |
Thursday 08:00-10:00 |
Tutorial | Hannah Kuempel | All tutorials should be attended (weekly rhythm) |
Enrolment key: Stat_Model_2022
- معلم: Helmut Küchenhoff
- معلم: Hannah Kümpel
- معلم: Martje Rave
- معلم: Dielle Syliqi
- معلم: Nora Valiente Bauer
- معلم: Simon Wiegrebe
Termine
- Dienstag: 12:15 - 13:45 Uhr
- Mittwoch: 12:15 - 13:45 Uhr
Einschreibeschlüssel
- Der Einschreibeschlüssel lautet "einf_biom_w2122".
- معلم: Sabine Hoffmann
- معلم: Raphael Rehms
- معلم: Theresa Ullmann
Lecture (Robert Czudaj)
Tuesday, 4 p.m. - 6 p.m., (Start: October 19, 2021)
Tutorial (Christoph Berninger)
Thursday, 6 p.m. - 8 p.m., (Start: October 28, 2021)
Einschreibeschlüssel:- Der Einschreibeschlüssel lautet: FinEco_2021_22
- معلم: Christoph Berninger
- معلم: Robert Czudaj
- معلم: Robert Czudaj
- معلم: Dennis Mao
- معلم: Philipp Schiele
Termine
- Vorlesung: Dienstag, 12:00 - 14:00 Uhr,
- Übungen: Montag, 14:00 - 16:00 Uhr
Einschreibeschlüssel
- Der Einschreibeschlüssel lautet "einf_oek_w2122".
- معلم: Robert Czudaj
- معلم: Philipp Schiele
the enrollment key is Fréchet
- معلم: Hannah Blocher
- معلم: Yusuf Sale
- معلم: Georg Schollmeyer
Schedule
Time | Lecturer | Beginn | ||
---|---|---|---|---|
Lecture | Monday, 12:15 - 13:45 | Prof. Dr. Heumann | 18.10.21 | |
Lecture | Tuesday 12:15-13:45 | Prof. Dr. Heumann | 19.10.21 | |
Excercise Course 1 | Tuesday, 10:15-11:45 | Spargali, Rave, Blocher | ||
Excercise Course 2 | Wednesday, 08:15-09:45 | Spargali, Rave, Blocher | ||
Tutorium | Friday, 08:15 - 09:45 | |
Enrollment Key
- The enrollment key is "stat_inf_ws2122"
- معلم: Hannah Blocher
- معلم: Christian Heumann
- معلم: Eleftheria Papavasiliou
- معلم: Martje Rave
- معلم: Nurzhan Sapargali
Schedule
- Lecture: Wednesday, 9 - 11 c.t.
- Tutorial: Wednesday, 11 - 12 c.t.
- Location: online (videos + zoom-meetings)
Enrollment key
- The enrollment key is "sesame_street".
- معلم: Matthias Aßenmacher
- معلم: Goran Glavas
- معلم: Christian Heumann
- معلم: Leonie Weißweiler
This bachelor/master-seminar deals with selected methods and topics that are less common in the classical statistics curriculum .
Besides rather classical topics like non-parametric statistics, some non-classical topics aim at applications of order and lattice theory, others are rather motivated by the philosophy of science in the context of statistics (including machine learning contexts), further topics deepen aspects of decision theory. For the latter, it is advantageous to have heard the corresponding lecture on decision theory, but this is not a must.
Since the seminar is offered for advanced bachelor students as well as master students and since the mathematical background of the students is probably relatively heterogeneous, this will be taken into account concerning the requirements and also in the supervision. In general, the exact design of the seminar topics (whether more in depth or more in breadth, etc.) can of course be individually shaped by the students in consultation with the lecturers.
The topics are partly related to each other and partly rather independent of each other.
The seminar will take place in blocks at the end of the semester (around the first week of March), the exact scheduling will take place in the preliminary meeting.
The preliminary meeting, during which the topic will be assigned, is tentatively scheduled for Monday, Nov. 15, beginning at 18:00 s.t. via zoom.
There will be an interim meeting (via zoom) around mid-January.
The seminar is planned as a virtual event via zoom.
- معلم: Christoph Jansen
- معلم: Georg Schollmeyer
Schedule
- Class: Friday, 10:15 - 11:45 a.m.
- Location: Schellingstr. 3 (S) - S 004
Enrollment key
- The enrollment key is I2ML
- معلم: Ludwig Bothmann
- معلم: Tobias Weber
- معلم: Lisa Wimmer
Audience:
- Master Statistik: Räumliche Statistik
- Master Biostatistik: Räumliche Statistik
- Master Statistik WiSo: Räumliche Statistik
- Master Statistics and Data Science: Spatial Statistics
- Master Data Science ESG: Elective
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 "Moran".
- معلم: Christopher Küster
- معلم: Volker Schmid
- Dienstag 10–12 (Vorlesung)
- Freitag 8.30–10 (Zusatztermin)
- Mittwoch 8.30–10 (Übung)
- Donnerstag 14–16 (Übung)
- Dienstag 18–20 (Tutorium)
Einschreibung
Einschreibeschlüssel: Tschebyschow
- معلم: Michael Kobl
- معلم: Dennis Mao
- معلم: Julian Rodemann
- معلم: Marie Scherzer
- معلم: Volker Schmid
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 "2122wiwistat".
- معلم: Matthias Aßenmacher
- معلم: Lukas Beise
- معلم: Alexander Fogus
- معلم: Anne Gritto
- معلم: Christian Heumann
- معلم: Katrin Racic-Rachinsky
- معلم: Nora Valiente Bauer