WS 22/23

Enrolment token: csds22/23

More information soon.

Schedule:

Kick-off meeting

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

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

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

• Der Einschreibeschlüssel lautet: "fmm"

• Der Einschreibeschlüssel lautet: "stat3nf2022"

Termine und Personen:

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

Einschreibeschlüssel: dskrpt

Termine:

Termine:

• Der Einschreibeschlüssel lautet: "EinfBiom2223"

• The enrolment key is: "StatMetEpi2223"

Schedule 

• Q&A + exercise session: Friday, 10 - 12 

• The enrollment key is OPTIM2022

Der Einschreibeschlüssel lautet CoVorMa22

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.

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.

Einschreibeschlüssel: InfStat12022!

Schedule

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

Enrollment key

• The enrollment key is I2ML

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

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

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 

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

• The enrollment key is "stat_inf_ws2223"
  

Schedule

• Lecture: Wednesday, 9 - 11 c.t.
• Tutorial: Wednesday, 11 - 12 c.t.
• Location: Oettingenstr. 67 B U101

Enrollment key

• Enrollment key: sesame_street

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".