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Informatik
[041243] Machine Learning Paradigms for Complex Data — Vorlesung
Erich Schubert, Emmanuel Müller, Daniel Gunter Wilmes

Score: 12.69 The lecture provides insights into advanced machine learning methods that form the basis for an emerging research area. Traditional methods have been considered for several years in the literature and are covered by basic machine learning lectures. However, due to the large and complex data used in today’s applications, some of these traditional methods are applicable only on relatively small and simple problem instances. Recently, research has addressed this challenge by proposing novel machine learning paradigms for large and high dimensional data. They aim at scalability with respect to size and dimensionality of data in todays and future applications. The lecture will derive novel challenges for machine learning out of recent application scenarios. The focus will be on advanced machine on the data. Outlier mining techniques in high dimensional data. Challenges and recent solutions in research and industrial projects. Overview on our future work in this research area. At the end of the lecture, the participants should be aware of – and able to explain – the necessity of advanced machine learning concepts. They should be able to assess and compare different approaches of data analysis Machine Learning Paradigms for Complex Data learning paradigms for knowledge discovery in high dimensional data. We will highlight the characteristic properties of different paradigms and discuss algorithmic solutions in each of these paradigms. Furthermore, novel evaluation techniques will be presented that enable evaluation of these methods in real world applications. Overview of the content: Motivation of novel challenges based on today’s

Statistik
[050627] Generalized Linear Models — Vorlesung
Andreas Groll

Score: 8.13 The lecture will be given in an inverted classroom-style. Every week, 2 lecture videos will be uploaded in Moodle and each Thursday, from 10-11 am, there will be a Q&A session on those videos in lecture room CT/ZE 15 (BCI-Building in front of HG II). Start: Thursday, 07.04.2022, exceptionally from 11-12 am !!!

Physik
[020129] Quantum theory of semiconductors — Vorlesung
Doris Reiter

Score: 7.53 Semiconductors play a vital role in modern devices used in computers, smartphones and quantum technologies. Using a microscopic description of semiconductors, the lecture will introduce the basic concepts of semiconductor theory. The lecture covers several topics from semiconductor physics including semiconductor band structures, heterostructures, excitons, light-matter interaction, transport theory as well as two dimensional materials like graphene or transition metal dichalcogenides. This gives a solid background to understand modern research papers. Literature: A script will be provided during the lecture.

Lehrstuhl Informatik XII, Informatik
[042503] Computer Vision — Vorlesung
Univ.-Prof. Dr. Gernot Fink

Score: 7.27 Topical focus areas (Schwerpunktgebiete): 2 (..., Embedded Systems, ...), 7 (Intelligent Systems) Specialization Module (Vertiefungsmodul INF-MA-502) for Master (Applied) Computer Science For up-to-date information on the lecture please refer to the accompanying web page. NOTE: There is no Moodle room associated with this lecture!

Physik
[020128] Ask me anything: Quantum Dots — Seminar
Doris Reiter

Score: 7.27 To cover these topics, the students are given material (either lecture notes, fundamental papers or recent research articles) covering one session. Each session will be hosted by a student, who is responsible for asking questions to the lecturer, such that the full content of the session is covered. The chair shall also involve other students to participate in the discussions.

Statistik, Maschinenbau, Informatik
[042539 074050] Industrial Data Science 2 — keine Angabe
Univ.-Prof. Dr.-Ing. Jochen Deuse, Jens Teubner, Erich Schubert, Lukas Schulte, Maximilian Berens, Nikolai West, Roman Bernhard Möhle

Score: 7.05 Industrial Data Science 2 Master Data Science Application

Statistik
[05] Data Literacy - Rinvorlesung — Vorlesung
Henrike Weinert

Score: 6.96 Data Literacy - Rinvorlesung Für mehr Infos zur Ringvorlesung und zur Anmeldung (via Moodle-Raum) siehe: http://www.dodsc.tu-dortmund.de/cms/de/home/Lehre/Data-Literacy-Lehrveranstaltungen/index.html

Statistik
[052002] R für Data Scientists — Vorlesung
Dr. Daniel Horn

Score: 6.96 R für Data Scientists

Statistik
[05] Advanced multivariate data analysis using R — Vorlesung mit Übung

Score: 6.87 Advanced multivariate data analysis using R

Maschinenbau
[073611] Applied Supply Chain Analytics - From Data to Decisions — Übung
Anne Meyer

Score: 6.79 Applied Supply Chain Analytics - From Data to Decisions

Maschinenbau
[073610] Applied Supply Chain Analytics - From Data to Decisions — Vorlesung
Anne Meyer

Score: 6.79 Applied Supply Chain Analytics - From Data to Decisions

Lehrstuhl für Datenverarbeitungssysteme
[080385] Scheduling Problems and Solutions — Vorlesung
Dr.-Ing. Uwe Schwiegelshohn, Sabine Winterhoff, M.Sc. Ganesh Nileshwar

Score: 6.76 Scheduling is generally understood as the mapping of jobs or tasks to machines or resources. In practice, these problems occur in various forms in the areas of industrial production, logistics, process management as well as in the generation of service schedules and timetables. In the context of this lecture we will first consider problems for the one machine model and analyze them by theoretical methods. Afterwards, we will use these results to address more complex problems with several machines. The third part of the lecture concerns different variants of production line problems. In the lecture we discuss deterministic and online algorithms. We distinguish so called easy and hard problems. The latter will although be addressed with heuristic approaches yielding performance guarantees

Statistik
[05] Modelling ordinal data: Standard frameworks and recent developments based on mixture models — Arbeitsgemeinschaft

Score: 6.70 Modelling ordinal data: Standard frameworks and recent developments based on mixture models

Statistik
[051073] Data Mining Cup — Blockkurs
Jonas Rieger, Emmanuel Müller, M.Sc. Steffen Maletz

Score: 6.62 Data Mining Cup

Lehrstuhl Informatik VIII
[040277] Fachprojekt "Data Mining und Datenanalyse - Nachrichtenartikel-Kategorisierung" — Fachprojekt
Gloria Feher

Score: 6.62 Fachprojekt "Data Mining und Datenanalyse - Nachrichtenartikel-Kategorisierung"

Institut für katholische Theologie
[147128] Vorlesung Systematische Theologie: Soteriologie - Lehre von der Erlösung / Lecture on Systematic Theology: Doctrine of Redemption — Vorlesung
Gregor Taxacher

Score: 6.57 Vorlesung Systematische Theologie: Soteriologie - Lehre von der Erlösung / Lecture on Systematic Theology: Doctrine of Redemption

Mathematik
[010562C] Programming Course: Data Science with Python — Kurs
Susanne Drees, Fatma Abd-Elateef Mohammed Ibrahim

Score: 6.54 Programming Course: Data Science with Python

Informatik
[041244] Übung zu Machine Learning Paradigms for Complex Data — Übung
Simon Klüttermann, Daniel Gunter Wilmes

Score: 6.54 Übung zu Machine Learning Paradigms for Complex Data

Lehrstuhl Informatik XIII, Informatik
[040338] Übung zu Data Privacy — Übung
Jun.-Prof. Dr. Thomas Liebig

Score: 6.54 Übung zu Data Privacy

Statistik
[052001] Einführung in die Data Science II — Vorlesung
Claus Weihs, Katja Ickstadt, Jens Teubner, Andreas Groll

Score: 6.47 Einführung in die Data Science II

Dekanat Informatik, Informatik
[040276] Fachprojekt "Big Data Analytics Lab" — Fachprojekt
Emmanuel Müller, Andreas Lang, Benedikt Böing

Score: 6.47 Fachprojekt "Big Data Analytics Lab"

Institut für katholische Theologie
[147129] Didaktisches Seminar zur Vorlesung Soteriologie / Didactic Seminar on the Lecture Soteriologie — Seminar
Gregor Taxacher

Score: 6.36 Didaktisches Seminar zur Vorlesung Soteriologie / Didactic Seminar on the Lecture Soteriologie

Lehrstuhl Informatik VIII, Lehrstuhl Informatik VI, Informatik
[040334] Übung zu Datenbanken in der Praxis - Data Warehousing — Übung
Jens Teubner

Score: 6.32 Übung zu Datenbanken in der Praxis - Data Warehousing

Lehrstuhl Informatik IX, Informatik
[046648] Green University - Anomaly Detection on Energy Data — Projektgruppe
Emmanuel Müller, Bin Li, Jelle Hüntelmann

Score: 6.32 Green University - Anomaly Detection on Energy Data

Mathematik
[010029] Höhere Mathematik II (Phy, ETIT, AngInf, Data Science) — Übung
Janine Textor, Karl Friedrich Siburg, Christopher Strothmann

Score: 6.24 Höhere Mathematik II (Phy, ETIT, AngInf, Data Science)

Statistik
[059877] Data Structures, Algorithms, and Applications in Python — Vorlesung mit Übung
Fatma Abd-Elateef Mohammed Ibrahim

Score: 6.24 Data Structures, Algorithms, and Applications in Python

Statistik
[05xxxx] Programming Course with R — Blockkurs
Susanne Frick

Score: 6.24 The course is elective as a "Programming Course" for the MD 3 module (Data Science in Practice).

Lehrstuhl für Bioprozesstechnik (BPT)
[065504] fällt aus : BPT - Data Science in Bioengineering (digital) — Vorlesung (digital)
Kristine Hemmer, Stephan Lütz, Georg Hubmann

Score: 6.17 fällt aus : BPT - Data Science in Bioengineering (digital)

Statistik
[05] Data Literacy - Elementares Datenverständnis — blendend learning-Veranstaltung
Henrike Weinert

Score: 6.04 Data Literacy - Elementares Datenverständnis

Physik
[020505] Practical aspects of instrumentation — Vorlesung mit Übung
Dozenten der Elementarteilchenphysik, Kevin Alexander Kröninger

Score: 5.99 The lecture will be given by Prof. Dr. Andreas Jung from Purdue University who will hold the Ulrich Bonse visiting chair for instrumentation during the summer term 2022.

Lehrstuhl für Systemdynamik und Prozessführung, Bio- und Chemieingenieurwesen
[061613] DYN - Data-based Dynamic Modeling (Summer Term) — Übung
Univ.-Prof. Dr.-Ing. Sebastian Engell

Score: 5.97 DYN - Data-based Dynamic Modeling (Summer Term)

Raumplanung, Fachgebiet Europäische Planungskulturen
[0910302] F 02 - Who owns the city? Digital Planning and Data Sovereignty — Projekt
Dipl.-Ing. Katrin Gliemann, Karsten Zimmermann, M.Sc. Lena Unger

Score: 5.84 F 02 - Who owns the city? Digital Planning and Data Sovereignty

Statistik
[051085] Foundations of Data Science — Seminar
Alexander Munteanu

Score: 5.84 Foundations of Data Science

Statistik
[053000] Advanced Statistical Learning — Vorlesung
Andreas Groll

Score: 5.72 and limitations of established methods are shown, in particular, with regard to large data sets (big data). Furthermore, the fundamental differences between supervised and non-supervised learning are evolved.

Informatik, Lehrstuhl Informatik II
[041407] Foundations of Data Science — Seminar
Alexander Munteanu

Score: 5.66 Foundations of Data Science

Lehrstuhl Informatik VI, Informatik
[040333] Datenbanken in der Praxis - Data Warehousing — Vorlesung
Jens Teubner

Score: 5.38 Datenbanken in der Praxis - Data Warehousing

Lehrstuhl Informatik XIII, Informatik
[040337] Data Privacy — Vorlesung
Jun.-Prof. Dr. Thomas Liebig

Score: 5.38 Data Privacy

Bio- und Chemieingenieurwesen, Lehrstuhl für Systemdynamik und Prozessführung
[061612] DYN - Data-based Dynamic Modeling (Summer Term) — Vorlesung
Univ.-Prof. Dr.-Ing. Sebastian Engell, Vanessa Wojcik

Score: 5.28 DYN - Data-based Dynamic Modeling (Summer Term)

Physik
[020242] Schwerpunktspraktikum ´Teilchenphysik und Detektoren´ / Advanced Laboratory course: Particle physics — Praktikum
Dr. Wolfgang Rhode, Johannes Albrecht, Kevin Alexander Kröninger, Dirk Wiedner

Score: 4.82 Content: Experimental techniques in particle physics including detector physics (semiconductor and scintillating fiber detectors), data analysis (CP violation and top-quark physics, reconstruction of particles) and advanced statistical methods (machine learning). Learning outcome: The students will obtain basic knowledge about particle and medical physics detectors, the technology used and the further processing of such data. They will understand how complex detector systems work and will apply their knowledge to laboratory experiments. The students will understand the relationship between the primary interactions of the particles to be detected with the entire material traversed and the different design methodologies. This leads to an understanding of the respective advantages

Lehrstuhl Mathematische Statistik und industrielle Anwendungen, Statistik
[059876] Introduction to Causal Inference — Vorlesung mit Übung
Markus Pauly, M.Sc. Philip Buczak, Menggang Yu

Score: 4.74 Please see https://www.statistik.tu-dortmund.de/3099.html for more information. Note: The course will run from June, 17th to July, 29th. As an exception, the first lecture will take place during the tutorial slot on June, 17th from 14:15 - 15:45 in CDI 120.

Mathematik
[010028] Höhere Mathematik II (Phy, ETIT, AngInf, Data Science) — Vorlesung
Janine Textor, Karl Friedrich Siburg, Christopher Strothmann

Score: 4.73 Höhere Mathematik II (Phy, ETIT, AngInf, Data Science)

Statistik
[050691] Time Series Analysis — Vorlesung
JProf. Antonia Arsova

Score: 4.50 To request a registration key for the moodle classroom "Time Series Analysis Summer Term 2022" please send an email to Mr. Pappert at pappert@statistik.tu-dortmund.de with subject "Registration key Time Series" stating your study program (MSc Statistik, MSc Econometrics, MSc Data Science, etc.) and your student-ID number. Thank you.

Raumplanung, Fachgebiet International Planning Studies
[092012a] Lecture Series International Planning Sessions — Seminar
Ing. Sophie Schramm

Score: 4.47 Lecture Series International Planning Sessions

Lehrstuhl Informatik II, Informatik, Lehrstuhl Informatik I
[040145] Theoretische Informatik für Studierende der Angewandten Informatik — Vorlesung
N. N

Score: 4.11 Anmeldung im Sommersemester 2022 Bitte melden Sie sich zur Vorlesung "Grundbegriffe der Theoretischen Informatik" an. https://www.lsf.tu-dortmund.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&veranstaltung.veranstid=266609&purge=y&topitem=lectures⊂item=editlecture&asi=t0I2cUuJt6uNrBAJJwGo

Physik
[020111] Statistical Methods of Data Analysis A — Vorlesung
Dr. Wolfgang Rhode

Score: 4.09 Statistical Methods of Data Analysis A : Numerische Methoden der Datenverarbeitung, Datenbehandlung und Programmierung, Algorithmen und Datenstrukturen, Methoden der linearen Algebra, Wahrscheinlichkeitsrechnung, ein- und mehrdimensionale Verteilungen, Zufallszahlen und Monte Carlo Methoden, Data-Mining Methoden: Diskriminanzanalyse, Hauptkomponenten-analyse, Feature Selection, Überwachtes Lernen (kNN, Decision Trees, Random Forests), MRMR

Bereich Fremdsprachen
[210506-S] Deutsch A1 Data Science (C) — Übung (digital)
Miri Shalini Stoll

Score: 3.66 Deutsch A1 Data Science (C)

Bereich Fremdsprachen
[210505-S] Deutsch A1 Data Science (B) — Übung (digital)
Lilian Hachenberg

Score: 3.66 Deutsch A1 Data Science (B)

Physik
[020636] Introduction to particle accelerator physics — Seminar
Adolfo Vélez Saiz

Score: 3.62 I will try as much as possible to accomodate the lectures not to coincide with your other duties. How: The 1st week will be devoted to go through the material you will be provided with. You can download the lectures (ppt) from the moodle and we will dedicate the time to go deeper into the introduced concepts. In 2nd week you will be given the choice to select a topic on which you will have to do

Informatik, Lehrstuhl Informatik VIII
[040269] Fachprojekt "Datenanalyse und Daten-Mining - Verteiltes Lernen" — Fachprojekt
Danny Heinrich

Score: 3.56 Daten, die von einem Verbund von Teleskopen aufgezeichnet werden. Ablauf: Im Fachprojekt erproben die Studierenden, in einer Gruppe ein größeres Software-Projekt umzusetzen. Dabei lernen Sie verschiedene hoch-aktuelle Software-Pakete kennen, unter anderem Python, scikit-learn, pyTorch, Git, etc. Weiterhin werden Grundlagen von Data Mining, künstlicher Intelligenz sowie maschinellen Lernen vermittelt . Die Studierenden arbeiten selbstständig in Kleingruppen an einem Problem, das mithilfe von maschinellem Lernen gelöst werden soll. Dabei findet zunächst eine Seminarphase statt, um den Studierenden einen Überblick über aktuelle Methoden des maschinellen Lernens und Data Mining zu geben. Anschließend wird in Gruppen an Aspekten des Problems praktisch, unter Verwendung aktueller Techniken und Tools

Bereich Fremdsprachen
[210504-S] Deutsch A1 Data Science (A) — Übung (digital)
Judith Schönhoff, Miri Shalini Stoll

Score: 3.56 Deutsch A1 Data Science (A)

Arbeitsgebiet Datentechnik
[000000] Versuch 207 (Digitaler Signalprozessor/Digital Signal Processor) — Labor
Carolin-Maria Linker

Score: 2.99 Stunden ablaufen. Für diese Session werden verschiedene Termine angeboten. Die Termine werden auf deutsch und englisch angeboten; die Wahl der Sprache ist frei. English - The practical training lab "Digital Signal Processor" is a mandatory part of the lectures "Signal Processing for Robotics and Automation (SPRA)" and "Theoretische Grundlagen der Informationstechnik (TGIT)". The material provided

Statistik
[051096] Visualisierung von Sportdaten — Seminar
Katja Ickstadt, Philipp Doebler, Andreas Groll, Dipl.-Journ. Christina Elmer

Score: 2.99 registration in the LSF is not necessary. All interested students will be informed by the lecturers whether they have received a place or not.

Institut für Journalistik
[157540i] Aktuelle Forschungsfelder: Sport und Statistik - Mit Daten Geschichten erzählen — Blockseminar
Dipl.-Journ. Christina Elmer

Score: 2.88 relevanter. In diesem Seminar sollen Studierende aus der Journalistik und Statistik/Data Science in interdisziplinären Teams Wissenswertes zu verschiedenen Sportarten recherchieren, auswerten und präsentieren. Ein Fokus liegt dabei auf der Überprüfung von weit verbreiteten Sport-Mythen. Journalistik-Studierende vertiefen nicht nur ihre Kenntnisse im Sportjournalismus, sondern lernen gemeinsam mit Statistik

Lehrstuhl Bodenpolitik, Bodenmanagement und kommunales Vermessungswesen, Raumplanung
[0920384] Land Policies in Europe — Seminar
Dipl.-Ing. Thomas Hartmann, M.Sc. Peter Rico Davids

Score: 1.93 will present each other the results of their study. • Second, students learn how to systematically analyse land policies and its instruments. Students will learn how policy instruments impact scarcity of land, based on effectiveness, efficiency, legitimacy, and justice. This teaching goals will be taught with a series of input lectures and with the book “Planning, Law and Economics: the rules we make

Institut für Musik und Musikwissenschaft
[160173] Musikinformatik — Blockseminar
Mark Gotham

Score: 1.32 können. Dieser Kurs erwartet ein gewisses Wissen in Musiktheorie und -analyse (z.B., Akkorde, Tonarten, Partituren, Tempo). Dies bedeutet, dass die musikalischen Ideen als vertrauter Maßstab für die Erforschung der rechnerischen Seite dienen können. Vorherige Erfahrung mit Programmierung, Data Science und fortgeschrittener Mathematik ist willkommen, aber nicht erforderlich. Wir werden lesen und

Herzlich Willkommen

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