course-schedule

An Adventure in Statistics and Gender Differences in Mental Labor (FSL) (Seminar)

Termin:
wöchentlich | Montag | 16:15 - 17:45 | 14.10.2024 - 31.01.2025 | C 9.102 Seminarraum

Inhalt: "Where would the hipsters of psychology, medicine, business studies and biology be without statistics? They’d be setting fire to their own pants at a party: happier, perhaps, but directionless and in danger of getting burned. So don’t run away or fear statistics. Strike up a conversation with it, be patient and kind and see what happens.” (Field, 2016) Working with data, is one of the core skills of scientists (and future scientists). In the seminar, students will (re-)gain a deep understanding of statistics, covering topics from measures of central tendency to multiple linear regression with interaction effects. To this end, students will perform two tasks in the seminar: (1) Students will present a chapter from the seminal statistics book "An Adventure in Statistics. The Reality Enigma" (Field, 2016) “At a simple level ‘an adventure in statistics’ is a story about Zach searching for [his vanished girlfriend] Alice, and seeking the truth, but it’s also about the unlikely friendship he develops with a sarcastic cat, it’s about him facing his fear of science and numbers, it’s about him learning to believe in himself. It’s a story about love, about not forgetting who you are. It’s about searching for the heartbeats that hide in the gaps between you and the people you love. It’s about having faith in others.” (Field, 2016) (2) Students will combine the presentation of the book chapter with an application of the statistical methods to data on gender differences in unpaid domestic work. Research on the gendered division of unpaid domestic labor has largely focused on its physical dimension (i.e., actually doing housework or caring for children), while the cognitive dimension known as "mental labor" (i.e., thinking, planning, organizing, etc.) is only beginning to receive both public and scholarly attention (Reich-Stiebert et al., 2022). Mental labor is defined by five characteristics: it is cognitive, it has a managerial component, it is communal, it is anticipatory, and it is usually invisible to both the person doing it and the person benefiting from it, and it may well be that Alice simply left Zach because he saw her as the house manager and himself as her subordinate, helping, yes, but not taking responsibility. Students will be assisted in performing their computations in either R or Python using R Notebooks or Jupyter Notebooks.

Applied Algorithms in Estimation and in Control of Technical, Economical, and Biological Dynamical Systems (FSL) (Seminar)

Dozent/in: Paolo Mercorelli

Termin:
wöchentlich | Montag | 18:15 - 19:45 | 14.10.2024 - 31.01.2025 | C 40.106 Konrad-Zuse-Raum

Inhalt: Introduction on Matlab/Simulink; the most important Matlab/Simulink functions; structure of a Matlab/Simulink model simulation through different examples (technical systems, economic systems, biologic systems,...). The course embraces stochastic and deterministic methods of identification and control of dynamical systems. Static and Recursive Least Squares Methods to estimate parameters of systems (simple technical and economic systems). An optimal estimator - Kalman Filter: the linear and nonlinear case. Examples of usage of Kalman Filter in technical, economic and biologic systems. (Examples: 1. estimation of random variables using Kalman Filter. 2 Inflation estimation in a simple capital investment system. Velocity, current and load in motor applications). An optimal control strategy - Model Predictive Control: the linear case. Examples of control of a simple capital investment system with estimated inflation. Control of an electrical motor.

Does it stay or does it go? Testing the effect of moss incorporation on nitrate leaching from soils. (FSL) (Seminar)

Dozent/in: Pia Bradler, David Walmsley

Termin:
wöchentlich | Montag | 12:15 - 15:45 | 14.10.2024 - 31.01.2025 | C 12.006 Seminarraum

Inhalt: This module deals with the basic principles associated with performing a scientific experiment in the natural sciences. Experiments are performed as a means of addressing hypotheses and to what extent they can be accepted or rejected. Whilst most of you may have dealt with this basic scientific principle in theory, many of you probably have never actually performed a scientific experiment. This is not without challenges as we need to think carefully about how are our experimental design can answer our hypothesis as well as any factors that may influence the outcome before we perform any experiment. In this course we will test the hypothesis that the incorporation of moss into soils will reduce the amount of nitrate that is leached from the soil. This is a timely question, as the movement of nitrogen (mainly in the form of nitrate) from the soils to the groundwater is a critical issue in many agricultural systems including those in Germany and especially Lower Saxony. We will conduct the experiment in groups starting from the underlying theory on which the hypothesis (and hypothesis testing) is based upon, leading on to the experimental design and finally conducting and analysing the experiment itself. The results of your findings are then to be presented in form of a “mini-paper” which forms your written assessment.

How study designs inform the story: a practical approach to designing robust studies (FSL) (Seminar)

Dozent/in: Sophia Carmel Turner

Termin:
wöchentlich | Montag | 08:15 - 09:45 | 14.10.2024 - 31.01.2025 | C 12.102 Seminarraum

Inhalt: Data is all around us and is used on a daily basis to inform decision making, from the advertisements that you see on social media, to laws passed by government. However, data are not always accurately portrayed. To be able to make correct inferences, it is important to understand the conditions of the study, how the data was collected, and how it was analyzed. In this seminar, you will gain valuable insights and practical experience designing and testing different study design approaches. This will allow you to distinguish between different methods of collecting data, understand how the parameters of the study affect the interpretation of the data, how to manage confounding factors and sampling biases, and finally how to create your own defensible study design. By the end of this seminar, you will have a greater appreciation for the power of a robust study design and be better equipped to understand how data is portrayed in the real world.

Introduction to GPU Programming (FSL) (Seminar)

Dozent/in: N. N.

Termin:
wöchentlich | Freitag | 12:15 - 13:45 | 14.10.2024 - 31.01.2025 | C 7.019 Seminarraum

Inhalt: This course is designed to teach students how to leverage the power of Graphics Processing Units (GPUs) through parallel programming. Traditionally, programs utilize the CPU as the primary unit for execution. In this course, students will learn how to run programs on a GPU and observe the significant difference in processing speed. Initially, GPU cards were used exclusively for video rendering. However, with the advent of CUDA, we can now utilize GPUs for general-purpose computing, an approach known as General-Purpose Computing on GPUs (GPGPU). CUDA is an application programming interface (API) that enables software to use the GPU for accelerated processing. CUDA utilizes C++ as its programming language. Therefore, a solid understanding of C++ is essential for students to effectively work with CUDA. This course is divided into four modules. The first two modules will provide students with a strong foundation in C++ programming. Then, students will apply their C++ knowledge to work with CUDA and execute programs on a GPU. At the end of each module, there will be a project designed to reinforce the concepts learned by students. To ensure the course's generality, these projects will focus on image processing tasks, such as image coloring and blurring. After all, who doesn’t enjoy working with images! Course Modules The course is structured into four modules: C++ Fundamentals, Object-Oriented Programming in C++, GPU Programming, and GPU Hardware. C++ Fundamentals In this module, students will learn to develop, compile, and execute C++ programs. Topics covered include syntax, variables, functions, containers, stack memory, heap memory, and file I/O. At the end of this module, students will work on a project involving reading and writing images using the OpenCV library and converting them into grayscale images. Object-Oriented Programming in C++ This module covers the core principles of object-oriented programming: encapsulation, inheritance, polymorphism, and abstraction. Moreover, students will learn how to compile multi-file projects using Cmake. The project for this module involves creating an image editor program that allows students to load, display, and perform basic operations on images (grey-scaling and blurring). GPU Programming In this module, students will learn the core concepts of GPU programming and the main CUDA syntax. Students will run simple programs on a GPU for the first time and explore its capabilities. The project at the end of this module involves converting the CPU-based program from the previous module to a GPU-based program, allowing students to observe the difference in program speed. GPU Hardware This module delves into the hardware architecture of GPUs and the mechanisms of parallel data communication within the card. By comprehending the intricacies of GPU hardware, students can optimize GPU utilization for enhanced performance. By the end of this module, students will revisit their previous projects, applying their knowledge of GPU data communication to further accelerate program speed.

Smart Home Basics (Seminar)

Dozent/in: Andreas Möller

Termin:
wöchentlich | Montag | 12:15 - 13:45 | 14.10.2024 - 31.01.2025 | C 12.013 Seminarraum

Inhalt: Die Technik ist so weit fortgeschritten, dass man heute Computer in Alltagsgegenstände einbauen kann, etwa in LED-Leuchten, Thermostatventilen; Lichtschalter werden ersetzt durch "Panels" und Smartphone-Apps. Das Smart Home kann man aber auch selbst aufbauen oder um eigene Komponenten erweitern. Es ist möglich, sehr einfach einen winzigen Microcomputer um Sensoren (Temperatur, Bewegung, Energieverbrauch etc.) auszustatten, Aktoren wie Leuchtdioden oder Relais (z.b. als Schalter zu Ein- und Ausschalten von 240V-Leitungen) zu ergänzen und zu programmieren. Dazu besteht die Hardware (hoffentlich Raspberry Pi Pico) aus einem USB-Anschluss zum Anschluss an ein Laptop (benötigt man für das Programmieren), einem Anschluss für Spannungsversorgung und vor allem vielen Input- und Outputanschlüsse für die Sensoren und Aktoren. NICHT vorausgesetzt werden Hard- und Softwarekenntnisse, es ist auch nicht notwendig zu wissen, wie man Hardware aufbaut ("löten"). Im Vordergrund stehen kreative computerbasierte Lösungen für das Smart Home. Das fängt an bei einfachen Blicklichtern, Energieverbrauchsmessungen, Temperaturmessungen, CO2-und VOC-Messungen, Feinstaubmessungen... Im Internet finden sich zahlreiche Beispiele, was man alles mit diesen kleinen Microcomputern machen kann. In einer letzten Phase des Seminars werden Fragen der Zukunft der Gesellschaft und Nachhaltigkeit behandelt (Open Source und Open Hardware, Maker-Communities in der Gesellschaft, Green IT, Technik-Trends wie Matter usw.).

Course Schedule

Lehrveranstaltungen