Hier findest du eine \u00dcbersicht Deiner eingeschriebenen Kurse im KI-Campus-Moodle. Eine \u00dcbersicht \u00fcber alle Lernangebote des KI-Campus findest du auf unserer Hauptseite.
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Machine Learning (ML) <\/strong>is at the core of many applications of artificial intelligence<\/strong>. A key goal of this course series is to teach the fundamental building blocks <\/strong>behind supervised ML<\/strong>. Additional to understanding<\/strong> the theoretical basics, participants will learn how to apply<\/strong> and evaluate different ML algorithms in R and Python <\/strong>with a focus on tabular data.<\/p>\r\n After a general introduction to the basics of ML<\/strong> in part 1\/3, participants will get in touch with several learning algorithms<\/strong> in part 2\/3. Finally, we will cover the evaluation and tuning of ML models<\/strong> in depth in part 3\/3.<\/p><\/span><\/span><\/h5>","inLanguage":[],"instructor":[],"url":"https:\/\/moodle.ki-campus.org\/course\/view.php?id=6","learningResourceType":{"identifier":"https:\/\/w3id.org\/kim\/hcrt\/course","type":"Concept","inScheme":"https:\/\/w3id.org\/kim\/hcrt\/scheme"},"publisher":{"name":"KI Campus","type":"Organization","url":"https:\/\/ki-campus.org\/"},"license":[{"identifier":"proprietary","url":null}],"creator":[{"name":"KI Campus","type":"Organization"}],"access":["free"],"rating":{"avg":4.67,"count":3,"max":5}}},{"id":"a578a163-3b7f-5fac-9dea-75219875ca9f","type":"Course","attributes":{"name":"Dr. med. KI - Ethics","description":" Erfahre in diesem interaktiven Lernangebot aus der Reihe Dr. med. 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Finde im Kurs auch heraus, welche Rolle Aspekte wie Regulierung, Innovation und erkl\u00e4rbare KI (XAI) im ethischen Kontexten spielen und wie mit Formen der digitalen Diskriminierung umgegangen werden kann. <\/p> <\/p>KI-Chatbots wie ChatGPT <\/strong>sorgen seit Ende 2022 f\u00fcr Gespr\u00e4chsstoff rund um den Einsatz intelligenter Systeme in Bildungsinstitutionen und f\u00fcr oft auch emotional aufgeladene Diskussionen zu M\u00f6glichkeiten und Grenzen generativer KI<\/strong>. <\/p>","inLanguage":["de"],"instructor":[{"name":"Isabella Buck","type":"Person"},{"name":"Aljoscha Burchardt","type":"Person"},{"name":"Anika Limburg","type":"Person"},{"name":"Christian Spannagel","type":"Person"}],"url":"https:\/\/moodle.ki-campus.org\/course\/view.php?id=19","learningResourceType":{"identifier":"https:\/\/w3id.org\/kim\/hcrt\/course","type":"Concept","inScheme":"https:\/\/w3id.org\/kim\/hcrt\/scheme"},"publisher":{"name":"KI Campus","type":"Organization","url":"https:\/\/ki-campus.org\/"},"license":[{"identifier":"proprietary","url":null}],"creator":[{"name":"KI Campus","type":"Organization"}],"access":["free"],"rating":{"avg":5,"count":5,"max":5}}},{"id":"3bfdef91-1a7a-5d7c-87e8-8e74cfd7fa21","type":"Course","attributes":{"name":"Identifikation geeigneter KI-Anwendungsf\u00e4lle","description":" <\/p>\r\n <\/p>\r\n Die Identifizierung der richtigen KI-Use Cases<\/strong> ist entscheidend f\u00fcr eine erfolgreiche KI-Innovation<\/strong>. Nicht zuletzt angesichts einer ausgepr\u00e4gten Pr\u00e4senz im \u00f6ffentlichen Diskurs ergibt sich jedoch die diffuse Erwartung, dass KI universell wertschaffend eingesetzt werden kann. 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Aber\r\nman kann diesen Verzerrungen entgegenwirken und praktische\r\nStrategien zur Implementierung sozialverantwortlicher KI<\/b> entwickeln. Wir machen Sie vertraut mit dem Rahmenmodell\r\nder sozialverantwortlichen KI-Gestaltung<\/b>, ein Konzept, das darauf\r\nabzielt, Ethik und Gleichheit in den Entwicklungsprozess von KI-Technologien zu\r\nintegrieren. In f\u00fcnf Wissensnuggets<\/b> geben\r\nwir einen \u00dcberblick \u00fcber das Rahmenmodell und stellen die vier\r\nQuadranten des Modells vor. Unsere Wissensnuggets richten sich an alle\r\n\u2013 egal ob Entwickler:in,\r\nF\u00fchrungskraft oder Student:in \u2013 die aktiv dazu beitragen\r\nm\u00f6chten, dass Algorithmen wirklich intelligent, und damit auch gerecht und\r\numsichtig agieren. Viel\r\nSpa\u00df auf dem Weg zur Gestaltung von KI, die eine bessere Welt f\u00fcr alle schafft!<\/p> <\/p> <\/p>","inLanguage":["de"],"instructor":[{"name":"Nicole Dierolf","type":"Person"},{"name":"Nicola Marsden","type":"Person"},{"name":"Annelie Rothe-Wulf","type":"Person"},{"name":"Edda Sellin","type":"Person"}],"url":"https:\/\/moodle.ki-campus.org\/course\/view.php?id=29","learningResourceType":{"identifier":"https:\/\/w3id.org\/kim\/hcrt\/course","type":"Concept","inScheme":"https:\/\/w3id.org\/kim\/hcrt\/scheme"},"publisher":{"name":"KI Campus","type":"Organization","url":"https:\/\/ki-campus.org\/"},"license":[{"identifier":"proprietary","url":null}],"creator":[{"name":"KI Campus","type":"Organization"}],"access":["free"],"rating":{"avg":4.71,"count":7,"max":5}}},{"id":"1ced3cb3-d4f9-5f19-bbca-de9249515eb6","type":"Course","attributes":{"name":"Dr. med. KI - Basler Modul","description":" <\/p> Dr. med. 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Instead of starting with abstract syntax, students begin by exploring real-world questions\u2014ranging from interpreting correlations to evaluating medical studies\u2014and gradually build up both the technical and conceptual tools to answer them. <\/p>\r\n Throughout the course, participants will learn the fundamentals of data science, including extracting and visualizing data, understanding the distinction between causality and correlation, working with random variables, comparing samples, testing hypotheses, estimating unknown quantities, making predictions, and addressing the ethical issues that arise in data analysis. In parallel, they will be introduced to key programming concepts, including variables, control flow, data types, and built-in data processing functions.<\/p>\r\n By the end of the course, students will be able to formulate and test data-driven hypotheses, interpret statistical results, and write simple programs to analyze structured data. 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The syllabus includes Introduction: sensors and data acquisition in remote sensing, georeferencing of remote sensing data, hand-crafted features; Methods for Machine Learning and Deep Learning for image analysis; Deep domain adaptation and learning under label noise; Topographic applications of CNN: classification of land cover and land use for acquiring and updating geospatial databases; and Other applications of CNN for image analysis with examples from autonomous driving and the preservation of cultural heritage. <\/p>","inLanguage":[],"instructor":[{"name":"Hubert Kanyamahanga","type":"Person"}],"url":"https:\/\/moodle.ki-campus.org\/course\/view.php?id=46","learningResourceType":{"identifier":"https:\/\/w3id.org\/kim\/hcrt\/course","type":"Concept","inScheme":"https:\/\/w3id.org\/kim\/hcrt\/scheme"},"publisher":{"name":"KI Campus","type":"Organization","url":"https:\/\/ki-campus.org\/"},"license":[{"identifier":"proprietary","url":null}],"creator":[{"name":"KI Campus","type":"Organization"}],"access":["free"],"rating":{"avg":0,"count":0,"max":5}}},{"id":"af4ee9ea-a821-550a-ba30-430168722bba","type":"Course","attributes":{"name":"LUH: Machine Learning","description":" For Machine Learning course from Leibniz AI Academy Welcome to the course Artificial intelligence for production engineering<\/strong> from Leibniz AI Academy. 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Initially, it offers a formal\r\nunderstanding of probability theory by covering probability spaces, random\r\nvariables, distributions, expected values, and central convergence results.\r\nSubsequently, it introduces methods from descriptive and inferential\r\nstatistics, which are used for data description, estimation of key figures, and\r\nhypothesis testing.<\/p>","inLanguage":["en"],"instructor":[],"url":"https:\/\/moodle.ki-campus.org\/course\/view.php?id=53","learningResourceType":{"identifier":"https:\/\/w3id.org\/kim\/hcrt\/course","type":"Concept","inScheme":"https:\/\/w3id.org\/kim\/hcrt\/scheme"},"publisher":{"name":"KI Campus","type":"Organization","url":"https:\/\/ki-campus.org\/"},"license":[{"identifier":"proprietary","url":null}],"creator":[{"name":"KI Campus","type":"Organization"}],"access":["free"],"rating":{"avg":0,"count":0,"max":5}}},{"id":"0ebe4f19-73b4-5e5d-8d7a-cfb27fcf04aa","type":"Course","attributes":{"name":"KI-Vertiefung und Ethik - Micro-Credential II HU Berlin","description":" 2. 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Mit dem Kurs Sprachassistenzen als Chance f\u00fcr die Hochschullehre<\/strong> sollen Hochschullehrende KI-Sprachmodelle im Bildungskontext kreativ erproben<\/strong> und gleichzeitig auch kritisch hinterfragen k\u00f6nnen. So sollen \u00c4ngste im Umgang mit generativer KI abgebaut und die Rolle von Lehrenden als Lernbegleiter in einer durch digitale Tools gepr\u00e4gten Welt gest\u00e4rkt werden. Angestrebt wird ein Perspektivwechsel, der sich mit den Chancen intelligenter Sprachtechnologien f\u00fcr die universit\u00e4re Lehre<\/strong> besch\u00e4ftigt.\r\n
<\/h4>\r\n
\r\nDieser Online-Selbstlernkurs richtet sich an Studierende des Bachelor-Studiengangs Betriebswirtschaftslehre und Betriebswirte in Unternehmen, sowie weitere Interessierte, die KI-Technologien in ihrem beruflichen Kontext verstehen und anwenden m\u00f6chten. <\/span>
<\/span>
<\/span><\/span><\/h5>Im Kurs beleuchten wir den Handelssektor als praxisnahes Beispiel, um die vielf\u00e4ltigen Einsatzm\u00f6glichkeiten von KI aufzuzeigen. Neben relevantem Grundlagenwissen, erwirbst du Kenntnisse \u00fcber mathematisch-statistische Methoden und deren Anwendung. <\/span>
<\/span>
<\/span><\/h5>Im weiteren Verlauf laden wir dich dein, die Methoden durch anwendungsbezogene Python Programmier\u00fcbungen selbst auszuprobieren und deine Programmierkenntnisse durch ausgew\u00e4hlte \u00dcbungen zu vertiefen. <\/span><\/h5>
<\/p>
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Target group:<\/strong> Students of geodesy and geoinformatics
Credits:<\/strong> 5
Language:<\/strong> English
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\u00a0<\/h5>\r\n
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