Bliv en del af udviklingen
Efteruddannelse i kunstig intelligens tager afsæt i den nyeste viden på området. Udviklingen af deep learning modeller til analyse og generering af billeder og tekst åbner hver dag nye muligheder. Deep learning er en af hovedteknikkerne i kunstig intelligens og ligger bl.a. til grund for store sprogmodeller som chatGPT. Vi kan indføre dig i de nyeste modeller og deres anvendelse inden for fx miljø- og klimaovervågning, biodiversitet, medicinsk teknologi og observationelle befolkningsundersøgelser.
Styrk dine kompetencer
Natural Language Processing og de såkaldte Large Language Models spiller en central rolle i forskning og udvikling af kunstig intelligens. De bygger på attention baserede modeller, som også danner grundlag for den nyeste billedteknologi. Gennem en kombination af Machine Learning og praktisk erfaring kan du lære mere om de grundlæggende beregningsmodeller, samt hvordan tekst repræsenteres i Machine Learning algoritmer. Du kan også blive klogere på udvalgte, avancerede emner inden for Natural Language Processing og computer vision.
Skræddersyede forskningsbaseret virksomhedsforløb giver jer mulighed for at tilrettelægge et kursusforløb for en gruppe medarbejdere.
Det giver jer adgang til den nyeste forskning inden for kunstig intelligens i en kontekst, der passer til jeres virksomhed. I tæt samarbejde med jer og KU’s forskere tilrettelægger vi et efteruddannelsesforløb. Det kan være et forløb inden for specifikke faglige emner i takt med, at ny forskning bliver tilgængelig, og derved byder på nye muligheder.
Kontakt os for at høre nærmere
Københavns Universitet
Videreuddannelse og Livslang Læring
lifelonglearning@adm.ku.dk
| Kursus | Niveau | Startdato | Varighed |
|---|---|---|---|
| Advanced Topics in Natural Language Processing (ATNLP) The purpose of this course is to expose students to selected advanced topics in natural language processing. Topics include: natural language understanding, representation learning, multitask learning, learning from multiple modalities, deep generative models, reinforcement learning, and generative adversarial learning. |
Kandidat | November | 9 uger |
| Advanced Deep Learning (ADL) This course will give you detailed insight into deep learning, covering algorithms, theory and tools. Deep learning is a fundamental technique in AI and it has pushed the state-of-the-art in numerous applications across a wide range of domains. These include object classification in images, generative models for images, and natural language processing with tasks such as automatic translation. |
Kandidat | April | 9 uger |
| Advanced Topics in Image Analysis (ATIA) The purpose of this course is to expose the student to selected advanced topics in image analysis. The course will bring the student up to a level sufficient for master thesis work within image analysis and computer vision. Focus is not on specific topics, but rather on recent research trends.
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Kandidat | September | 9 uger |
| Medical Image Analysis (MIA) This course will give an introduction to medical image formation in the different scanning modaliti es: X-ray, CT, MR, fMRI, PET, US etc. We will continue with the underlying image analysis disciplines of segmentation, registration and end with specific machine learning applications in clinical practise. A key to achieving success in the medical image analysis is formal evaluation of methodologies, thus an introduction to performance characterisation will also be a central topic. We will use techniques from image analysis and real-world examples from the clinic. |
Kandidat | September | 9 uger |
| Natural Language Processing (NLP) Have you ever wondered how to build a system that can process, understand or generate text automatically? For instance, to translate between languages, answer questions, or recognise the names of people in text? Then this course is for you. This course will introduce the fundamentals of natural language processing (NLP), i.e., computational models of language and their applications to text. We will combine machine learning (ML) with a strong hands-on experience. |
Kandidat | September | 9 uger |
| Signal and Image Processing (SIP) The course introduces basic computational, statistical, and mathematical techniques for representing, modeling, and analysing signals and images. Signals and images are measurements, which are correlated over time and/or space, and these measurements typically originate from a physical system ordered on a grid. |
Kandidat | Februar | 9 uger |
| Vision and Image Processing (VIP) Vision and Image Processing (VIP) gives an overview of modern vision techniques. Focus is both on conceptual understanding of the models and methods, and on practical experience. The course covers state of the art methods for image analysis including how to solve visual processing tasks such as object recognition and content-based image search and retrieval. |
Kandidat | November | 9 uger |
| SAS og AI i praksis Kurset dækker avancerede programmeringsmuligheder i SAS, herunder brug af datastep, konvertering af datasæt fra andre formater og håndtering af formatbiblioteker. Specielle emner, der inkluderer håndtering af tekstvariabler, do-løkker, arrays og SAS-makroer, anvendt til fx Monte Carlo og bootstrap-analyser, gennemgås. |
Bachelor og kandidat | Forår | 1 semester |
| AI for Humanity: Machine Decisions, Learning and Societal Consequences This course provides students with a comprehensive foundation in state-of-the-art machine learning methods, their practical and methodological applications, and their societal consequences. The course focuses equally on technical expertise and critical analysis of machine decision-making in various societal contexts, ensuring interdisciplinary appeal to computational and social science students alike. |
Kandidat og Ph.D. | Efterår | 1 semester |
| English - Elective Subject, topic 3: Ethics in AI - Navigating Fairness, Biases, and Transparency Challenges in the Humanities This course introduces students to artificial intelligence applications, with particular attention to the current use of AI systems in the humanities. Students will reflect on the ethical implications of AI in teaching and learning contexts (e.g. for text production, translation, and language learning) as well as in a series of real-world cases. Contexts and cases will focus on English language use, learning and teaching. |
Bachelor | Forår | 1 semester |
| Ethics of AI This course introduces students to artificial intelligence applications, with particular attention to healthcare systems. After a technical overview of how some of these systems work from a programming and learning perspective, the course will offer students an overview of the various ethical, social, legal and political issues around AI and especially machine learning algorithms within healthcare and health informatics – but also their wider use within medicine and public health, and society at large. In particular, students will have the opportunity to learn about and work with ethical frameworks as lenses through which to analyze the implications of AI. |
Kandidat | Efterår | 1 semester |