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openHPI is a free, open learning platform from the Hasso Plattner Institute, offering courses on IT-Technologies and digital transformation.
Learn basic programming concepts by coding in Python and R, working through exercises drawn from digital health.
Learn basic and intermediate programming in Python and R: data structures, conditions, loops, data input and output, analysis and visualization.
Learn basic concepts of statistical data analysis and apply them practically to wearable sensor data in mobile computing and connected healthcare.
Discover insights from leading experts at EMOOCs 2023, exploring the latest trends and innovations in massive open online courses and digital education over three engaging days.
Assess AI hype versus reality, power business strategy with AI, integrate it into operations, scale for profitable growth, and examine Germany's position in the AI landscape.
Learn to program in Python from scratch: variables, data types, loops, functions, libraries, and file input and output, and write simple programs.
Use quantum algorithms on IBM Quantum to simulate molecules and study their properties, exploring applications in material and drug design.
Learn how quantum computers tackle hard combinatorial optimization problems, working through the algorithms used to formulate and solve them on IBM Quantum hardware.
Learn how to select and implement pre-trained AI models, using a speech-to-summary pipeline as the worked example.
Learn to analyze time series data using trends, seasonality and stationarity, then build forecasts with classical statistical models, machine learning, and RNNs, LSTMs and transformers.
Energy-efficient AI can rival supercomputer weather prediction: apply LoRA fine-tuning, diffusion models, and GNSS-based sensing to forecast accurately while cutting energy use and COâ‚‚ emissions.
Explore how DNA stores the code of life: study cell components, discover genetic variants and mutations, and assess their links to diseases such as cancer.
Learn the principles and technologies of Linked Data, including ontologies and Semantic Web knowledge representation, to discover, access, integrate and reuse data across the Web of Data.
Learn design research methods: spot workarounds and misuses, observe potential users in their context, conduct qualitative interviews, and infer meaning from your findings.
Study learning from informant, a formal model of binary classification: prove consistency and mind-change results, apply Blum and Blum's separation technique, and adapt models toward incremental deep learning.
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