DELTA
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DELTA
Deep Learning for Text Analytics (DELTA) focuses on modern natural language processing using deep neural networks, building on prior knowledge in machine learning and Python. Students explore techniques such as word embeddings, RNNs, and Transformers, and apply them to real-world tasks like sentiment analysis and document classification. The course combines theory with hands-on programming using libraries like Keras and NLTK.
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Requirements
- Prior completion of the Business Analytics and Data Science module or equivalent knowledge in machine learning and solid Python programming skills are expected.
General Overview
Description
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Meet the instructors !
Stefan received a PhD from the University of Hamburg in 2007, where he also completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. He serves as an associate editor for the International Journal of Business Analytics, Digital Finance, and the International Journal of Forecasting, and as department editor of Business and Information System Engineering (BISE). Stefan has secured substantial amounts of research funding and published several papers in leading international journals and conferences. His research concerns the support of managerial decision-making using quantitative empirical methods. He specializes in applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. Stefan actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.