Winter term 2022/2023
| 19330101 (V) /19330102 (Ü): Machine Learning for Data Science (VL/Ü) | ||
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| The course provides an overview of machine learning (ML) methods and algorithms for data science. It consists of two parts: The first part of the course discusses the emergence of big data, presents common ML-based visualization techniques for large datasets, and introduces basic ML methods such as principal component analysis, k-means clustering, linear regression, and linear discriminant. ... | ||
| Instructor | Dr. Grégoire Montavon | |
| Time | Oct 18, 2022 - Feb 16, 2023 Tuesday (4-6 p.m., lecture room Takustr. 9) and Thursday (12-2 p.m., lecture room Takustr. 9) Tutorial: online | |
| 19332311: Explainable AI for Data Science (Seminar) | ||
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| Explainable AI is a recent and growing subfield of machine learning (ML) that aims to bring transparency into ML models without sacrificing their predictive accuracy. This seminar will explore current research on the use of Explainable AI for extracting insights from large datasets of interest. Use cases in biomedicine, chemistry and earth sciences will be covered. Students will select a few ... | ||
| Instructor | Dr. Grégoire Montavon | |
| Time | Oct 21, 2022 - Feb 17, 2023 Friday 2 - 4 a.m. | |
| 19333511: Machine Learning for Process Monitoring (Seminar) | ||
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| Numerous real-world processes require close monitoring by humans and machine learning (ML) models in order to verify calibration/stability and detect potential shifts/anomalies. The real-time nature of such tasks brings unique challenges from a ML perspective. This seminar will explore relevant ML aspects such as online learning, real-time data visualization, and energy efficiency. Use cases in ... | ||
| Instructor | Dr. Grégoire Montavon | |
| Time | Oct 21, 2022 - Feb 17, 2023 Friday, 4-6 a.m. | |