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03:08

Welcome!

Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
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07:00

(A) Linear algebra 3: Direct sum

Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
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41:28

(H) High-dimensional spaces

Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
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16:28

(A) Linear algebra 22: Scalar product

Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
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"ML in Science": Showcase of the innovation fund projects (III)

This is the live stream of the “Machine Learning in Science” Conference 2020. The conference is held by the Cluster of Excellence “Machine Learning: New Perspectives for Science”, University of Tübingen, Germany. Conference program: https://uni-tuebingen.de/en/165313 Follow @ml4science on twitter for latest updates. Source of this “Tübingen Machine Learning” AI Video
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01:02:36

Self-Driving Cars – Lecture 7.1 (Odometry, SLAM and Localization: Visual Odometry)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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01:35:34

Probabilistic ML – Lecture 9 – Gaussian Processes

This is the ninth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig in the Summer Term 2020 at the University of Tübingen. Time-stamped slides available at https://uni-tuebingen.de/en/180804. Contents: * Gaussian processes * kernels * (linear and cubic) splines from “infinitely wide neural networks” with step and ReLU features * kernels form a […]
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46:43

(P) Probability 17a: Glivenko-Cantelli theorem

Part of the Course “Mathematics for Machine Learning”, Winter Term 2020/21, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
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37:28

Nicole Ludwig: Challenges in Renewable Energy Systems: A (mostly) Probabilistic Perspective

Nicole Ludwig is head of the Early Career Research Group “Machine Learning in Sustainable Energy Systems (MSES)” at the University of Tübingen. Her group is interested in developing new machine learning algorithms to build and maintain a future sustainable energy system, which is intelligent and relies on as many renewable energy sources as is economically […]
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08:39

Self-Driving Cars – Lecture 3.4 (Direct Perception: Driving Policy Transfer)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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36:44

Self-Driving Cars – Lecture 11.3 (Object Tracking: Association)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video