Neural Data Science — Lecture 1 — Spike detection
Lecture 1 in the Neural Data Science course by Philipp Berens, Summer Term 2021 at the University of Tübingen. www.berenslab.org Source of this “Tübingen Machine Learning” AI Video
Deep Learning – Lecture 4.1 (Deep Neural Networks II: Output and Loss Functions)
Lecture: Deep Learning (Prof. Andreas Geiger, University of Tübingen)
Statistical Machine Learning Part 11 – Cross validation
Part of the course “Statistical Machine Learning”, Summer Term 2020, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
Zeynep Akata: Learning Decision Trees Recurrently through Communication
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
(A) Linear algebra 19: Convex set induces a norm
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
Neural Compression — Lecture 4 — Random Variables and Autoregressive Models
Fourth week of the course “Data Compression With Deep Probabilistic Models” by Prof. Robert Bamler at University of Tübingen (summer term of 2021). These videos were deliberately recorded at a calm pace. Click the cogwheel in the lower right corner of the video player to adjust playback speed to your personal preference. Viewers who are […]
Computer Vision – Lecture 11.1 (Self-Supervised Learning: Preliminaries)
Lecture: Computer Vision (Prof. Andreas Geiger, University of Tübingen)
(C) Calculus 6: Fundamental theorem of calculus on R
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
(S) Statistics 10: The bootstrap
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
Self-Driving Cars – Lecture 4.1 (Reinforcement Learning: Markov Decision Processes)
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
Self-Driving Cars – Lecture 12.3 (Decision Making and Planning: Behavior Planning)
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
Introduction to Machine Learning – 02 – Multiple linear regression and SVD
Lecture 2 in the Introduction to Machine Learning (aka Machine Learning I) course by Dmitry Kobak, Winter Term 2020/21 at the University of Tübingen. Source of this “Tübingen Machine Learning” AI Video
Statistical Machine Learning Part 38 – Statistical learning theory: Convergence and consistency
Part of the Course “Statistical Machine Learning”, Summer Term 2020, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
Neural Compression — Lecture 1 — Problem Setting & Symbol Codes
First full lecture of the course “Data Compression With Deep Probabilistic Models” by Prof. Robert Bamler at University of Tübingen (summer term of 2021). These videos were deliberately recorded at a calm pace. Click the cogwheel in the lower right corner of the video player to adjust playback speed to your personal preference. Playlist for […]
Deep Learning – Lecture 7.2 (Convolutional Neural Networks: Downsampling)
Lecture: Deep Learning (Prof. Andreas Geiger, University of Tübingen)
Peter Dayan: Modelling and Manipulating Behaviour Using Recurrent Networks
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
(A) Linear Algebra 29: Singular value decomposition
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
Neural Data Science — Lecture 7 — Single cell receptive field estimation
Lecture 7 in the Neural Data Science course by Philipp Berens, Summer Term 2021 at the University of Tübingen. Source of this “Tübingen Machine Learning” AI Video
Computer Vision – Lecture 8.2 (Shape-from-X: Photometric Stereo)
Lecture: Computer Vision (Prof. Andreas Geiger, University of Tübingen)
(C) Calculus 12: A set that is not Lebesgue-measurable
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
Self-Driving Cars – Lecture 7.3 (Odometry, SLAM and Localization: Localization)
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
Probabilistic ML – Lecture 4 – Sampling
This is the fourth 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: * Monte Carlo Methods * Moments and Expectations * Rejection and Importance Sampling © Philipp Hennig / University of Tübingen, 2020 CC BY-NC-SA 3.0 […]
Statistical Machine Learning Part 7 – Linear least squares
Part of the course “Statistical Machine Learning”, Summer Term 2020, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video
Statistical Machine Learning Part 36 – Spectral clustering, unnormalized case
Part of the Course “Statistical Machine Learning”, Summer Term 2020, Ulrike von Luxburg, University of Tübingen Source of this “Tübingen Machine Learning” AI Video