University of Tübingen AI Videos - Artificial Intelligence Videos

Order By: Like

02:28:53

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 […]

22:27

Computer Vision – Lecture 11.1 (Self-Supervised Learning: Preliminaries)

Lecture: Computer Vision (Prof. Andreas Geiger, University of Tübingen)

33:24

(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

50:17

(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

33:03

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

10:12

Self-Driving Cars – Lecture 12.3 (Decision Making and Planning: Behavior Planning)

109

34:26

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

01:08:28

Deep Learning – Lecture 4.1 (Deep Neural Networks II: Output and Loss Functions)

Lecture: Deep Learning (Prof. Andreas Geiger, University of Tübingen)

18:28

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

59:29

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

31:25

(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

26:27

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

20:35

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

38:05

Self-Driving Cars – Lecture 7.3 (Odometry, SLAM and Localization: Localization)

52:18

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

42:33

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

01:02:50

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 […]

19:04

Neural Compression — Lecture 4 — Random Variables and Autoregressive Models

Computer Vision – Lecture 11.1 (Self-Supervised Learning: Preliminaries)

(C) Calculus 6: Fundamental theorem of calculus on R

(S) Statistics 10: The bootstrap

Self-Driving Cars – Lecture 4.1 (Reinforcement Learning: Markov Decision Processes)

Self-Driving Cars – Lecture 12.3 (Decision Making and Planning: Behavior Planning)

Neural Data Science — Lecture 1 — Spike detection

Deep Learning – Lecture 4.1 (Deep Neural Networks II: Output and Loss Functions)

Statistical Machine Learning Part 11 – Cross validation

Zeynep Akata: Learning Decision Trees Recurrently through Communication

(A) Linear algebra 19: Convex set induces a norm

(A) Linear Algebra 29: Singular value decomposition

Neural Data Science — Lecture 7 — Single cell receptive field estimation

Computer Vision – Lecture 8.2 (Shape-from-X: Photometric Stereo)

(C) Calculus 12: A set that is not Lebesgue-measurable

Self-Driving Cars – Lecture 7.3 (Odometry, SLAM and Localization: Localization)

Introduction to Machine Learning – 02 – Multiple linear regression and SVD

Statistical Machine Learning Part 38 – Statistical learning theory: Convergence and consistency

Neural Compression — Lecture 1 — Problem Setting & Symbol Codes

Deep Learning – Lecture 7.2 (Convolutional Neural Networks: Downsampling)

Peter Dayan: Modelling and Manipulating Behaviour Using Recurrent Networks

Computer Vision – Lecture 5.4 (Probabilistic Graphical Models: Belief Propagation)

Computer Vision – Lecture 5.5 (Probabilistic Graphical Models: Examples)

(A) Linear algebra 23: Orthogonal vectors and basis

#23 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 15]

Ep 01: Notebooks vs Python Projects

#19 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 11]