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Films · Computer science · Machine learning

Prof. Dr. Dirk Schieborn

Applied Mathematics 3 · Reutlingen University

Lecture film · Applied Mathematics 3

A Spam Filter, a Robot Vacuum, a Velvet Cloth: How Machines Learn

Lecture film · Applied Mathematics 3

A Spam Filter, a Robot Vacuum, a Velvet Cloth: How Machines Learn

Prof. Dr. Dirk Schieborn

Applied Mathematics 3 · Reutlingen University

Machine learning needs a task, experience and a performance measure. Supervised learning learns from answers, unsupervised learning looks for groups, reinforcement learning learns from reward and punishment. And a good model hits the future, not every old point.

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More films: Signature · Prof. Dr. Dirk Schieborn

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Chapter 1 · Task, experience, performance

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A Spam Filter, a Robot Vacuum, a Velvet Cloth: How Machines Learn

Prof. Dr. Dirk SchiebornApplied Mathematics 3 · Reutlingen University
Watch on YouTube

NewNov 3, 2026 · 6 chapters

An explainer film in six chapters, made from a real lecture: task, experience and performance measure for a spam filter and a forecast, supervised learning as a line across the map, unsupervised learning without labels, reinforcement learning with reward and punishment from the robot vacuum to AlphaGo, the table of features and labels as a cloud of points in space, and why a model with more sliders turns into a rubber mat instead of a velvet cloth. Pause anywhere and ask.

How a film works on heyprof

Every film here listens. Pause it, ask it questions and let it continue with your own examples.

  1. PauseTap pause or press the space bar. The film waits right at that moment.
  2. AskTap “Ask” and type what you want to know. The answer comes inside the film, with picture and voice.
  3. Steer itTell it what to show, for example “Do this with z = 2 − 3i”. The film sets up the app and works it out.
  4. Try it yourselfWhile it is paused, drag points and sliders yourself. “Continue” takes you back into the film.

Without an account: five questions per visit. With an account you can also ask by voice.

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