Computer Vision: 6th lecture (selected topics: biases in data sets, uncertainty in learning)
Topics discussed: Selected topics: biases in data sets, uncertainty in learning Find the course syllabus at https://www.glauner.info/teaching.
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Computer Vision: guest lecture (detecting forged paintings by Wolfgang Reuter)
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Computer Vision: 1st lecture (introduction, pixels and filters)
![Yann LeCun's $1B Bet Against LLMs [Part 1]](https://i.ytimg.com/vi/kYkIdXwW2AE/hqdefault.jpg?sqp=-oaymwEjCNACELwBSFryq4qpAxUIARUAAAAAGAElAADIQj0AgKJDeAE=&rs=AOn4CLDbV4izF3i-wxevCVIn7FJjoy1vlA)
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Yann LeCun's $1B Bet Against LLMs [Part 1]
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Ch 3 Cleaning Messy Data
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Computer Vision: 5th lecture (image sequence processing)
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Gil Strang's Final 18.06 Linear Algebra Lecture
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Can AI Become a Real Data Scientist? | Gaël Varoquaux on scikit-learn, Probabl & Scientific Judgment
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Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026
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RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source
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Billionaire's WARNING: I'm SELLING. The Crash Is Already Here!
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Algorithms and Data Structures: 6th lecture (recursion, sorting)
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The Big Short (2015): The Jenga Scene – Explaining the Financial Collapse
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Yann LeCun: World Models: Enabling the next AI revolution
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What rebuilding AlphaGo teaches us about self-play, RL, and future of LLMs - Eric Jang
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"A.I. and Our Economic Future," Professor Chad Jones
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1: Introduction to Neural Networks and Deep Learning; Training Deep NNs
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Algorithms and Data Structures: 12th lecture (quantum computing)
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From Child Prodigy to Winning Fields Medal, Nobel of Math
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Training Sand to Think: Artificial General Intelligence & Future of Physics
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