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TITLE: Week 39: Optimization and Gradient Methods
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TITLE: Week 39: Resampling methods and logistic regression
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AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo
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DATE: Week 39
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===== Lecture Monday September 22 =====
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!bblock Material for the lecture on Monday September 22
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* Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of gradient methods
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* Stochastic Gradient descent with examples and automatic differentiation (theme also for next week).
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# * "Video of lecture":"https://youtu.be/ISGpTC28Vmk"
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# * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember23.pdf"
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* Readings and Videos:
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* These lecture notes
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* For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.
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* Raschka et al, pages 53-76 on Logistic regression and pages 37-52 on gradient optimization
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* "Video on gradient descent":"https://www.youtube.com/watch?v=sDv4f4s2SB8"
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* "Video on stochastic gradient descent":"https://www.youtube.com/watch?v=vMh0zPT0tLI"
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!split

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