Probabilistic Machine Learning Lectures
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Lecture 1. Introduction.
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Lecture 2. PageRank - Why Math helps.
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- Lecture 3. Probability introduction.
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- Lecture 4. Probabilistic graphical models.
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- Lecture 5. Linear modelling: least squares, ridge and
lasso. Regression and classification.
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- Lecture 6. Nonlinear supervised learning.
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- Lecture 7. Constrained optimisation and SVMs.
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- Lecture 8. Unsupervised Learning. K-means, mixtures and EM.
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Demos:
- Lecture 9. Dynamic models.
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- Lecture 10.
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