Lectures
Lectures 1 & 2. Introduction to machine learning and data mining.
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Lectures 3 & 4. SVD
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Lecture 5. Linear supervised learning.
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Lecture 6. Ridge regression.
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Lecture 7. Constrained linear regression.
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Lectures 8 & 9. Kernel regression and classification.
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Lecture 10. Unsupervised learning and EM.
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Lecture 11. Probabilistic graphical models.
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Lecture 12. Boosting.
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