Exploring 10 601 Machine Learning Spring 2015 Lecture 12
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- Lecture 12
- Topics: high-level overview of
- Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
- Topics: boosting, weak vs strong PAC
- Topics: principal component analysis (PCA),
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 12
Topics: inference in graphical models, d-separation, conditional independence Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Topics: inference in graphical models, expectation maximization (EM)
Course:
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