Exploring Aa 17 18 Lecture 17
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- Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
- Dimensionality reduction: feature extraction with PCA; self-organzing maps.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Lazy learning. K-NN. Kernel regression and kernel density estimation.
In-Depth Information on Aa 17 18 Lecture 17
Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Introduction to clustering. K-means and k-medoids. Expectation maximization. Introduction. Hi Everyone. Welcome to JR College. I am Rahul Jaiswal. Like, share and subscribe. #jrcollege . Follow JR College Insta Page ...
Supervised learning, minimization (least squares), polynomial regression.
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