Short Overview: Overfitting, model selection and regularization with logistic regression. Supervised learning, minimization (least squares), polynomial regression.

Aa 19 20 Lecture 5 -

Overfitting, model selection and regularization with logistic regression. Supervised learning, minimization (least squares), polynomial regression.

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  • Overfitting, model selection and regularization with logistic regression.
  • Supervised learning, minimization (least squares), polynomial regression.

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AA 19/20 Lecture 5

AA 19/20 Lecture 5

Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.

AA 19/20 Lecture 4

AA 19/20 Lecture 4

Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.

AA 19/20 Lecture 9

AA 19/20 Lecture 9

Maximum Margin Classifiers. Support vector machines for linear classification.

AA 19/20 Lecture 6

AA 19/20 Lecture 6

Lazy learning. K-NN. Kernel regression and kernel density estimation.

AA 19/20 Lecture 21

AA 19/20 Lecture 21

Probabilistic Clustering: mixture models. Expectation-Maximization revisited. Graphical methods, Hidden markov models.

AA 19/20 Lecture 1

AA 19/20 Lecture 1

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AA 19/20 Lecture 2

AA 19/20 Lecture 2

Supervised learning, minimization (least squares), polynomial regression.

AA 19/20 Lecture 19

AA 19/20 Lecture 19

Hierarchical Clustering. Agglomerative and Divisive Clustering.

AA 19/20 Lecture 11

AA 19/20 Lecture 11

Multiclass classification. Bootstrapping. Bias-variance decomposition and tradeoff.

AA 19/20 Lecture 12

AA 19/20 Lecture 12

Read more details and related context about AA 19/20 Lecture 12.