Exploring Aa 18 19 Lecture 3
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- Supervised learning, minimization (least squares), polynomial regression.
- Dimensionality reduction: feature extraction with PCA; self-organzing maps.
- Maximum Margin Classifiers. Support vector machines for linear classification.
- Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
- Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering. Bayesian ...
In-Depth Information on Aa 18 19 Lecture 3
Overfitting and regularization with polynomial regression. Select models: Train, validate, test. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Introduction. Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features.
Deep learning. The problem of backpropagation. Autoencoders and Stacked Denoising Autoencoders.
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