Understanding Aa 18 19 Lecture 7
If you are looking for information about Aa 18 19 Lecture 7, you have come to the right place. Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
Key Takeaways about Aa 18 19 Lecture 7
- Introduction.
- Perceptron and Multilayer Perceptron.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Dimensionality reduction: feature extraction with PCA; self-organzing maps.
- Supervised learning, minimization (least squares), polynomial regression.
Detailed Analysis of Aa 18 19 Lecture 7
Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Introduction to clustering. K-means and k-medoids. Expectation maximization. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
Generative models: naive bayes, bayes. Comparing classifiers. Assignment 1.
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