Understanding Aa 18 19 Lecture 2
Let's dive into the details surrounding Aa 18 19 Lecture 2. Supervised learning, minimization (least squares), polynomial regression.
Key Takeaways about Aa 18 19 Lecture 2
- Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Introduction.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- SVM: soft margins, kernel trick, overfitting and regularization.
Detailed Analysis of Aa 18 19 Lecture 2
Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
Dimensionality reduction: feature extraction with PCA; self-organzing maps.
That wraps up our extensive overview of Aa 18 19 Lecture 2.
