Introduction to Aa 19 20 Lecture 1
Exploring Aa 19 20 Lecture 1 reveals several interesting facts. Introduction.
Aa 19 20 Lecture 1 Comprehensive Overview
Hierarchical Clustering. Agglomerative and Divisive Clustering. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Supervised learning, minimization (least squares), polynomial regression.
SVM: soft margins, kernel trick, overfitting and regularization. Assignment
Summary & Highlights for Aa 19 20 Lecture 1
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Introduction to deep learning.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Ensemble methods: bagging and boosting.
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