Understanding Aa 17 18 Lecture 1
Let's dive into the details surrounding Aa 17 18 Lecture 1. Introduction.
Key Takeaways about Aa 17 18 Lecture 1
- Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- In this video, we will discuss some of the methods by which astronomers are able to measure the masses and diameters of the ...
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
Detailed Analysis of Aa 17 18 Lecture 1
Supervised learning, minimization (least squares), polynomial regression. Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions. Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
Generative models: naive bayes, bayes. Comparing classifiers. Assignment
That wraps up our extensive overview of Aa 17 18 Lecture 1.
