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
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
- Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Graphical methods, Hidden markov models. The Baum-Welch and Vitterbi algorithms.
- Introduction to clustering. K-means and k-medoids. Expectation maximization.
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. In this video, we will discuss some of the methods by which astronomers are able to measure the masses and diameters of the ...
Generative models: naive bayes, bayes. Comparing classifiers. Assignment
That wraps up our extensive overview of Aa 17 18 Lecture 1.
