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
- Empirical Risk Minimization. Decision theory. Probably Approximately Correct Learning. VC dimension and shattering.
- Hi Everyone. Welcome to JR College. I am Rahul Jaiswal. Like, share and subscribe. #jrcollege . Follow JR College Insta Page ...
- Supervised learning, minimization (least squares), polynomial regression.
- Scoring classifiers. Cross-validation. Overfitting, model selection and regularization with logistic regression.
- Overfitting and regularization with polynomial regression. Select models: Train, validate, test.
Detailed Analysis of Aa 17 18 Lecture 1
Introduction to clustering. K-means and k-medoids. Expectation maximization. Affinity Propagation clustering and problems with prototype-based clustering. Density Clustering. Clustering validation. Hierarchical Clustering. Agglomerative and Divisive Clustering. Clustering Features.
Classification. Linear separability and discriminants. Logistic Regression. Using linear classifiers in higher dimensions.
That wraps up our extensive overview of Aa 17 18 Lecture 1.