Introduction to 10 601 Machine Learning Spring 2015 Lecture 20
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 20. Topics: wrap-up of semi-supervised
10 601 Machine Learning Spring 2015 Lecture 20 Comprehensive Overview
Topics: Logistic regression and its relation to naive Bayes, gradient descent Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ... Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
Topics: inference in graphical models, d-separation, conditional independence
Summary & Highlights for 10 601 Machine Learning Spring 2015 Lecture 20
- Topics: principal component analysis (PCA),
- Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging
- Topics: high-level overview of
- Topics: introduction to computational
- Topics: clustering, k-means, k-means++, hierarchical clustering
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 20.
