Understanding 10 601 Machine Learning Spring 2015 Lecture 17
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 17. Topics: kernel methods, margin, kernelizing a
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 17
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
- Topics: support vector machines (SVM), multi-class classification, constrained optimization using Lagrange multipliers Lecturer: ...
- Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
- Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 17
Topics: support vector machines (SVM), semi-supervised Topics: generalization error of Adaboost, margin, perceptron algorithm Lecturer: Maria-Florina Balcan ... Topics: semi-supervised
Topics: additional practice for graphical models, conditional independence, inference Lecturer: Micol Marchetti-Bowick ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 17.
