Exploring 10 601 Machine Learning Spring 2015 Lecture 26
Exploring 10 601 Machine Learning Spring 2015 Lecture 26 reveals several interesting facts.
- Topics: semi-supervised
- Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
- Topics: support vector machines (SVM), semi-supervised
- Topics: clustering, k-means, k-means++, hierarchical clustering Lecturer: Maria-Florina Balcan ...
- Topics: inference in graphical models, d-separation, conditional independence Lecturer: Tom Mitchell ...
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 26
Topics: deep Topics: reinforcement Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ... Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
Topics: application of naive Bayes to document classification, Gaussian naive Bayes and application to brain imaging Lecturer: ...
Stay tuned for more updates related to 10 601 Machine Learning Spring 2015 Lecture 26.
