Exploring 10 601 Machine Learning Spring 2015 Lecture 22
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 22.
- Topics: inference in graphical models, d-separation, conditional independence
- Topics: deep learning, restricted Boltzmann machines, privacy in
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: never-ending
- Topics: sample complexity, Rademacher complexity, regularization, overfitting Lecturers: Maria-Florina Balcan, Tom Mitchell ...
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 22
Topics: principal component analysis (PCA), Lecture 22 Subtleties of Naive Bayes HMM1 Topics: high-level overview of
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 22.