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: kernel methods, margin, kernelizing a
- Topics: inference in graphical models, d-separation, conditional independence
- Topics: introduction to computational
- Topics: clustering, k-means, k-means++, hierarchical clustering
- Subtleties of Naive Bayes HMM1
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 22
Topics: principal component analysis (PCA), Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecture 22 Topics: neural networks, backpropagation, deep
Topics: reinforcement
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 22.
