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