Exploring 10 601 Machine Learning Spring 2015 Lecture 15
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 15.
- Topics: generalization error of Adaboost, margin, perceptron algorithm
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: support vector
- Topics: kernel methods, margin, kernelizing a
- Topics: exam review, review of past exam questions
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 15
Topics: boosting, weak vs strong PAC Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... S V N Vishwanathan (Vishy) and Prateek Jain will offer a Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 15.
