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: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
- Topics: review of boosting, Adaboost, strong vs weak PAC
- Topics: support vector
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 15
Topics: boosting, weak vs strong PAC Lecture 15 Topics: high-level overview of S V N Vishwanathan (Vishy) and Prateek Jain will offer a
Topics: exam review, review of past exam questions
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 15.