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