Exploring 10 601 Machine Learning Spring 2015 Lecture 2
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Lecture 2.
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: support vector
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 2
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Topics: boosting, weak vs strong PAC
Topics: high-level overview of
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