Exploring 10 601 Machine Learning Spring 2015 Lecture 2
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Lecture 2.
- Topics: boosting, weak vs strong PAC
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
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: support vector Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation
Topics: clustering, k-means, k-means++, hierarchical clustering
In summary, understanding 10 601 Machine Learning Spring 2015 Lecture 2 gives us a better perspective.