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: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Topics: support vector
- Topics: exam review, review of past exam questions
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: 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: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
In summary, understanding 10 601 Machine Learning Spring 2015 Lecture 2 gives us a better perspective.
