Understanding 10 601 Machine Learning Spring 2015 Lecture 17
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 17. Topics: kernel methods, margin, kernelizing a
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 17
- Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
- Neural Networks 2: Backpropagation
- Topics: Logistic regression and its relation to naive Bayes, gradient descent
- Topics: support vector
- Topics: EM algorithm, Gaussian mixture models, Chow-Liu algorithm
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 17
Topics: additional practice Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ... Topics: high-level overview of
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 17.