Understanding 10 601 Machine Learning Spring 2015 Lecture 8
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 8. Topics: introduction to computational
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 8
- Lecture 8
- Topics: graphical models, d-separation, Bayes' ball algorithm, inference
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: linear regression, logistic regression, gradient descent
- Topics: generative and discriminative classifiers (relationship between naive Bayes and logistic regression), linear regression ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 8
Topics: review of the solutions to midterm exam Topics: shattered sets, Vapnik–Chervonenkis (VC) dimension Topics: high-level overview of
Topics:
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 8.