Understanding 10 601 Machine Learning Spring 2015 Lecture 1
Let's dive into the details surrounding 10 601 Machine Learning Spring 2015 Lecture 1. Topics: high-level overview of
Key Takeaways about 10 601 Machine Learning Spring 2015 Lecture 1
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
- Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecturer: Tom Mitchell ...
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
- Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ...
- Topics: Bayes rule, joint probability, maximum likelihood estimation (MLE), maximum a posteriori (MAP) estimation Lecturer: Tom ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Lecture 1
Topics: support vector Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ... Okay um how many people are in the
Topics: bias-variance tradeoff, introduction to graphical models, conditional independence Lecturer: Tom Mitchell ...
That wraps up our extensive overview of 10 601 Machine Learning Spring 2015 Lecture 1.
