Understanding 10 701 Machine Learning Fall 2014 Lecture 4
Let's dive into the details surrounding 10 701 Machine Learning Fall 2014 Lecture 4. Topics: logistic regression, generative vs discriminative classifiers, analysis of perceptron algorithm Lecturers: Aarti Singh and ...
Key Takeaways about 10 701 Machine Learning Fall 2014 Lecture 4
- Topics: analysis of boosting, introduction to graphical models Lecturers: Aarti Singh and Geoff ...
- Topics: plate notation in graphical models, introduction to
- Topics: principal component analysis (PCA), deep
- Topics: course logistics, high-level overview of
- Topics: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization (EM)
Detailed Analysis of 10 701 Machine Learning Fall 2014 Lecture 4
Topics: support vector Introduction to Topics: clustering, hierarchical clustering methods, k-means, mixture of Gaussians
Topics: perceptron, linear programming, "perceptron algorithm"
That wraps up our extensive overview of 10 701 Machine Learning Fall 2014 Lecture 4.