Exploring 10 601 Machine Learning Spring 2015 Lecture 26
Exploring 10 601 Machine Learning Spring 2015 Lecture 26 reveals several interesting facts.
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP)
- Topics: support vector
- Topics: linear regression, logistic regression, gradient descent
- Topics: never-ending
- Topics: neural networks, backpropagation, deep
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 26
Topics: deep learning, restricted Boltzmann machines, privacy in Topics: high-level overview of Topics: reinforcement Topics: Logistic regression and its relation to naive Bayes, gradient descent
Topics: decision trees, overfitting, probability theory Lecturers: Tom Mitchell and Maria-Florina Balcan ...
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