Understanding 10 601 Machine Learning Spring 2015 Recitation 13
Welcome to our comprehensive guide on 10 601 Machine Learning Spring 2015 Recitation 13. Topics: neural networks, neural net design/architectures, derivation of backpropagation Lecturer: Abu Saparov ...
Key Takeaways about 10 601 Machine Learning Spring 2015 Recitation 13
- Topics:
- Topics: Octave tutorial, Gaussian/normal distribution, maximum likelihood estimation (MLE), maximum a posteriori (MAP) Lecturer: ...
- Topics: review of naive Bayes, naive Bayes with Bernoulli, Gaussian, and multinomial (categorical) distributions Lecturer: Micol ...
- Topics: review of the solutions to midterm exam Lecturer: Travis Dick http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html.
- Topics: principal component analysis (PCA), dimensionality reduction, kernel PCA Lecturer: Ahmed Hefny ...
Detailed Analysis of 10 601 Machine Learning Spring 2015 Recitation 13
Topics: inference in graphical models, expectation maximization (EM) Lecturer: Tom Mitchell ... Topics: exam review, review of past exam questions Lecturer: Willie Neiswanger ... Topics: support vector
Topics: graph-based semi-supervised
In summary, understanding 10 601 Machine Learning Spring 2015 Recitation 13 gives us a better perspective.
