Machine Learning 05: Graphical Models
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Graphical Models
Key Ideas:
Conditional Independence as......
1252
Hubert Wang
Machine Learning 04: DNN, CNN, RNN, and Representation Learning
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Why not Logistic Regression?
We like logistic regressio......
1202
Machine Learning 03: Gaussian Naïve Bayes and Logistic Regression
Course Notes of Professor Tom Mitchell Machine Learning @ CMU
Naïve Bayes with Continuous X
In order to train a Naive Bayes classi......
1147
Machine Learning 02: MLE, MAP, and Naïve Bayes
Course Notes of Professor Tom Mitchell Machine Learning @ CMU
Probablistic learning
Probablistic function approximation:
inste......
1071
Machine Learning 01: Decision Tree, Entropy, and Overfitting
Course Notes of Professor Tom Mitchell Machine Learning @ CMU
Machine Learning is the study of algorithms that:
Improve their p......
1049
5th Annual Global Big Data Conference
Here are my notes after attending the conference.
Part of the presentation slides here.
The Analytics Of Things
Bill Franks, Chief Analytics Officer, International......
1089
Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation
Selected examples of object proposals on the PASCAL VOC 2007 test dataset. The l......
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