Machine Learning 09: Learning Representations and Unify Machine Learning
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Principle Components Analysis (PCA)
Idea:
Given data p......
194
Hubert Wang
Machine Learning 08: Reinforcement, Semi-supervised, and Ensemble
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Reinforcement Learning: AlphaGo
This is a Guest lectur......
199
Machine Learning 07: Kernel Regression and SVM
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Regression
Wish to learn f: X—>Y, where Y is real, g......
201
Machine Learning 06: PAC Theory
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Computational Learning Theory
What general laws constrai......
195
Machine Learning 05: Graphical Models
Course Notes of Professor Tom Mitchell Machine Learning Course @ CMU, 2017
Graphical Models
Key Ideas:
Conditional Independence as......
175
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......
177
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......
172
Machine Learning 02: MLE, MAP, and Naïve Bayes
Course Notes of Professor Tom Mitchell Machine Learning @ CMU
Probablistic learning
Probablistic function approximation:
inste......
178
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......
182