Course work for COMP 551 - Applied Machine Learning
Author: Antonios Valkanas
Statistical Decision Theory - Linear Regression - K-NN - Linear Classification - Indicator Regression - PCA - LDA - QDA - GDA - Naive Bayes - Logistic Regression - Perceptron - Separating Hyperplanes - SVM - Decision Trees - ensemble learning - bagging - boosting - stacking - Neural Networks - Backpropagation - Training Deep Neural Nets - Convnets - RNNs - MLE/MAP - Bayesian Learning - Density estimation - Bayesian Linear Regression - Kernel Methods - Gaussian Process - Clustering - K-means - DBScan - GMM - EM Algorithm - Frontiers in ML.
At least Python 3.5 and compatible Numpy, OpenCV, tensorflow, keras, sci-kit learn and their dependencies. A good way of getting all of these is Anaconda.
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