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COMP312P - Machine learning (learning methods (supervised learning…
COMP312P - Machine learning
learning methods
supervised learning
decision trees
classification & regression tree (ID3)
random forest
entropy, information gain, gini impurity
Support vector machine
multi-class SVM
one-vs-one
one-vs-all
SVM regression
SVM classification
Lagrangian optimisation
replace inner products w kernel fn
logistic regression (discrete)
ensemble methods
bagging
boosting (AdaBoost)
linear regression
Naive Bayes classifier
discrete Naive Bayes
Gaussian Naive Bayes (probabilistic logistic regression)
unsupervised learning
clustering
k-mean algo
EM steps
self-organising maps
Gaussian mixture model
dimensionality reduction
feature selection
feature transformation
non-linear dimensionality reduction - manifold learning (LLE, Isomap)
linear dimensionality reduction - subspace learning (PCA)
vector quantisation
matrix factorisation
density estimation
model types
parametric
linear regression
neural network
non-parametric
Gaussian processes
density kernel functions
multivariate
univariate
k-nearest neighbour
Pre-processing
feature selection
feature transformation
non-linear dimensionality reduction - manifold learning (LLE, Isomap)
linear dimensionality reduction - subspace learning (PCA)