[SOLVED] CS algorithm LECTURE 4 TERM 2:

LECTURE 4 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE

MACHINE LEARNING JARGON
Model
Interpolating / Extrapolating Data Bias
Noise / Outliers
Learning algorithm
Inference algorithm
Supervised learning
Unsupervised learning
Classification
Regression
Clustering
Decomposition
Parameters
Optimisation
Training data
Testing data
Error metric
Linear model
Parametric model
Model variance
Model bias
Model generalization
Overfitting
Goodness-of-fit
Hyper-parameters
Failure modes
Confusion matrix
Data density
Partition
Hidden parameter
Feature space
High dimensional space
Low dimensional space
Separable data
Manifold / Decision surface
Hyper cube / volume / plane

D. DECOMPOSITION PROJECTION METHODS
Dimensionality reduction

D. DECOMPOSITION KERNEL METHODS

D. DECOMPOSITION MANIFOLD LEARNING

KERNEL METHODS
SupportVector Machines

DECISION BOUNDARIES

A. CLASSIFICATION CATEGORICAL VARIABLE

LARGE MARGIN

FEATURE SCALING

HARD MARGIN

MARGIN VIOLATIONS

HIGHER DIMENSIONS (FEATURES)

LINEAR SVM

POLYNOMIAL KERNEL

SIMILARITY FEATURES

RBF KERNEL

SVM REGRESSION

SVM POLYNOMIAL REGRESSION

DECISION FUNCTION

OP TIMIZ ATION
Kernel Trick
Optimization
Quadratic programming The Dual problem

LECTURE 4 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE