Machine Learning
Machine learning ✔✔form of artificial intelligence that does not require you to explicitly
program, learning from patterns in data
Supervised learning ✔✔type of machine learning in which the response variable is
known
Unsupervised learning ✔✔type of machine learning in which the response variable is
unknown
R ✔✔a mathematics based programming language that is often used for machine learning
WEKA ✔✔a graphical program (or a visual programming language)
High-dimensionality ✔✔a high-dimensionality problem is a machine-learning problem in
which the number of dimensions or features is much more than the number of cases
Clustering ✔✔part of unsupervised learning that in which similar cases are grouped
together
Classification ✔✔the process of using machine learning to identify different cases based
on patterns found in data (example: classifying tumors as malignant or benign;
classifying emails as spam or not spam)
SVM ✔✔(Support Vector Machine) a machine learning algorithm that computes a
hyperplane in order to separate different classes of data points (example: a SVM could be
used to compute a hyperplane that separates data points that represent cancer and not
cancer)
OVA ✔✔(One vs. All) special types of SVMs that are used in multi-class problems -
builds one SVM that compares each class to the rest of the classes (example: in a problem
regarding the diagnosis of 14 different cancers, 14 SVMs would be built such as breast
cancer vs. everything else, prostate cancer vs. everything else, etc.)
Decision tree ✔✔a machine learning algorithm that makes a map with generic
characteristics that can be used to determine what class a specific case falls into
