Prerequistes Flashcards
A set of quantities or properties that describe an observation
Features
Usually paired with a set of features for use in supervised learning. Can be discrete or continuous
Labels
Pairs of features and labels
Examples
The number of features associated with a particular example
Dimensions
A feature vector which is list of features representing a particular example
Vector
An array of values usually consisting of multiple rows and columns
Matrix
An operator that flips a matrix over its diagonal
Matrix Transpose
A function with more than one variable/coefficient pair
Polynomial
Indicates how much the output of a function will change with respect to a change in its input
Derivative
How likely something is to occur can be independent like the roll of a die
Probability
A function that takes in an outcome and outputs the probability of that particular outcome occurring
Probability Distribution
A very common type of probability distribution which fits many real world observations; also called a normal distribution
Gaussian Distribution
A probability distribution in which each outcome is equally likely; rolling a normal six-sided for
Uniform Distribution
Models take in ???
Features
Cite the three types of observations?
Continuous
Categorical
Ordinal
In supervised learning Features are paired with?
Labels (indicator)
True or False
In deep learning data can be represented as images
True
True or false
Deep Learning also uses Labels
True
True or False
In deep learning feedback is provided during training
True
True or False
Unsupervised learning is data without labels
True
Clustered data is considered unsupervised learning
True
Unsupervised learning doesn’t require the use of labels
False
In unsupervised learning clustered data is categorized by?
Similarity
