1 / 71

# Lecture 2: Math Primer - PowerPoint PPT Presentation

Lecture 2: Math Primer. Machine Learning CUNY Graduate Center. Today. Probability and Statistics Naïve Bayes Classification Linear Algebra Matrix Multiplication Matrix Inversion Calculus Vector Calculus Optimization Lagrange Multipliers. Classical Artificial Intelligence.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Lecture 2: Math Primer' - keala

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Machine Learning

• Probability and Statistics

• Naïve Bayes Classification

• Linear Algebra

• Matrix Multiplication

• Matrix Inversion

• Calculus

• Vector Calculus

• Optimization

• Lagrange Multipliers

Expert Systems

Theorem Provers

Shakey

Chess

Largely characterized by determinism.

Fingerprint ID

Internet Search

Vision – facial ID, object recognition

Speech Recognition

Asimo

Jeopardy!

Statistical modeling to generalize from data.

Black Swans

The Long Tail

• In the 18th Century, black swans were discovered in Western Australia

• Black Swans are rare, sometimes unpredictable events, that have extreme impact

• Almost all statistical models underestimate the likelihood of unseen events.

In the 17th Century, all known swans were white.

Based on evidence, it is impossible for a swan to be anything other than white.

• Many events follow an exponential distribution

• These distributions have a very long “tail”.

• I.e. A large region with significant probability mass, but low likelihood at any particular point.

• Often, interesting events occur in the Long Tail, but it is difficult to accurately model behavior in this region.

2 Boxes, one red and one blue.

Each contain colored balls.

Suppose we randomly select a box, then randomly draw a ball from that box.

The identity of the Box is a random variable, B.

The identity of the ball is a random variable, L.

B can take 2 values, r, or b

L can take 2 values, g or o.

Given some information about B and L, we want to ask questions about the likelihood of different events.

What is the probability of selecting an apple?

If I chose an orange ball, what is the probability that I chose from the blue box?

• The probability (or likelihood) of an event is the fraction of times that the event occurs out of n trials, as n approaches infinity.

• Probabilities lie in the range [0,1]

• Mutually exclusive events are events that cannot simultaneously occur.

• The sum of the likelihoods of all mutually exclusive events must equal 1.

• If two events are independent then,

p(X, Y) = p(X)p(Y)

p(X|Y) = p(X)

A Joint Probability function defines the likelihood of two (or more) events occurring.

Let nij be the number of times event i and event j simultaneously occur.

Consider the probability of X irrespective of Y.

The number of instances in column j is the sum of instances in each cell

Therefore, we can marginalize or “sum over” Y:

• Consider only instances where X = xj.

• The fraction of these instances where Y = yi is the conditional probability

• “The probability of y given x”

Sum Rule

Product Rule

In general, we’ll refer to a distribution over a random variable as p(X) and a distribution evaluated at a particular value as p(x).

Posterior

Prior

Likelihood

Prior: Information we have before observation.

Posterior: The distribution of Y after observing X

Likelihood: The likelihood of observing X given Y

• Assuming I’m inherently more likely to select the red box (66.6%) than the blue box (33.3%).

• If I selected an orange ball, what is the likelihood that I selected the red box?

• The blue box?

This is a simple case of a simple classification approach.

Here the Box is the class, and the colored ball is a feature, or the observation.

We can extend this Bayesian classification approach to incorporate more independent features.

Some theory first.

Assuming independent features simplifies the math.

Prior:

So far, X has been discrete where it can take one of M values.

What if X is continuous?

Now p(x) is a continuous probability density function.

The probability that x will lie in an interval (a,b) is:

Sum Rule

Product Rule

Given a random variable, with a distribution p(X), what is the expected value of X?

If a variable, x, can take 1-of-K states, we represent the distribution of this variable as a multinomial distribution.

The probability of x being in state k is μk

The expected value is the mean values.

One Dimension

D-Dimensions

• Expectation

• The expected value of a function is the hypothesis

• Variance

• The variance is the confidence in that hypothesis

The variance of a random variable describes how much variability around the expected value there is.

Calculated as the expected squared error.

The covariance of two random variables expresses how they vary together.

If two variables are independent, their covariance equals zero.

• Vectors

• A one dimensional array.

• If not specified, assume x is a column vector.

• Matrices

• Higher dimensional array.

• Typically denoted with capital letters.

• n rows by m columns

Transposing a matrix swaps columns and rows.

Transposing a matrix swaps columns and rows.

• Matrices can be added to themselves iff they have the same dimensions.

• A and B are both n-by-m matrices.

• To multiply two matrices, the inner dimensions must be the same.

• An n-by-m matrix can be multiplied by an m-by-k matrix

• The inverse of an n-by-n or squarematrix A is denoted A-1, and has the following property.

• Where I is the identity matrix is an n-by-n matrix with ones along the diagonal.

• Iij = 1 iffi = j, 0 otherwise

Matrices are invariant under multiplication by the identity matrix.

The norm of a vector,x, represents the euclidean length of a vector.

• Scalar

• Vector

• Positive Definite matrix M

• Positive Semi-definite

Derivatives and Integrals

Optimization

A derivative of a function defines the slope at a point x.

Integration is the inverse operation of derivation (plus a constant)

Graphically, an integral can be considered the area under the curve defined by f(x)

Derivation with respect to a matrix or vector

Change of Variables with a Vector

Derivative w.r.t. a vector

Given a vector x, and a function f(x), how can we find f’(x)?

Derivative w.r.t. a vector

Given a vector x, and a function f(x), how can we find f’(x)?

Also referred to as the gradient of a function.

Scalar Multiplication

Product Rule

Derivative of an inverse

Change of Variable

Have an objective function that we’d like to maximize or minimize, f(x)

Set the first derivative to zero.

• What if I want to constrain the parameters of the model.

• The mean is less than 10

• Find the best likelihood, subject to a constraint.

• Two functions:

• An objective function to maximize

• An inequality that must be satisfied

Find maxima of f(x,y) subject to a constraint.

Maximizing:

Subject to:

Introduce a new variable, and find a maxima.

Maximizing:

Subject to:

Introduce a new variable, and find a maxima.

Now have 3 equations with 3 unknowns.

Eliminate Lambda

Substitute and Solve

• Calculus

• We need to identify the maximum likelihood, or minimum risk. Optimization

• Integration allows the marginalization of continuous probability density functions

• Linear Algebra

• Many features leads to high dimensional spaces

• Vectors and matrices allow us to compactly describe and manipulate high dimension al feature spaces.

• Vector Calculus

• All of the optimization needs to be performed in high dimensional spaces

• Optimization of multiple variables simultaneously – Gradient Descent

• Want to take a marginal over high dimensional distributions like Gaussians.

Linear Regression and Regularization