Machine Learning CMPT 726 Simon Fraser University

Machine LearningCMPT 726Simon Fraser University

CHAPTER 1: INTRODUCTION

Outline

• Comments on general approach.• Probability Theory.

• Joint, conditional and marginal probabilities.• Random Variables.• Functions of R.V.s

• Bernoulli Distribution (Coin Tosses).• Maximum Likelihood Estimation.• Bayesian Learning With Conjugate Prior.

• The Gaussian Distribution.• Maximum Likelihood Estimation.• Bayesian Learning With Conjugate Prior.

• More Probability Theory.• Entropy.• KL Divergence.

Our Approach

• The course generally follows statistics, very interdisciplinary.• Emphasis on predictive models: guess the value(s) of target variable(s). “Pattern Recognition”• Generally a Bayesian approach as in the text.• Compared to standard Bayesian statistics:

• more complex models (neural nets, Bayes nets)• more discrete variables• more emphasis on algorithms and efficiency

Things Not Covered

• Within statistics:• Hypothesis testing• Frequentist theory, learning theory.

• Other types of data (not random samples)• Relational data• Scientific data (automated scientific discovery)• Action + learning = reinforcement learning.

Could be optional – what do you think?

Probability Theory

Apples and Oranges

Probability Theory

Marginal Probability

Conditional ProbabilityJoint Probability

Probability Theory

Sum Rule

Product Rule

The Rules of Probability

Sum Rule

Product Rule

Bayes’ Theorem

posterior likelihood × prior

Bayes’ Theorem: Model Version

• Let M be model, E be evidence.

•P(M|E) proportional to P(M) x P(E|M)

Intuition• prior = how plausible is the event (model, theory) a priori before seeing any evidence.• likelihood = how well does the model explain the data?

Probability Densities

Transformed Densities

Expectations

Conditional Expectation(discrete)

Approximate Expectation(discrete and continuous)

Variances and Covariances

The Gaussian Distribution

Gaussian Mean and Variance

The Multivariate Gaussian

Reading exponential prob formulas

• In infinite space, cannot just form sumΣx p(x) grows to infinity.

• Instead, use exponential, e.g.p(n) = (1/2)n

• Suppose there is a relevant feature f(x) and I want to express that “the greater f(x) is, the less probable x is”.

• Use p(x) = exp(-f(x)).

Example: exponential form sample size

• Fair Coin: The longer the sample size, the less likely it is.

• p(n) = 2-n.

ln[p(n)]

Sample size n

Exponential Form: Gaussian mean

• The further x is from the mean, the less likely it is.

ln[p(x)]

2(x-μ)

Smaller variance decreases probability

• The smaller the variance σ2, the less likely x is (away from the mean).

ln[p(x)]

-σ2

Minimal energy = max probability

• The greater the energy (of the joint state), the less probable the state is.

ln[p(x)]

E(x)

Gaussian Parameter Estimation

Likelihood function

Maximum (Log) Likelihood

Properties of and

Curve Fitting Re-visited

Maximum Likelihood

Determine by minimizing sum-of-squares error, .

Predictive Distribution

Frequentism vs. Bayesianism

• Frequentists: probabilities are measured as the frequencies of repeatable events.• E.g., coin flips, snow falls in January.

• Bayesian: in addition, allow probabilities to be attached to parameter values (e.g., P(μ=0).

• Frequentist model selection: give performance guarantees (e.g., 95% of the time the method is right).

• Bayesian model selection: choose prior distribution over parameters, maximize resulting cost function (posterior).

MAP: A Step towards Bayes

Determine by minimizing regularized sum-of-squares error, .

Bayesian Curve Fitting

Bayesian Predictive Distribution

Model Selection

Cross-Validation

Curse of Dimensionality

Rule of Thumb: 10 datapoints per parameter.

Curse of Dimensionality

Polynomial curve fitting, M = 3

Gaussian Densities in higher dimensions

Decision Theory

Inference stepDetermine either or .

Decision stepFor given x, determine optimal t.

Minimum Misclassification Rate

Minimum Expected Loss

Example: classify medical images as ‘cancer’ or ‘normal’

DecisionTr

uth

Minimum Expected Loss

Regions are chosen to minimize

Why Separate Inference and Decision?

• Minimizing risk (loss matrix may change over time)• Unbalanced class priors• Combining models

Decision Theory for Regression

Inference stepDetermine .

Decision stepFor given x, make optimal prediction, y(x), for t.

Loss function:

The Squared Loss Function

Generative vs Discriminative

Generative approach: ModelUse Bayes’ theorem

Discriminative approach: Model directly

Entropy

Important quantity in• coding theory• statistical physics• machine learning

Entropy

Entropy

Coding theory: x discrete with 8 possible states; how many bits to transmit the state of x?

All states equally likely

Entropy

The Maximum Entropy Principle

• Commonly used principle for model selection: maximize entropy.

• Example: In how many ways can N identical objects be allocated M bins?

Entropy maximized when

Differential Entropy and the Gaussian

Put bins of width ¢ along the real line

Differential entropy maximized (for fixed ) when

in which case

Conditional Entropy

The Kullback-Leibler Divergence

Mutual Information