L1. State of the Art in Machine Learning

State of the Art in Machine Learning

Poul Petersen

BigML

2

What is ML?

“a field of study that gives computers the ability to learn without being explicitly

programmed”

Professor Arthur Samuel, 1959

•What “ability to learn” do computers have?

•What does “explicitly programmed” mean?

Square Feet

Price

Sacramento Real Estate Prices by sq_ft

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Ability to Learn

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Ability to Learn• Provide computer with examples of the relationship between

square footage X and price Y.

• The computer learns the equation of a line F() which fits the examples.

• The computer can now predict the price of any home knowing only the square footage: F( x ) = y

• This model is known as Linear Regression. There are other types of models.

• You may have noticed there was some points that did not fit. This is important!

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Learning Problems (fit)

Under-fitting Over-fitting

• Model does not fit well enough

• Does not capture the underlying trend of the data

• Change algorithm or features

• Model fits too well does not “generalize”

• Captures the noise or outliers of the data

• Change algorithm or filter outliers

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Learning Problems (missing)

• Missing values at training/prediction time

• Some algorithms can handle missing values, some no

• Missing data is sometimes important

• Replace missing values

• Predict missing values

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Learning Problems (missing)

Missing@ Decision Trees KNN Logistic

RegressionNaive Bayes

Neural Networks

Training Yes No No Yes Yes*

Prediction Yes No No Yes No

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Not Explicitly Programmed

Control System

“Customers go on vacation… We need a program that predicts if the light will come on when the control

system flips the switch.”

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Not Explicitly Programmed

@iLoveRuby

Switch Light?

on TRUE

off FALSE

• @iLoveRuby reasoned the rules by experience

• Then programmed the rules explicitly

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Not Explicitly ProgrammedSwitch Light?

on TRUE

off FALSE

• The ML System reasoned the rules from the data and created a Model

• Functionally the Model is the same as @iLoveRuby’s explicit model

@iLoveML

question

answer

ML System Model

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Not Explicitly Programmed

• how long has the bulb has been in service

• reliability of brand

• rated power: higher power shorter life

• duty cycle

• room conditions: temperature, humidity

Goal is to predict if the bulb will come onbut the switch is not the important variable:

Even worse: None of these conditions is absolute.

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Not Explicitly Programmedbrand power age duty temp humidity FAIL?koala 45 338 1 16 0.03 FALSEotter 15 140 1 27 0.27 FALSEkoala 15 315 1 19 0.37 TRUEotter 45 338 1 29 0.27 TRUEkoala 45 211 1 23 0.85 TRUEotter 15 328 1 17 0.56 FALSEkoala 15 318 2 22 0.45 TRUEkoala 15 273 1 27 0.18 FALSEkoala 45 102 1 21 0.48 FALSEkoala 15 110 2 15 0.99 TRUEotter 45 355 2 15 0.01 FALSEotter 15 69 1 24 0.70 FALSEkoala 15 69 1 24 0.70 FALSEkoala 15 337 2 27 0.83 TRUE

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Not Explicitly Programmed

@iLoveRuby

• Multi-dimensional data is much harder to find rules

• Explicit program requires modification

brand power age duty temp humidity FAIL?

koala 45 338 1 16 0.03 FALSE

otter 15 140 1 27 0.27 FALSE

koala 15 315 1 19 0.37 TRUE

otter 45 338 1 29 0.27 TRUE

koala 45 211 1 23 0.85 TRUE

otter 15 328 1 17 0.56 FALSE

koala 15 318 2 22 0.45 TRUE

koala 15 273 1 27 0.18 FALSE

koala 45 102 1 21 0.48 FALSE

koala 15 110 2 15 0.99 TRUE

otter 45 355 2 15 0.01 FALSE

otter 15 69 1 24 0.70 FALSE

koala 15 69 1 24 0.70 FALSE

koala 15 337 2 27 0.83 TRUE

@iLoveML ML System Model

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Not Explicitly Programmed

• ML System easily re-trains on new data

question

answer

brand power age duty temp humidity FAIL?

koala 45 338 1 16 0.03 FALSE

otter 15 140 1 27 0.27 FALSE

koala 15 315 1 19 0.37 TRUE

otter 45 338 1 29 0.27 TRUE

koala 45 211 1 23 0.85 TRUE

otter 15 328 1 17 0.56 FALSE

koala 15 318 2 22 0.45 TRUE

koala 15 273 1 27 0.18 FALSE

koala 45 102 1 21 0.48 FALSE

koala 15 110 2 15 0.99 TRUE

otter 45 355 2 15 0.01 FALSE

otter 15 69 1 24 0.70 FALSE

koala 15 69 1 24 0.70 FALSE

koala 15 337 2 27 0.83 TRUE

@iLoveML ML System Model

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Terminology

input

prediction

brand power age duty temp humidity FAIL?

koala 45 338 1 16 0.03 FALSE

otter 15 140 1 27 0.27 FALSE

koala 15 315 1 19 0.37 TRUE

otter 45 338 1 29 0.27 TRUE

koala 45 211 1 23 0.85 TRUE

otter 15 328 1 17 0.56 FALSE

koala 15 318 2 22 0.45 TRUE

koala 15 273 1 27 0.18 FALSE

koala 45 102 1 21 0.48 FALSE

koala 15 110 2 15 0.99 TRUE

otter 45 355 2 15 0.01 FALSE

otter 15 69 1 24 0.70 FALSE

koala 15 69 1 24 0.70 FALSE

koala 15 337 2 27 0.83 TRUE

datasource

training

“putting intoproduction”

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Supervised Learning

brand power age duty temp humidity FAIL?

koala 45 338 1 16 0.03 FALSE

otter 15 140 1 27 0.27 FALSE

koala 15 315 1 19 0.37 TRUE

otter 45 338 1 29 0.27 TRUE

koala 45 211 1 23 0.85 TRUE

otter 15 328 1 17 0.56 FALSE

koala 15 318 2 22 0.45 TRUE

features

instances

label

Labeled Data

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Supervised Learning

animal state … proximity actiontiger hungry … close run

elephant happy … far take picture

Classification

animal state … proximity min_kmhtiger hungry … close 70hippo angry … far 10

Regression

label

animal state … proximity action1 action2tiger hungry … close run look untasty

elephant happy … far take picture call friends

Multi-Label Classification

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Unsupervised Learning

date customer account auth class zip amount

Mon Bob 3421 pin clothes 46140 135

Tue Bob 3421 sign food 46140 401

Tue Alice 2456 pin food 12222 234

Wed Sally 6788 pin gas 26339 94

Wed Bob 3421 pin tech 21350 2459

Wed Bob 3421 pin gas 46140 83

The Sally 6788 sign food 26339 51

features

instances

Unlabeled Data

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Unsupervised Learning

date customer account auth class zip amountMon Bob 3421 pin clothes 46140 135Tue Bob 3421 sign food 46140 401Tue Alice 2456 pin food 12222 234Wed Sally 6788 pin gas 26339 94Wed Bob 3421 pin tech 21350 2459Wed Bob 3421 pin gas 46140 83The Sally 6788 sign food 26339 51

Clustering

date customer account auth class zip amountMon Bob 3421 pin clothes 46140 135Tue Bob 3421 sign food 46140 401Tue Alice 2456 pin food 12222 234Wed Sally 6788 pin gas 26339 94Wed Bob 3421 pin tech 21350 2459Wed Bob 3421 pin gas 46140 83The Sally 6788 sign food 26339 51

Anomaly Detection

similar

unusual

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Semi-Supervised Learning

brand power age duty temp humidity FAIL?

koala 45 338 1 16 0.03 FALSE

otter 15 140 1 27 0.27

koala 15 315 1 19 0.37

otter 45 338 1 29 0.27 TRUE

koala 45 211 1 23 0.85 TRUE

otter 15 328 1 17 0.56

koala 15 318 2 22 0.45 TRUE

label

Labeled and Unlabeled Data

} infer

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Data Types

numeric

1 2 3

1, 2.0, 3, -5.4 categoricaltrue, yes, red, mammal categoricalcategorical

A B C

DATE-TIME2013-09-25 10:02

DATE-TIME

YEAR

MONTH

DAY-OF-MONTH

YYYY-MM-DD

DAY-OF-WEEK

HOUR

MINUTE

YYYY-MM-DD

YYYY-MM-DD

M-T-W-T-F-S-D

HH:MM:SS

HH:MM:SS

2013

September

25

Wednesday

10

02

textBe not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon 'em.

text

“great”“afraid”“born”“some”

appears 2 timesappears 1 timeappears 1 timeappears 2 times

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Text Analysis

Be not afraid of greatness: some are born great, some achieve greatness, and some have greatnessthrust upon 'em.

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Text Analysis

Be not afraid of greatness: some are born great, some achieve greatness, and some have greatnessthrust upon 'em.

great: appears 4 times

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Text Analysisgreat afraid born achieve

4 1 1 1

… … … …

Be not afraid of greatness: some are born great, some achieve greatness, and some have greatnessthrust upon ‘em.

Model

The token “great” does not occur

The token “afraid” occurs more than once

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Topic Modeling

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Brief History of ML

1950 1960 1970 1980 1990 2000 2010

PerceptronNeural

Networks

Ensembles

Support Vector Machines

Boosting

Inte

rpre

tabi

lity

Rosenblatt, 1957

Quinlan, 1979 (ID3),

Minsky, 1969

Vapnik, 1963 Corina & Vapnik, 1995

Schapire, 1989 (Boosting) Schapire, 1995 (Adaboost)

Breiman, 2001 (Random Forests)Breiman, 1994 (Bagging)

Deep LearningHinton, 2006Fukushima, 1989 (ANN)

Breiman, 1984 (CART)

2020

+

-

Decision Trees

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Why ML Now?

• Decreasing cost of data

• Abundant computing power, especially cloud

• Machine Learning APIs

• Abundance of APIs + internet to combine easily

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Composability

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The Stages of a ML App

State the problem

Data Wrangling

Feature EngineeringLearning

Deploying

Predicting

Measuring Impact