Slides from my talk at PHP Benelux 2018. Abstract: From chatbots to your home thermostat, it seems like machine learning algorithms are everywhere nowadays. How about understanding how this works now? In this talk, you will learn about the basics of machine learning through various basic examples, without the need for a PhD or deep knowledge of assembly. At the end of this talk, you will know what the Naive Bayes classifiers, sentiment analysis and basic genetic algorithms are and how they work. You will also see how to create your own implementations in Javascript.

1. Learning Machine
Learning
A little intro to a (not that complex) world

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About Me
@joel__lord
joellord

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Our Agenda for today…
• AI vs ML
• Deep Learning &
Neural Networks
• Supervised vs
unsupervised
• Naïve Bayes Classifier
• Genetic Algorithms

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Artificial Intelligence
Artificial intelligence (AI)
is intelligence exhibited by machines.
In computer science, the field of AI
research defines itself as the study of
"intelligent agents": any device that
perceives its environment and takes actions
that maximize its chance of success at
some goal.

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Artificial Intelligence
“takes actions that
maximize its chance of
success at some goal”

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Examples in real life

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Machine Learning
Machine learning (ML) is the subfield
of computer science that gives "computers
the ability to learn without being explicitly
programmed."

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“Don’t be afraid of artificial
intelligence, be afraid of humanity.”

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Deep Learning
& Big Data
• Explosion of digital data
• Can’t be processed with
traditional methods
anymore

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Neural
Networks
• Breaking big
problems in small
layers

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Supervised
Learning
• Requires feedback
• Starts with nothing
and increases its
understanding
• Useless if the data
is of bad quality
• Use cases:
• Classification

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Unsupervised
Learning
• There is no feedback
• Good in the case of no right or
wrong answer
• Helps to identify patterns or data
structures
• Use case:
• You might also be interested
in…
• Grouping customers by
purchasing behaviors

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The Naïve Bayes Classifier

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Bayes Theorem

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Bayes Theorem
where

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Bayes Theorem
•

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Bayes Theorem
•

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Naive Bayes
Classifier
• Let’s look at a concrete
example.
• You never know what
you’re gonna get

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Probability that a chocolate has nuts
Nuts No Nuts
Round 25% 75%
Square 75% 25%
Dark 10% 90%
Light 90% 10%

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1
0.225 0.075

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Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 10% 90% - -
Light 90% 10% 0.9 0.1
0.225 0.075

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Naïve Bayes Classifier in code
var Classifier = function() {
this.dictionaries = {};
};
Classifier.prototype.classify = function(text, group) {
};
Classifier.prototype.categorize = function(text) {
};

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Sentiment
Analysis
• Not Machine
Learning
• Uses classifiers and
AFINN-165 (and
emojis)

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Sentiment
Analysis
• Javascript:
• npm install
sentiment
• PHP:
• composer require
risan/sentiment-
analysis

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Genetic
Algorithm
• Awesome shit!

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Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep a few random
individuals
• Introduce random
mutations
• Randomly create ”children”
• Magically end up with a
valid solution

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Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep a few random
individuals
• Introduce random
mutations
• Randomly create ”children”
• Magically end up with a
valid solution

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Genetic Algorithm
Credit: AutoDesk https://autodeskresearch.com/projects/
Dreamcatcher

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https://www.youtube.com/watch?v=pgaEE27nsQw

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Genetic Algorithm in code
//Declare Consts
function randomInt(min, max) {…}
function random(min, max) {…}
function fitness(individual) {…}
function sortByFitness(population) {…}
function randomIndividual() {…}
function randomPopulation(size) {…}
function mutate(population) {…}
function reproduce(father, mother) {…}
function evolve(population) {…}
function findSolution() {
var population = randomPopulation(POP_SIZE);
var generation = 0;
while (fitness(population[0]) > CLOSE_ENOUGH) {
generation++;
population = evolve(population);
}
return {solution: population[0], generations: generation};
}
var sol = findSolution();

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What did we learn?
• Machine Learning and Artificial Intelligence
• Big Data and Deep Learning
• Supervised vs unsupervised
• Basic Algorithms
• Naïve Bayes Classifier
• Sentiment Analysis
• Genetic Algorithm
• Hopefully, you don’t feel intimidated by ML anymore

40. Presented By
JOEL LORD
PHP Benelux, January 26th 2018
@joel__lord
joellord
Thank you!

41. Presented By
JOEL LORD
PHP Benelux, January 26th 2018
@joel__lord
joellord
Questions?

42. Impact of parameters on Genetic Algorithms