Machine Learning for Everyone
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Machine Learning for Everyone
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Transcript of Machine Learning for Everyone
Machine Learning for Everyone
Agenda
Goal: Encourage you to use Machine Learning… today!
‣ About me
‣ Machine Learning Misconceptions Concepts Problems and Algorithms
‣ For Everyone!
About meElectronics Engineering, Software Development, Data Science… Why not?
Neural TB
Tool that aids in the diagnosis of Tuberculosis using Neural Networks
Neural Ringer
Algorithm for online electron/jet discrimination for the ATLAS detector at CERN using Neural Networks
djBrazil
Intelligent music platform specialized in Brazilian music
Jigsaw Dots
Interactive exploratory visualization of employees based on their skills
Higgs Challenge
Machine LearningIt is all about learning
Misconceptions
Too difficult
Big upfront investments
Needs supercomputers
Only for PhDs from MIT
Data Silo
Takes too long to pay off
! " # $ % &
Reality
♥
Feature Extraction
( Item { Feature 1
Feature 2 Feature 3
… Feature N
Feat
ure
2
0
8
16
24
32
40
Feature 10 10 20 30 40 50 60 70 80 90
Supervised Learning
( ( ( ( ( ( ( ( ( ( ( (
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19)
3
2
…
5
( 20, 39, 59, 68 3♥
Items Features Labels Algorithm
New Item PredictionFeatures Model
Supervised Learning AlgorithmsK-Nearest Neighbors Neural Networks
Decision Tree Random Forest
Regression
( ( ( ( ( ( ( ( ( ( ( (
(921 37
23 2487 1541
21 2121 21
?
Boston housing prices
Prediction of house prices at Boston suburbs based on census data using Linear Regression
Classification
( ( ( ( ( ( ( ( ( ( ( (
(BA B
A AB AB
A AB B
?
Detecting particles
Online electron detection based on more than 1500 detector cells using Neural Networks
(GeV)TE0 10 20 30 40 50 60 70 80
o (%
)a~
Prob
. de
Rej
eic
20
30
40
50
60
70
80
90
100
RingerT2Calo
smicosoC
η-2 -1 0 1 2
o (%
)a~
Prob
. de
Rej
eic
80
85
90
95
100
RingerT2Calo
φ-3 -2 -1 0 1 2 3
o (%
)a~
Prob
. de
Rej
eic
90
92
94
96
98
100
RingerT2Calo
Classifying products
Product classification on 9 different classes based on 90 numerical features using Amazon Machine Learning
Diagnosing Tuberculosis
Tuberculosis diagnosis based on patients questionnaires using Neural Networks
Unsupervised Learning
( ( ( ( ( ( ( ( ( ( ( (
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19)
( 20, 39, 59, 68 ♥
Items Features Algorithm
New ItemBetter
RepresentationFeatures Model ( ( ( ( ( (
( ( ( (
( ( (
Unsupervised Learning Algorithms
K-Means Self-Organising Maps t-SNE
( ( ( ( ( (
Clustering
( ( ( ( ( ( ( ( ( ( ( (
( (
( ( ( (
Clustering crime
Crime clusters based on information from the San Francisco Police Department Crime Incident Reporting system using K-means
Dimensionality Reduction
( ( ( ( ( ( ( ( ( ( ( (
Visualizing employees
Visualization of 2000+ employees described by 200+ skills after reducing dimensionality using the t-SNE algorithm
For everyone!You can do it!
Massive Online Open Courses
Open Source Tools
Pay-as-you-go
Amazon Machine Learning
Google Cloud Machine Learning
Azure Machine Learning
Cheat sheets
Kaggle
Sponsors
Workflow Reasons
Code Snippet
>>> classifier = RandomForestClassifier().fit(features, labels) >>> prediction = classifier.predict(new_features)
( ( ( ( ( ( ( ( ( ( ( (
23, 45, 67, 78
12, 48, 68, 22
…
34, 58, 77, 19)
A
B
…
B
( 20, 39, 59, 68 A♥
Items Features Labels Algorithm
New Item PredictionFeatures Model
