AI and Predictive Analytics in Data-Center Environments - - PowerPoint PPT Presentation

AI and Predictive Analytics in Data-Center Environments

Introduction to Machine Learning

Josep Ll. Berral @BSC

Intel Academic Education Mindshare Initiative for AI

Introduction

“Let the machine to automate the analysis for you”

Introduction

• Machine Learning:

1. Algorithms and methods… 2. …to automatically learn/model a system… 3. …from some observations

Machine Learning

Collected Data

“a-posteriori” labelling Yep! It’s Versicolor

Labelled Data

Obtaining Data and Pre-processing Samples

Example of “Supervised Learning”

Machine Learning

Collected Data

“a-posteriori” labelling Yep! It’s Versicolor

Labelled Data Labelled Data

ML method Model for Labelling Obtaining Data and Pre-processing Training a Model Samples

Example of “Supervised Learning”

Machine Learning

Collected Data

“a-posteriori” labelling Yep! It’s Versicolor

Labelled Data Labelled Data

ML method Model for Labelling Model for Labelling “It’s Versicolor” Obtaining Data and Pre-processing Training a Model Infering New Data New sample Samples

Example of “Supervised Learning”

Prediction

Learning Example

Let’s see that with Real Data

Learning Example

• “Recognizing iris flowers”
• Iris setosa? Iris versicolor? Iris virginica?

Learning Example

• The “Iris Data-Set”
• People measured and classified the flowers
• 1. x1: sepal length in cm
• 2. x2: sepal width in cm
• 3. x3: petal length in cm
• 4. x4: petal width in cm
• 5. class: [Iris Setosa, Iris Versicolor, Iris Virginica]

*R.A. Fisher (1936). Source: https://archive.ics.uci.edu/ml/datasets/Iris

Learning Example

• We have labeled samples:

sepal length sepal width petal length petal width class 5.1 3.5 1.4 0.2 Setosa 7.0 3.2 4.7 1.4 Versicolor 5.8 2.7 5.1 1.9 Virginica ... ... ... ... ...

Learning Example

• Given any iris, we want to know to which class it belongs

sepal length sepal width petal length petal width class 6.1 3.2 5.0 1.3 ???

Learning Example

• Find a function:
• That function can be linear/non-linear/tree/set-of-rules/...

f(sepal length, sepal width, petal length, petal width) → class

The ML algorithm will produce that Model (here a formula) The Model can predict new samples

Learning Example

Another example

Another Example

• Algorithm to detect spam e-mails
• A Bayes-based (Naïve Bayes) approach
• Counting the word “diamonds” from spam-classified e-mails:

spam ¬ spam diamonds 130 5 135 ¬ diamonds 987 300 1287 1117 305 1422

Another Example

• The NB algorithm concludes from emails that:
• These stats will define the model

P(spam) = 1117/1422 = 0.786 P(¬spam) = 305/1422 = 0.214 P(diamonds) = 135/1422 = 0.095 P(¬diamonds) = 1287/1422 = 0.905 P(diamonds & spam) = 130/1422 = 0.091 P(diamonds & ¬spam) = 5/1422 = 0.0035 spam ¬ spam diamonds 130 5 135 ¬ diamonds 987 300 1287 1117 305 1422

Another Example

• The model for “diamonds” become:
• Given a new mail with “diamonds”:
• The mail is classified as “spam”

P(spam) = 1117/1422 = 0.786 P(¬spam) = 305/1422 = 0.214 P(diamonds) = 135/1422 = 0.095 P(¬diamonds) = 1287/1422 = 0.905 P(diamonds & spam) = 130/1422 = 0.091 P(diamonds & ¬spam) = 5/1422 = 0.0035 P(spam | diamonds) ← P(diamonds & spam) P(diamonds) P(¬spam | diamonds) ← P(diamonds & ¬spam) P(diamonds) P(spam | diamonds) = 0,9514 P(¬spam | diamonds) = 0,0367

ML Capabilities

• Models are based on statistical properties of data
• ML algorithms “automate” such modeling
• Uses:

1. Estimate values (regression) 2. Predict classes or categories (classification) 3. Find similarities from data (clustering) 4. Recommendations 5. Display properties of the modeled system

• There are lots of different Algorithms, with different properties

Interpretability

• Interpreting Models
• Know the mechanism behind data!

Model

Data

A → B

Interpretability

• Models are usually interpretable
• Read the model
• View the statistical properties of data

Model

Data

A → B

Model

Data

C = Ax + B

Model

Data

A > 5 → C A ≤ 5 → B

Interpretability

• E.g. Regressions
• E.g. Decision trees
• E.g. Naive Bayes

f(x) = 102 + speed * 10.34 + weight * 5.14

P(diamonds | spam) = 0.115 P(spam) = 0.786

Summary

• Time to decide which algorithm use
• Choose those that are expected to fit better to your problem
• Look for the statistical analysis you would perform
• And which question you want to answer
• According to
• … the data you have
• … the problem you are solving
• … what you know about the problem
• … how you are going to use the model
• Some people…
• … have a favorite set of methods
• … just try a bunch of them, and see which one works better
• … select the method according to their constraints