How to Use the PowerPoint Template - PUE · Mostrar el nuevo curso disponible en Oracle Academy,...
Transcript of How to Use the PowerPoint Template - PUE · Mostrar el nuevo curso disponible en Oracle Academy,...
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Oracle Academy
Inteligencia Artificial con Machine Learning en Java
Jordi Ariño | Software Developer Manager at PUE
10:10 a 11:10h
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Inteligencia Artificial con Machine Learning en Java
Madrid, 8 de mayo
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Introducción
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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Introducción
Esta workshop se enmarca dentro del programa educativoOracle Academy.
Oracle Academy ofrece una solución completa de recursospara la educación en TI, con el objetivo de ayudar a losestudiantes a prepararse para su futuro profesional.
PUE es el partner escogido por Oracle para la difusión ygestión de su iniciativa Oracle Academy en España.
Son ya más de 125 centros que participan activamente eneste proyecto y reciben soporte activo por parte de PUEpara la correcta implantación de los recursos docentesque tienen a su disposición.
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Introducción
Objetivo:Mostrar el nuevo curso disponible en Oracle Academy,Artificial Intelligence with Machine Learning in Java.
Disponible dentro de la plataforma Oracle iLearning paralas instituciones educativas homologadas en el programaOracle Academy.
El curso ofrece una introducción o iniciación a los conceptosbase del proceso de Aprendizaje Automático dentro delcampo de la Inteligencia Artificial, mientras se diseña unsencillo modelo o solución de “Machine Learning” con Java.
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Introducción
Con la inscripción a este workshop habréis recibido accesogratuito a formación online en formato de autoaprendizajea una serie de cursos oficiales de Java y bases dedatos Oracle.
Objetivo:Poder evaluar algunos de los recursos e-learning que OracleAcademy pone a disposición de centros y profesores para laformación de sus alumnos, de manera oficial, en desarrolloen Java y diseño y programación de Bases de Datos Oracle.
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Programa Oracle Academy
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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Programa Oracle Academy
FACULTY PROFESSIONALDEVELOPMENT
CURRICULUM ORACLE PRESSBOOKS
LECTURE SERIES TECHNICAL ARTICLES WEBCASTS & PODCASTS
CERTIFICATION PREPARATION & EXAMS
BADGES, LOGOS &MERCHANDISE ENGAGE WITHEXPERTS ORACLE OPEN WORLD & JAVAONE
CLOUDENVIRONMENTS JAVA DEVELOPMENTENVIRONMENTS
50% OFFPreparation
25% OFFExams
IN-CLASS TRAINING
VIRTUAL +IN-CLASSTRAINING
VIRTUAL INSTRUCTO
RLED
SELF-SERVICE RESOURCES
ORACLE SOFTWARE
INDIVIDUAL LEARNING
WORKSHOPS INABOX
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Oracle Academy: Plan de estudios
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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Oracle Academy: Plan de estudios
Getting Started with Java Using Alice Creating Java Programs with Greenfoot
Solve it with SQL
Programming the Finch Robot in Greenfoot
Workshops disponibles
Programming the Finch Robot in Java
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Recursos
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Oracle Academy: Plan de estudios
Java Foundations Java Fundamentals Java Programming
Database Foundations Database Design and Programming with SQL
Programming with PL/SQL APEXApplication Development
Foundations
Cursos disponibles
Artificial Intelligence with Machine Learning in Java
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Curso AiML: Artificial Intelligence with ML in Java
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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AiML: Artificial Intelligence with ML
Este curso amplía las habilidades que adquieren losestudiantes en los cursos de Java Foundations, JavaFundamentals y Java Programming.
Se introduce a los estudiantes en los conceptos básicosde aprendizaje automático (Machine Learning) dentrode la IA.
Los alumnos aprenden terminología y conceptosbásicos de ML, además de los pasos necesarios paracrear desde cero una solución o modelo simple deMachine Learning.
Idioma: InglésTiempo: 40 horas (recomendado)
Curso AiML
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AiML: Artificial Intelligence with ML
El curso no requiere ningún conocimiento previo deInteligencia Artificial o Machine Learning.
Se requieren conocimientos previos de programaciónen Java, y se recomienda estar familiarizado –aunqueno es obligatorio- con el uso de estructuras de datosy recursividad.
El objetivo final del curso es entender e implementardesde cero un modelo sencillo de Machine Learningutilizando un algoritmo de tipo árbol de decisión.
Curso AiML
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AiML: Artificial Intelligence with ML
El curso está formado por 4 módulos o secciones concontenido interactivo de autoaprendizaje y pequeñasprácticas relacionadas para construir un proyecto final.
Section 1: IntroductionSection 2: Machine LearningSection 3: Trees and RecursionSection 4: Entropy and the ID3 Algorithm
Course Syllabus
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Section 1: Introduction
1.1 Course Overview• Understand the nature of the course• Understand the delivery mechanism
1.2 Introduction to AI• Define artificial intelligence• Define machine learning• Give examples of using artificial intelligence• Define data exhaust
1.3 Data and Information• Define data• Define information• Differentiate between data and information
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 1: Introduction
1.4 Categorizing Data• Define supervised learning• Define unsupervised learning• Define classification• Define regression• Define structured and unstructured data
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 2: Machine Learning
2.1 Why now?• State the reasons behind the growth in AI• Understand the growth in processing power
2.2 Machine Learning Workflow• Understand the use of models within machine learning• Understand the CRISP-DM Model
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 3: Trees and Recursion
3.1 Binary Trees• Understand a node• Understand a binary tree• Create a Node class
3.2 Recursion• Define recursion• Understand recursive methods• State the advantages and disadvantages of recursion
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 3: Trees and Recursion
3.3 Tree Traversal• Describe tree traversal• Define pre-order traversal• Define post-order traversal• Define in-order traversal• Create methods for Btree
3.4 Yes/No Game• Describe the use of decision trees• Create a yes/no game• State the problems of creating a manual decision tree
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 4: Entropy and the ID3 Algorithm
4.1 Decision Tree Algorithms• State a number of decision tree algorithms• Identify the ID3 algorithm
4.2 Information Entropy• Define information entropy• Understand variance• Calculate information entropy• Understand information entropy
Course Syllabus
AiML: Artificial Intelligence with ML
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Section 4: Entropy and the ID3 Algorithm
4.3 ID3 Worked Example• Calculate entropy• Calculate gain• Manually work through the ID3 algorithm
4.4 Create an ID3 Tree• Understand non binary tree structure• Create a non-binary tree structure
Course Syllabus
AiML: Artificial Intelligence with ML
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AiML: Artificial Intelligence with ML
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AiML: Artificial Intelligence with ML
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AiML: Artificial Intelligence with ML
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Demo: Plataforma Oracle iLearning
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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http://ilearning.oracle.com/ilearn/en/learner/jsp/login.jsp?site=OracleAcad
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Hands-On Labs
Inteligencia Artificial con Machine Learning en Java
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To do Doing Done
1.- Introducción
2.- Programa Oracle Academy
3.- Oracle Academy: Plan de estudios
4.- AiML: Artificial Intelligence with ML
5.- Demo: Plataforma Oracle iLearning
6.- Hands-On Labs
Agenda
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Hands-On Labs Machine Learning Workflow
CRISP-DM (Cross-Industry Standard Process for Data Mining)
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Hands-On Labs Machine Learning Workflow
CRISP-DM (Cross-Industry Standard Process for Data Mining)
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Hands-On Labs Machine Learning
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Hands-On Labs Machine Learning
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Hands-On Labs Data Source
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Hands-On Labs Data Source
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Hands-On Labs Data Source
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Hands-On Labs Weka 3: Machine Learning Software
@relation Weather
@attribute Outlook {Sunny, Overcast, Rain}@attribute Temperature {Hot, Mild, Cold}@attribute Humidity {High, Normal}@attribute Wind {Weak, Strong}@attribute Play {Yes, No}
@dataSunny,Hot,High,Weak,NoSunny,Hot,High,Strong,NoOvercast,Hot,High,Weak,YesRain,Mild,High,Weak,YesRain,Cold,Normal,Weak,YesRain,Cold,Normal,Strong,NoOvercast,Cold,Normal,Strong,YesSunny,Mild,High,Weak,NoSunny,Cold,Normal,Weak,YesRain,Mild,Normal,Weak,YesSunny,Mild,Normal,Strong,YesOvercast,Mild,High,Strong,YesOvercast,Hot,Normal,Weak,YesRain,Mild,High,Strong,No
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Hands-On Labs Weka 3: Machine Learning Software
@relation Titanic-Passengers
@attribute Class {"1st","2nd","3rd","crew"}@attribute Age {"Adult","Child"}@attribute Sex {"Male","Female"}@attribute Survived {"Yes","No"}
@data1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No...
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Hands-On Labs
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Hands-On Labs Árboles de Decisión
Existen muchos algoritmos de tipo Árbol de Decisión(Decision Tree Algorithms) en Machine Learning.
Algunos de los más populares son:
• Iterative Dichotomiser3 (ID3)• Classification and Regression Tree (CART)• C4.5• Chi-squared Automatic Interaction Detection (CHAID)• Decision Stump• Conditional Decision Trees
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Hands-On Labs Algoritmo ID3
Se usará, como punto de partida, un pequeño dataset pararecorrer paso a paso cada una de las fases del algoritmo ID3y comprender todo el proceso de creación del árbol.
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Hands-On Labs Yes/No Game
Árboles binarios
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Hands-On Labs Yes/No Game
Computer > File to read the knowledge base from? Human > resources\kbs\got--------------------------------------------------------------------Computer > Topic: 'Game of Thrones character'--------------------------------------------------------------------Computer > Are you thinking in a 'Game of Thrones character'? (Y/N)Human > YComputer > Is a man? (Y/N)Human > YComputer > She/He/It is 'Tyrion Lannister'? (Y/N)Human > N--------------------------------------------------------------------Computer > You win, I loose!!!----------------------------------------------------------------------------------------------------------------------------------------Topic: Game of Thrones characterComputer: 0 winsHuman: 1 wins--------------------------------------------------------------------Computer > Who/what were you thinking of? Human > John SnowComputer > Please enter a yes/no question that would distinguish'John Snow' from 'Tyrion Lannister' Human > Is a King?Computer > For 'John Snow' the answer would be (Y/N)Human > Y
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Hands-On Labs Yes/No Game---------------------------------------------------------------------Computer > Topic: 'Game of Thrones character'---------------------------------------------------------------------Computer > Are you thinking in a 'Game of Thrones character'? (Y/N)Human > YComputer > Is a man? (Y/N)Human > YComputer > Is a King? (Y/N)Human > YComputer > She/He/It is 'John Snow'? (Y/N)Human > N---------------------------------------------------------------------Computer > You win, I loose!!!------------------------------------------------------------------------------------------------------------------------------------------Topic: Game of Thrones character Computer: 0 wins Human: 2 wins ---------------------------------------------------------------------Computer > Who/what were you thinking of? Human > Robert BaratheonComputer > Please enter a yes/no question that would distinguish 'Robert Baratheon' from 'John Snow' Human > Is alive?Computer > For 'Robert Baratheon' the answer would be (Y/N)Human > N
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Hands-On Labs Yes/No Game
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Hands-On Labs Yes/No Game
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Hands-On Labs Yes/No Game
AnimalesQ: Es un animal acuatico?Q: Es un animal muy inteligente?A: DelfinQ: Es un animal muy peligroso?Q: Tienen cuerpo gelatinoso?A: MedusaA: TiburonQ: Se alimentan de placton?Q: Tiene la boca en forma de trompeta?A: Caballito de marA: PezQ: Es un animal de rio?A: CocodriloA: BallenaQ: Es un reptil?Q: Cambia de color?A: CamaleonQ: Se arrastra por el suelo?A: SerpienteA: LagartoQ: Es un mamifero?Q: Se cuelga por los arboles?A: MonoQ: Es de la familia de los felinos?Q: Caza animales de tamano mediano o grande para comer?
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Hands-On Labs Yes/No Game
Game of Thrones characterQ: Is a man?A: John NieveA: Danerys
Game of Thrones characterQ: Is a man?Q: Is a King?Q: Is alive?A: John SnowA: Robert BaratheonA: Tyrion LannisterQ: Is youngth?A: DanerysA: Cersei Lannister
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Hands-On Labs Yes/No Game
Árboles binarios
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Hands-On Labs Yes/No Game
Árboles binarios
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Hands-On Labs Yes/No Game
Árboles binarios
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Hands-On Labs Binary Tree Structure
public class TreeNode<T> {
private T content;private TreeNode<T> leftNode;private TreeNode<T> rightNode;
public TreeNode(T content, TreeNode<T> leftNode, TreeNode<T> rightNode) {
this.setContent(content);this.setLeftNode(leftNode);this.setRightNode(rightNode);
}
public TreeNode(T content) {this(content, null, null);
}…
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Hands-On Labs Binary Tree Structure
public class BinaryTree<T> {
private TreeNode<T> root;private TreeNode<T> current;
public BinaryTree(TreeNode<T> root) {this.setRoot(root);this.setCurrent(root);
}
public BinaryTree(T content) {this(new TreeNode(content));
}
public String preOrder() {return this.getRoot().preOrder();
}public String postOrder() {
return this.getRoot().postOrder();}
public String inOrder() {return this.getRoot().inOrder();
}
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Hands-On Labs Pre-Order Traversal
1,2,4,5,7,3,6
Se accede primero a un node antes de acceder a sus correspondientes nodos hijos.
Se procesan primero los nodos de la izquierda.
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Hands-On Labs Post-Order Traversal
4,7,5,2,6,3,1
Se acceden primero a los nodos hijos antes de acceder al correspondiente padre.
Se procesan primero los nodos de la izquierda.
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Hands-On Labs In-Order Traversal
4,2,7,5,1,3,6
Se acceden primero al correspondiente sub-árbol izquierdo de un nodo padre.
Luego al correspondiente nodo padre y finalmente al sub-árbol derecho.
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Hands-On Labs Dataset o conjunto de datos
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Hands-On Labs
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Algoritmo ID3
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Hands-On Labs Entropía y ganancia
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Hands-On Labs Entropía de la Información
La entropía de la información es un concepto definido por losmatemáticos en 1949, y se refiere a la incertidumbre en losdatos.
Los datos con un alto nivel de incertidumbre (o entropía)contendrán más información útil que podrá ser utilizada.
La entropía de la información se mide como:
Esta ecuación devolverá cuánta información se puede esperarde una acción dada.
Cuanto mayor sea el número, más información obtenemos.
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Hands-On Labs Entropía de la Información
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Hands-On Labs Entropía de la Información
Tenemos bolsa con 10 canicas verdes
Calcular la entropía asociada a la acción de sacar una canica.
Obtenemos una entropía de 0.
El nivel de incertidumbre es mínimo, por lo que podemosobtener poca información de este conjunto de datos. Por lotanto, no es un buen conjunto de entrenamiento.
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Hands-On Labs Entropía de la Información
Tenemos bolsa con 5 canicas verdesy 5 cánicas rojas.
Calcular la entropía asociada a la acción de sacar una canica.
Este es un valor de entropía más alto, por lo que tenemos un mejor conjunto de entrenamiento para el aprendizaje.
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
Paso 1Calculamos la entropía asociada al atributo de salida: Play
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Hands-On Labs Árbol de decisión ID3
Paso 2Ahora calculamos el valor de ganancia de información(gain) para cada uno de los atributos o features de nuestroconjunto de datos.
Así podremos elegir porqué atributo o feature se puedehacer un mejor split en nuestro árbol.
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Ganancia de la información
El cálculo de la ganancia de información –gain- mide lacantidad de entropía que se reduce en el dataset, alparticionar o clasificar a partir de un atributo específico (A).
Cuanto mayor sea el número de ganancia obtenido, mejorse clasificarán los datos a partir de ese atributo.
Clave: Calcular la entropía para cada posible valor delatributo y luego calcular la ganancia para ese atributo.
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
A: Outlook #Play: Yes # Play: No Total
Sunny 2 3 5
Overcast 4 0 4
Rain 3 2 5
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Hands-On Labs Árbol de decisión ID3
A: Outlook #Play: Yes # Play: No Total
Sunny 2 3 5
Overcast 4 0 4
Rain 3 2 5
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
A: Temperature #Play: Yes # Play: No Total
Cold 3 1 4
Mild 4 2 6
Hot 2 2 4
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Hands-On Labs Árbol de decisión ID3
A: Temperature #Play: Yes # Play: No Total
Cold 3 1 4
Mild 4 2 6
Hot 2 2 4
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
A: Humidity #Play: Yes # Play: No Total
High 3 4 7
Normal 6 1 7
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Hands-On Labs Árbol de decisión ID3
A: Humidity #Play: Yes # Play: No Total
High 3 4 7
Normal 6 1 7
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
A: Wind #Play: Yes # Play: No Total
Weak 6 2 8
Strong 3 3 6
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Hands-On Labs Árbol de decisión ID3
A: Wind #Play: Yes # Play: No Total
Weak 6 2 8
Strong 3 3 6
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
¿Qué atributo seleccionamos ahora para continuarbajando por nuestro árbol?
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Hands-On Labs Árbol de decisión ID3
¿Qué atributo seleccionamos ahora para continuarbajando por nuestro árbol?
Y así para el resto de atributos …
A: Temperature #Play: Yes # Play: No Total
Cold 0 2 2
Mild 1 1 2
Hot 1 0 1
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Árbol de decisión ID3
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Hands-On Labs Algoritmo ID3
No es tan complicado!! Ahora a implementarlo!!
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Hands-On Labs Tarea final
Implementar el algoritmo de aprendizaje automático ID3.
El correspondiente árbol de decisión será almacenado enuna estructura de tipo árbol, que habrá que implementar.
No se trata de un árbol binario, ya que un nodo podrá tener2 o más nodos hijos.
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Hands-On Labs Implementar la clase Node
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Hands-On Labs Implementar la clase Tree
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Hands-On Labs Implementar la clase Tree
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Hands-On Labs Weka 3: Machine Learning Software
@relation Weather
@attribute Outlook {Sunny, Overcast, Rain}@attribute Temperature {Hot, Mild, Cold}@attribute Humidity {High, Normal}@attribute Wind {Weak, Strong}@attribute Play {Yes, No}
@dataSunny,Hot,High,Weak,NoSunny,Hot,High,Strong,NoOvercast,Hot,High,Weak,YesRain,Mild,High,Weak,YesRain,Cold,Normal,Weak,YesRain,Cold,Normal,Strong,NoOvercast,Cold,Normal,Strong,YesSunny,Mild,High,Weak,NoSunny,Cold,Normal,Weak,YesRain,Mild,Normal,Weak,YesSunny,Mild,Normal,Strong,YesOvercast,Mild,High,Strong,YesOvercast,Hot,Normal,Weak,YesRain,Mild,High,Strong,No
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Hands-On Labs Weka 3: Machine Learning Software
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Hands-On Labs Weka 3: Machine Learning Software
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Hands-On Labs Weka 3: Machine Learning Software
@relation Titanic-Passengers
@attribute Class {"1st","2nd","3rd","crew"}@attribute Age {"Adult","Child"}@attribute Sex {"Male","Female"}@attribute Survived {"Yes","No"}
@data1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,Yes1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No1st,Adult,Male,No...
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Hands-On Labs Weka 3: Machine Learning Software
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Hands-On Labs Weka 3: Machine Learning Software
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Hands-On Labs Weka 3: Machine Learning Software
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Hands-On Labs Weka 3: Machine Learning Software
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