This document provides a summary of Lecture 4 of an introduction to machine learning course. It recaps topics from Lecture 3, including different machine learning paradigms and problems that will be studied like classification, regression, clustering, and association analysis. It then discusses classification and prediction problems in more detail. Specific algorithms for classification, regression, clustering and association rule mining are introduced. Finally, it previews that the next class will focus on the C4.5 classification algorithm.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Artificial Intelligence is a branch of computer science that endeavors to replicate or simulate human intelligence in a machine, so machines can perform tasks that typically require human intelligence. Some programmable functions of AI systems include planning, learning, reasoning, problem-solving, and decision making.
Mainly AI can be divided into four types.
#datascience #artificialintelligence #datatrained #tech #technology #IT #machinelearning #deeplearning #programming #python #computerscience #programminglanguages
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning et Intelligence ArtificielleSoft Computing
Machine Learning (ML) et Intelligence Artificielle (AI) sont au cœur des stratégies des géants du net : reconnaissance de textes, de visages, de sentiments, analyse de signaux issus notamment d’objets connectés. Comment capitaliser sur ces méthodologies pour des applications Marketing ? Avec quels outils, méthodes et compétences ?
Google, Facebook, Apple et autres Microsoft se livrent une bataille de Titan sur le terrain de l’Intelligence Artificielle. Cette débauche de moyens en recherche et développement génère la diffusion en Open Source de nombreux algorithmes ou le foisonnement de fonctions et d’API de Machine Learning et Deep Learning et d’AI « as a service ». Avec des efforts minimes, tout-un-chacun peut aujourd’hui accéder simplement et pour un coût modique à des fonctionnalités puissantes pour reconnaître un visage, une voix, des sentiments …
En outre, la démocratisation des technologies Big Data donne accès à des puissances de traitement considérables qui permettent d’appliquer ces algorithmes de Machine Learning sur des centaines de milliers de points, des milliards d’enregistrements et des volumes de plusieurs péta-octets.
Le Marketing et la connaissance client capitalisent sur toutes ces nouvelles possibilités : conseiller le bon produit – en mode recommandation ou substitution, anticiper des changements dans les comportements, s’adresser au client de façon complètement personnalisée, surveiller en temps réel des indicateurs de bon ou mauvais fonctionnement – objets connectés, fluidifier et optimiser l’expérience client en identifiant des axes d’amélioration des parcours ou process.
Ce séminaire vise à démystifier le Machine Learning, à en dessiner des applications potentielles pour le Marketing. Les modalités de mise en œuvre – outils, procédures et techniques, forces et faiblesses – seront détaillées à travers la présentation de cas d’usage.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Artificial Intelligence is a branch of computer science that endeavors to replicate or simulate human intelligence in a machine, so machines can perform tasks that typically require human intelligence. Some programmable functions of AI systems include planning, learning, reasoning, problem-solving, and decision making.
Mainly AI can be divided into four types.
#datascience #artificialintelligence #datatrained #tech #technology #IT #machinelearning #deeplearning #programming #python #computerscience #programminglanguages
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
This presentation describes various categories of machine learning techniques.It starts with importance of Machine learning and difference between ML and traditional AI. Examples and in-depth explanation of different learning techniques in ML.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning et Intelligence ArtificielleSoft Computing
Machine Learning (ML) et Intelligence Artificielle (AI) sont au cœur des stratégies des géants du net : reconnaissance de textes, de visages, de sentiments, analyse de signaux issus notamment d’objets connectés. Comment capitaliser sur ces méthodologies pour des applications Marketing ? Avec quels outils, méthodes et compétences ?
Google, Facebook, Apple et autres Microsoft se livrent une bataille de Titan sur le terrain de l’Intelligence Artificielle. Cette débauche de moyens en recherche et développement génère la diffusion en Open Source de nombreux algorithmes ou le foisonnement de fonctions et d’API de Machine Learning et Deep Learning et d’AI « as a service ». Avec des efforts minimes, tout-un-chacun peut aujourd’hui accéder simplement et pour un coût modique à des fonctionnalités puissantes pour reconnaître un visage, une voix, des sentiments …
En outre, la démocratisation des technologies Big Data donne accès à des puissances de traitement considérables qui permettent d’appliquer ces algorithmes de Machine Learning sur des centaines de milliers de points, des milliards d’enregistrements et des volumes de plusieurs péta-octets.
Le Marketing et la connaissance client capitalisent sur toutes ces nouvelles possibilités : conseiller le bon produit – en mode recommandation ou substitution, anticiper des changements dans les comportements, s’adresser au client de façon complètement personnalisée, surveiller en temps réel des indicateurs de bon ou mauvais fonctionnement – objets connectés, fluidifier et optimiser l’expérience client en identifiant des axes d’amélioration des parcours ou process.
Ce séminaire vise à démystifier le Machine Learning, à en dessiner des applications potentielles pour le Marketing. Les modalités de mise en œuvre – outils, procédures et techniques, forces et faiblesses – seront détaillées à travers la présentation de cas d’usage.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
ENHANCED SIGNATURE VERIFICATION AND RECOGNITION USING MATLABAM Publications
Signature verification and recognition is a technology that can improve security in our day to day
transaction held in society. This paper presents a novel approach for offline signature verification. In this paper
offline signature verification using neural network is projected, where the signature is written on a paper are
obtained using a scanner or a camera captured and presented in an image format. For authentication of
signature, the proposed method is based on geometrical and statistical feature extraction and then the entire
database, features are trained using neural network .The extracted features of investigation signature are
compared with the previously trained features of the reference signature. This technique is suitable for various
applications such as bank transactions, passports with good authentication results etc
Presentation given at Mendeley's Open Day 2014. A high level description of what machine learning is used for in Mendeley before going a little deeper in user profiling and recommender systems. I am responsible for the first part of the presentation only. Lili Tcheang and Maya Hristakeva created parts II and II respectively.
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or unfeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach,Optical character recognition (OCR),learning to rank and computer vision.
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Thesis Statement for students diagnonsed withADHD.ppt
Lecture4 - Machine Learning
1. Introduction to Machine
Learning
Lecture 4
Slides based on Francisco Herrera course on Data Mining
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull
2. Recap of Lecture 3
Typically, techniques in ML have been divided in
different paradigms
Inductive learning
Explanation-based learning
p g
Analogy-based learning
Evolutionary learning
Connectionist Learning
Slide 2
Artificial Intelligence Machine Learning
3. Recap of Lecture 3
Problems that we’ll study
Data l
D t classification: C4 5 kNN N ï B
ifi ti C4.5, kNN, Naïve Bayes …
1.
Statistical learning: SVM
2.
Association analysis: A-priori
3.
Link mining: Page Rank
4.
Clustering: k-means
5.
Reinforcement learning: Q-learning, XCS
g g,
6.
Regression
7.
Genetic Fuzzy Systems
8.
8
Slide 3
Artificial Intelligence Machine Learning
4. Today’s Agenda
Situation: Where Are We?
Classification
Prediction
Clustering
Association
Data Mining Systems
D t Mi i S t
Slide 4
Artificial Intelligence Machine Learning
5. Situation: Where Are We?
The input consists of examples featured by
different characteristics
Slide 5
Artificial Intelligence Machine Learning
6. Situation: Where Are We?
What can we do with a bunch of examples?
Depend on the type of examples we may have
Classification: Find the class to which a new instance belongs to
g
E.g.: Find whether a new patient has cancer or not
Numeric prediction: A variation of classification in which the output
p p
consists of numeric classes
E.g.: Find the frequency of cancerous cell found
Regression: Find a function that fits your examples
E.g.: Find a function that controls your chain process
Association: Find association among your problem attributes or
variables
E.g.: Find relations such as a patient with high-blood-pressure i
E Fi d l ti h ti t ith hi h bl d is
more likely to have heart-attack disease
Clustering: Process to cluster/group the instances into classes
E.g.: Group clients whose purchases are similar
Slide 6
Artificial Intelligence Machine Learning
7. Data Classification
Test set
New instance
Information based Knowledge
on experience extraction
t ti
Learner Model
Dataset
Predicted Output
Training set
Slide 7
Artificial Intelligence Machine Learning
8. Example of Data Classification
Data Set Classification Model How
The classification model can be implemented in several ways:
• Rules
• Decision trees
• Mathematical formulae
Slide 8
Artificial Intelligence Machine Learning
9. Classification as a Two-Step Process
Model usage: to classify future or unknown objects
g y j
Estimate the accuracy of the model
The known label of test samples is compared with the label
predicted by the system
The accuracy rate is the p p
y proportion of test examples that are
p
correctly classified by the model
The test set is independent of the training set
If the experts thing that the model is acceptable
Then, use to the model to predict unknown examples
Slide 9
Artificial Intelligence Machine Learning
10. Going to Real World
katydids
Definition: Given a collection of
a o a ed data (in s
annotated da a ( this case katydids
a yd ds
and grasshoppers), decide what type
of insect in the following one
grasshoppers
Slide 10
Artificial Intelligence Machine Learning
11. Going to Real World
How can I put a katydid or a g
p y grasshopper into my
pp y
computer?
Slide 11
Artificial Intelligence Machine Learning
12. Going to Real World
Thus, the classification problem has been reduced to
, p
Insect Abdomen Antennae Insect
ID Length
L th Length
L th Class
Cl
1 2.7 5.5 Grasshopper
2 8.0 9.1 Katydid
3 0.9
09 4.7
47 Grasshopper
4 1.1 3.1 Grasshopper
5 5.4 8.5 Katykid
6 2.9 1.9 Grasshopper
7 6.1 6.6 Katydid
8 0.5 1.0 Grasshopper
9 8.3 6.6 Katydid
10 8.1
81 4.7
47 Katydid
We have an observation with abdomen length 5 1 and
5.1
antennae length 7?
Slide 12
Artificial Intelligence Machine Learning
13. Going to Real World
Actually, we could write that
y,
How do I classify this domain?
Slide 13
Artificial Intelligence Machine Learning
14. How to Create Classification Models
We will study some of this methods:
The decision tree C4 5
C4.5
The instance based classifier kNN
The probabilistic classifier Naïve Bayes
Slide 14
Artificial Intelligence Machine Learning
15. Regression or Prediction
Prediction vs data classification
Similarities: Both learn from a data set
Difference:
Diff
In classification, each example has a class associated
In
I prediction, each example has a numerical value
di ti h lh ill
associated
Slide 15
Artificial Intelligence Machine Learning
16. How to Extract a Model?
Prediction works analogously to data classification
Use
U an algorithm to b ild a model
build
l ih dl
Use this model to predict the new unknown example
Types of regression
Linear and multiple regression
Non-linear regression
Two of the most-used approaches to regression
pp g
Neural networks
F lb d t
Fuzzy rule-based systems
Slide 16
Artificial Intelligence Machine Learning
17. Clustering
The clustering problem
gp
Given a data base D={t1, t2, …, tn} of transactions and an
integer value k, the c us e g p ob e refers to de e a
ege a ue , e clustering problem e e s o define
mapping f: D {1,…, k} where each ti is assigned to one cluster
kj, 1<=j<=k
Main difference with classification
In classification, each example is labeled with a class
classification
In clustering, examples are not labeled
Examples of clustering
Segment customer data base based on
similar buying patterns
Group houses in a town into
G h i t it
neighborhoods based on similar features
Identify new plant species
Identify similar web usage patterns
Slide 17
Artificial Intelligence Machine Learning
18. Example of Clustering
Put these people in different clusters
pp
Which are the keys?
Define what’s similar
Group similar things in
different clusters
Size of the clusters?
Which type of clustering do I want?
Hierarchical clustering?
Partition-based clustering?
Slide 18
Artificial Intelligence Machine Learning
20. How to Group the Elements?
Slide 20
Artificial Intelligence Machine Learning
21. Which Type of Clustering?
Many types of clustering
y yp g
Hierarchical: Nested set of clusters
Partition-based: One set of clusters
Incremental: Each element handled at one time
Simultaneous: All elements h dl d t
Si lt l t handled together
th
Overlapping/non-overlapping
Hierarchical Clustering Partition-based Clustering
Slide 21
Artificial Intelligence Machine Learning
22. Association Rules
Given a set of items I={I1, I2, …, Im} and a database of
{, , , }
transactions D={t1, t2, …, tn} where ti={Ii1, Ii2, …, Iik}
and Iij Є I
The association rule problem is to identify all the rules
with form
X Y
Rules ith minimum s pport
R les with minim m support and confidence
Support: Fraction of transactions which contain both X and Y
Confidence: Measures of how often items in Y appear in
transactions that contain X
Slide 22
Artificial Intelligence Machine Learning
23. Example Association Rules
I = {Beer, Bread Jelly Milk PeanutButter}
{Beer Bread, Jelly, Milk,
Support of {Bread, PeanutButter} is 60%
Slide 23
Artificial Intelligence Machine Learning
25. Before Finishing…
Some environments that contain algorithms to perform
g p
data classification, regression, clustering and
association rule mining
KEEL: http://www keel es
http://www.keel.es
Weka: http://www.cs.waikato.ac.nz/ml/weka/
Rapid Miner: http://rapid-i.com/content/blogcategory/38/69/
Slide 25
Artificial Intelligence Machine Learning
26. Next Class
Start with data classification
C4.5
Slide 26
Artificial Intelligence Machine Learning
27. Introduction to Machine
Learning
Lecture 4
Slides based on Francisco Herrera course on Data Mining
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull
