Peluffo-Ordóñez, Diego H.

Universidad de Nariño

Introduction to Machine LearningPeluffo, D. 1Diego H. Peluffo-Ordóñezdhpeluffo@yachaytech.edu.ecwww.diegopeluffo.comwww.sdas-group.comMay, 2018CONTEXT AI VS NIPeluffo, D. 23PerspectiveEasier forhumanArtificial and natural intelligence4Easier formachineAre the purple lines inclined or angled?Artificial and natural intelligence (2)5How many objects are in the picture?Artificial and natural intelligence (3)Easy for both6How many black points? Artificial and natural intelligence (4)Easier formachine7Perception is so important…Artificial and natural intelligence (5)8How many faces? What is the picture about?Is there a triangle?Artificial and natural intelligence (6)Peluffo, D. 9IntegrationMachine LearningPeluffo, D. 10Machine Learning & Data ProcessingD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsSignal processingPeluffo, D. 12Machine learningPeluffo, D. 13Digital SingalProcessingMachine LearningKNOWLEDGEPatternRecognitionPattern recognitionPattern recognitionPattern recognitionPattern Recognitionis the research area that studies the operationand design of systems that recognize patterns indata.It encloses: discriminant analysis, featureextraction, error estimation and cluster analysis.Applications: image analysis, characterrecognition, speech analysis, man and machinediagnostics, person identification and industrialinspection.DetectionPeluffo, D. 18Matrix notationMatrixVectorData set representationData matrix Label vectorn samples, p featuresExample:3-dimensional 3-class data setPR systemGoals and requirements for PRPR booksData classificationData classificationNot always it is that simple…Unsupervised vs SupervisedSupervised approach- Requires a labeled training data set.- Reaches high accuracy when model and training size is enough.- Is able to generalize from input data.- Outputs automatic classification.K-NN, Bayes Classifier, SVMD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsUnsupervised approach- Requires a groupingcriterion (No labeled data are required).- Generally reaches lower accuracy than supervised approaches.- Has no a natural generalization ability.- Outputs clusters holding similar objects.Unsupervised vs SupervisedD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsPeluffo, D. 29ApplicationsPeluffo, D. 30• Holter Recordings•Long term monitoring•Holter analysis•Holter monitoringBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on Kernels0.20.30.40.50.60.70.80.9100.050.10.150.20.10.20.30.40.50.60.70.80.91f 3Space featuresf1f2Feature spaceBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsEspacio para subtituloDSP – Pattern recognition  Biomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on Kernels-6-4-20510-3-2-101 C1C2 C3NV0 1 2 3 4 5 6-1-0.8-0.6-0.4-0.200.20.40.60.81Tiempo:segAmplitud NormalizadaBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on Kernels5 10 15 20 25 30 35 40 45 50 55 60-1-0.8-0.6-0.4-0.200.20.40.60.81Tiempo:segAmplitud normalizadaBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsGeneral schemeBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsBiomedical signals - ECGSegment-based schemeD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsBiomedical signals - ECGD. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsDynamic data analysis – TrackingPeluffo, D. 40Espacio para subtituloDSP – Pattern recognitionDynamic data analysis – Hidden objectsPeluffo, D. 41Espacio para subtituloDSP – Pattern recognitionDynamic data analysis – OverlappingPeluffo, D. 42Espacio para subtituloDSP – Pattern recognitionComplex data clusteringPeluffo, D. 43Complex data clusteringPeluffo, D. 44Homotopy - Deforming objects2D45: 0,11, 2 1, 2,hf f h f f         A homotopy between two functions is a continuous transformation from one function into another( 1, 2,0) 1h f f f( 1, 2,1) 2h f f f: ( ) ( )1 2 2 1h C f C f A feasible way to write a homotopy mapping isthrough a linear combination in which coefficientsare given in terms of λHomotopy(Nash et al., 2013) Generalized exponential approach (GExpH) m=2 (video)46Homotopy - Deforming objectsDimensionality reduction - StructureAims at representing high‐dimensional (HD) data in low-dimensional (LD) spaces,while preserving structure47Peluffo, D.Dimensionality reduction - SeparabilityPeluffo, D. 48More information at…http://sdas-group.com/research-lines/D. H. Peluffo–Ordóñez — Dynamic Spectral Clustering based on KernelsFinal remarks• Several applications• Open issues and big challenges• Not that expensive• Nice peoplePeluffo, D. 53To learn more…Peluffo, D. 54http://diegopeluffo.com/Thank you for your attentionAny question? Here or later at… dpeluffo@yachatech.edu.ec -www.diegopeluffo.comwww.sdas-group.com55http://sdas-group.com/gallery/https://www.dropbox.com/s/bhbnaecke1belnl/VideoTutorial.mp4?dl=0

Introduction to machine learning

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Introduction to machine learning

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