This document discusses neural networks and their applications. It covers perceptrons, which are single-layer neural networks, and the perceptron training rule. It also describes gradient descent search and the delta rule for training neural networks. The document introduces multi-layer neural networks and the backpropagation algorithm for training these more complex networks. In the end, it provides examples of applications of neural networks such as text-to-speech, fraud detection, and game playing.
This document discusses exercises related to information gain and decision tree learning. Exercise 2 calculates the information gain of attributes a1 and a2 on a sample dataset. Exercise 3 discusses overfitting related to using a unique identifier attribute. Exercise 4 shows that an attribute with many unique values can achieve maximum information gain but may not be a good predictor. Exercise 5 discusses approaches for handling missing values when calculating information gain.
The document discusses the history and future of the semantic web. It begins with a brief history of the world wide web and Tim Berners-Lee's original vision for a semantic web. It then explains the concept of the semantic web, how it will work by linking data on the web, and how this will change and improve how people search for and interact with information online. The document also outlines some of the key technologies that enable the semantic web, such as linked data, URIs, RDF, ontologies and SPARQL. It provides examples of current semantic web applications and concludes by discussing the opportunities and potential disruptions the semantic web may bring.
The document describes a study investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. The researchers designed a brainstorming application using an interactive table and wall display, and compared it to traditional paper-based brainstorming. They derived design guidelines for collaborative systems in interactive environments based on considerations from the application's design and observations during a user study with 30 participants. The guidelines aim to support group awareness, minimize cognitive load, and mediate mutual idea activation in order to foster collaborative creative problem solving.
This document discusses neural networks and their applications. It covers perceptrons, which are single-layer neural networks, and the perceptron training rule. It also describes gradient descent search and the delta rule for training neural networks. The document introduces multi-layer neural networks and the backpropagation algorithm for training these more complex networks. In the end, it provides examples of applications of neural networks such as text-to-speech, fraud detection, and game playing.
This document discusses exercises related to information gain and decision tree learning. Exercise 2 calculates the information gain of attributes a1 and a2 on a sample dataset. Exercise 3 discusses overfitting related to using a unique identifier attribute. Exercise 4 shows that an attribute with many unique values can achieve maximum information gain but may not be a good predictor. Exercise 5 discusses approaches for handling missing values when calculating information gain.
The document discusses the history and future of the semantic web. It begins with a brief history of the world wide web and Tim Berners-Lee's original vision for a semantic web. It then explains the concept of the semantic web, how it will work by linking data on the web, and how this will change and improve how people search for and interact with information online. The document also outlines some of the key technologies that enable the semantic web, such as linked data, URIs, RDF, ontologies and SPARQL. It provides examples of current semantic web applications and concludes by discussing the opportunities and potential disruptions the semantic web may bring.
The document describes a study investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. The researchers designed a brainstorming application using an interactive table and wall display, and compared it to traditional paper-based brainstorming. They derived design guidelines for collaborative systems in interactive environments based on considerations from the application's design and observations during a user study with 30 participants. The guidelines aim to support group awareness, minimize cognitive load, and mediate mutual idea activation in order to foster collaborative creative problem solving.
Michel van Ast en Bob Hofman faciliteren de opleiding ‘Expert formatief leren en werken.’ De deelnemers zijn werkzaam binnen het voortgezet onderwijs. Zij hadden mij gevraagd om in het kader van deze opleiding een sessie te verzorgen over het verzamelen van data om lerenden beter te kunnen ondersteunen en begeleiden.
LEAN MDM ; wat is het en hoe kun je het optimaal gebruiken?BBPMedia1
Direct beschikbare, goede kwaliteit data wordt tegenwoordig gezien als de kern van een succesvolle bedrijfsvoering. Het is de uitdaging om als MDM-organisatie de toenemende eisen en het tempo van veranderingen binnen de het bedrijf op een effectieve en efficiënte manier te kunnen volgen. Kunnen de Lean principes, ooit bedacht door Toyota in de vorige
eeuw, hier wellicht uitkomst bieden en welke rol kan Artificial Intelligence hierbij vervullen?
Hoe zijn de ervaringen met de digitale leeromgeving in PO en VO?SURF Events
Maandag 7 november
Sessieronde 1
Titel: Hoe zijn de ervaringen met de digitale leeromgeving in PO en VO?
Spreker(s): Wietse van Bruggen (Kennisnet)
Zaal: J.F. Staal Room
Fundamentele procesverbetering volgens de methode LEANBram van Vliet
Door procesverbeteringen te realiseren met het gebruik van LEAN, kunnen organisaties veel besparen. In deze presentatie vind je een voorbeeld van de aanpak van Hiemstra & De Vries.
Voorbeelden van hoe voorspellende modellen en business intelligence kan bijdragen aan data analyse binnen bedrijven. Met voorbeelden en cases illustreren wij de toepassing hiervan.
Waarden ethiek en ai in het onderwijs, deel 2 - Wilco Te Winkel (EUR), Arun R...SURF Events
AI in het onderwijs is een beladen toepassing. Hoe beschermen we onze waarden en zorgen we dat algoritmen ethisch worden ingezet? In deze sessie wordt concreet ingegaan op welke wijze ethische overwegingen een rol spelen tijdens de ontwikkelingfase als de productiefase AI algoritmen.
Knowledge Graphs: Concept, mogelijkheden en aandachtspuntenChristophe Debruyne
Kennis en informatie in een bedrijfsorganisatorische context zijn doorgaans versnipperd en verspreid over databases, rekenbladen, documenten, etc. Daarnaast bezitten kenniswerkers ook domeinexpertise die niet in een systeem wordt opgeslagen. Maar wat als men die kennis en informatie wenst te integreren om, bijvoorbeeld, processen te automatiseren of nieuwe inzichten te verwerven?
Knowledge graphs bieden hiervoor een oplossing. In deze presentatie werpt Christophe Debruyne zijn licht op het concept van de knowledge graphs en hun mogelijkheden. Hij behandelt daarvoor de volgende punten:
Wat is een knowledge graph?
Knowledge graphs versus andere initiatieven
Knowledge graphs versus andere AI technieken
Toepassingsgebied van knowledge graphs
Bouwen en onderhouden van een knowledge graph
SAI.be avondseminarie van 16-11-2021
This document provides instructions for 5 exercises on data mining homework. Students are asked to submit their answers to the given exercises electronically by November 25, 2010. The exercises cover topics such as information gain, handling missing attribute values, perceptrons, gradient descent, and stochastic gradient descent. Contact information is provided for two teaching assistants in case students have any questions.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. It begins by discussing the uncertainty in the world and how probability provides a framework to model uncertainty. The fundamentals of probability are then reviewed, including discrete random variables, probabilities, the axioms of probability, and theorems that can be derived from the axioms. Conditional probability and Bayesian inference are introduced. Joint probability distributions are discussed as a way to specify probabilities over multiple variables. The document aims to provide the foundations for understanding probabilistic modeling and reasoning.
This document discusses cross-validation techniques for evaluating machine learning models on a dataset and preventing overfitting. It introduces linear regression, quadratic regression, and join-the-dots/nonparametric regression on a sample regression problem. It then explains the test set method for model evaluation but notes its high variance. Leave-one-out cross-validation (LOOCV) and k-fold cross-validation are presented as alternatives that make more efficient use of data. Examples are given comparing the performance of different models using these cross-validation techniques on the sample regression problem. The document concludes by discussing how cross-validation can be used for model selection tasks like choosing the number of hidden units in a neural network or the k value in
The document describes the process of constructing decision trees. It begins with an example weather dataset and shows how to build a decision tree to predict whether to play or not based on attributes like outlook, temperature, etc. It then discusses the key steps in constructing decision trees which include selecting the best attribute to split on at each node based on information gain. It also discusses overfitting and the need for tree pruning. The document provides formulas to calculate information gain and discusses strategies like using a chi-squared test to select statistically robust splits during tree construction.
This document outlines linear regression, which is a machine learning technique for predicting real-valued outputs based on numerical input variables. It assumes a linear relationship between the inputs and outputs. Linear regression finds the linear equation that best fits the training data by minimizing a sum of squared errors function. The parameters of the linear equation can be estimated analytically through differentiation and solving for when the partial derivatives are equal to zero.
Michel van Ast en Bob Hofman faciliteren de opleiding ‘Expert formatief leren en werken.’ De deelnemers zijn werkzaam binnen het voortgezet onderwijs. Zij hadden mij gevraagd om in het kader van deze opleiding een sessie te verzorgen over het verzamelen van data om lerenden beter te kunnen ondersteunen en begeleiden.
LEAN MDM ; wat is het en hoe kun je het optimaal gebruiken?BBPMedia1
Direct beschikbare, goede kwaliteit data wordt tegenwoordig gezien als de kern van een succesvolle bedrijfsvoering. Het is de uitdaging om als MDM-organisatie de toenemende eisen en het tempo van veranderingen binnen de het bedrijf op een effectieve en efficiënte manier te kunnen volgen. Kunnen de Lean principes, ooit bedacht door Toyota in de vorige
eeuw, hier wellicht uitkomst bieden en welke rol kan Artificial Intelligence hierbij vervullen?
Hoe zijn de ervaringen met de digitale leeromgeving in PO en VO?SURF Events
Maandag 7 november
Sessieronde 1
Titel: Hoe zijn de ervaringen met de digitale leeromgeving in PO en VO?
Spreker(s): Wietse van Bruggen (Kennisnet)
Zaal: J.F. Staal Room
Fundamentele procesverbetering volgens de methode LEANBram van Vliet
Door procesverbeteringen te realiseren met het gebruik van LEAN, kunnen organisaties veel besparen. In deze presentatie vind je een voorbeeld van de aanpak van Hiemstra & De Vries.
Voorbeelden van hoe voorspellende modellen en business intelligence kan bijdragen aan data analyse binnen bedrijven. Met voorbeelden en cases illustreren wij de toepassing hiervan.
Waarden ethiek en ai in het onderwijs, deel 2 - Wilco Te Winkel (EUR), Arun R...SURF Events
AI in het onderwijs is een beladen toepassing. Hoe beschermen we onze waarden en zorgen we dat algoritmen ethisch worden ingezet? In deze sessie wordt concreet ingegaan op welke wijze ethische overwegingen een rol spelen tijdens de ontwikkelingfase als de productiefase AI algoritmen.
Knowledge Graphs: Concept, mogelijkheden en aandachtspuntenChristophe Debruyne
Kennis en informatie in een bedrijfsorganisatorische context zijn doorgaans versnipperd en verspreid over databases, rekenbladen, documenten, etc. Daarnaast bezitten kenniswerkers ook domeinexpertise die niet in een systeem wordt opgeslagen. Maar wat als men die kennis en informatie wenst te integreren om, bijvoorbeeld, processen te automatiseren of nieuwe inzichten te verwerven?
Knowledge graphs bieden hiervoor een oplossing. In deze presentatie werpt Christophe Debruyne zijn licht op het concept van de knowledge graphs en hun mogelijkheden. Hij behandelt daarvoor de volgende punten:
Wat is een knowledge graph?
Knowledge graphs versus andere initiatieven
Knowledge graphs versus andere AI technieken
Toepassingsgebied van knowledge graphs
Bouwen en onderhouden van een knowledge graph
SAI.be avondseminarie van 16-11-2021
This document provides instructions for 5 exercises on data mining homework. Students are asked to submit their answers to the given exercises electronically by November 25, 2010. The exercises cover topics such as information gain, handling missing attribute values, perceptrons, gradient descent, and stochastic gradient descent. Contact information is provided for two teaching assistants in case students have any questions.
This document discusses the need for benchmarking and evaluation of visualization tools for data mining. It proposes developing standardized test datasets and metrics to compare different visualization approaches. The challenges include:
1) Performance depends on user expertise - domain knowledge is needed to understand complex real-world datasets. Evaluations must account for different user skill levels.
2) Perceptual issues - comparisons require controlling display/viewing conditions and ensuring users receive comparable training to learn how to interpret visualizations.
3) Acceptance by the KDD community - overcoming technical and cultural barriers to establishing benchmarking as a standard practice. The document advocates developing a centralized testing laboratory to standardize evaluations.
This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. It begins by discussing the uncertainty in the world and how probability provides a framework to model uncertainty. The fundamentals of probability are then reviewed, including discrete random variables, probabilities, the axioms of probability, and theorems that can be derived from the axioms. Conditional probability and Bayesian inference are introduced. Joint probability distributions are discussed as a way to specify probabilities over multiple variables. The document aims to provide the foundations for understanding probabilistic modeling and reasoning.
This document discusses cross-validation techniques for evaluating machine learning models on a dataset and preventing overfitting. It introduces linear regression, quadratic regression, and join-the-dots/nonparametric regression on a sample regression problem. It then explains the test set method for model evaluation but notes its high variance. Leave-one-out cross-validation (LOOCV) and k-fold cross-validation are presented as alternatives that make more efficient use of data. Examples are given comparing the performance of different models using these cross-validation techniques on the sample regression problem. The document concludes by discussing how cross-validation can be used for model selection tasks like choosing the number of hidden units in a neural network or the k value in
The document describes the process of constructing decision trees. It begins with an example weather dataset and shows how to build a decision tree to predict whether to play or not based on attributes like outlook, temperature, etc. It then discusses the key steps in constructing decision trees which include selecting the best attribute to split on at each node based on information gain. It also discusses overfitting and the need for tree pruning. The document provides formulas to calculate information gain and discusses strategies like using a chi-squared test to select statistically robust splits during tree construction.
This document outlines linear regression, which is a machine learning technique for predicting real-valued outputs based on numerical input variables. It assumes a linear relationship between the inputs and outputs. Linear regression finds the linear equation that best fits the training data by minimizing a sum of squared errors function. The parameters of the linear equation can be estimated analytically through differentiation and solving for when the partial derivatives are equal to zero.
This document summarizes a lecture on decision tree learning. It introduces decision trees and algorithms like ID3 for building trees from data. Key concepts discussed include information gain, overfitting, pruning trees, handling continuous attributes, and predicting continuous values with regression trees. Decision trees are built by recursively splitting the training data on attributes that maximize information gain until reaching leaf nodes with class predictions.
Christof Monz gave a lecture on probabilities and information theory for a data mining class. He provided a quick refresher on key probability concepts like sample spaces, events, and probability functions. He discussed examples of calculating probabilities for coin tosses and dice rolls. Monz also covered entropy as a measure of uncertainty and how more optimal encoding can achieve lower entropy. Finally, he included a brief review of calculus concepts like derivatives that are relevant to data mining.
The document summarizes the key topics from the first lecture of a data mining course. It introduces data mining as the process of extracting implicit and potentially useful information from large amounts of data. It discusses why data mining is needed due to the abundance of data and challenges of manual organization. The lecture then covers machine learning techniques used for tasks like classification, clustering, and prediction. It provides examples of data mining applications and outlines the typical steps involved in a machine learning approach.
This document contains instructions for homework assignments in data mining. It includes 3 exercises:
1) Describe two scenarios where data mining could be applied, what would be predicted, relevant attributes, data used, and potential problems.
2) Derive Bayes' rule step-by-step from definitions of conditional probability and other rules.
3) Calculate entropy for variables with different probability distributions, find the minimum bits needed to represent values, and explain which distributions have highest and lowest entropy.
This chapter discusses subjectivism as an alternative to objectivism for providing a theoretical foundation for information management. Subjectivism focuses on human sense-making and interpretation rather than objective truths. The chapter argues that subjectivism fails to address economic value, a key concern for organizations. It suggests combining objectivism and subjectivism into an integrated approach. Subjectivism is illustrated using practice-based social theories, which view social practices as transcending the divide between objectivism and subjectivism. However, differences between the two philosophies remain fundamental.
Groups tend to focus discussion on information that is commonly known, neglecting unique information known to only some members. This can result in suboptimal decisions. Groups also tend to accentuate their initial views, leading to more extreme decisions than individuals would make alone. Highly cohesive groups may prioritize consensus over considering information that challenges group unity. Effective information management is needed to help groups make better use of all relevant information in their decision making.
This chapter discusses how information management has been strongly influenced by the philosophical tradition of objectivism. Objectivism views the world as consisting of distinct objects that exist independently of human cognition and can be studied to gain objective knowledge. It has shaped key definitions and goals in information management, such as defining information and knowledge as granules that represent objective realities. Information management also shows influence from microeconomics, viewing information exchange as a market and aiming to maximize participation and competition. However, the chapter argues that objectivism may not provide the best foundation for information management, as it cannot adequately deal with the subjective nature of information.
The document discusses text and images as visual sign systems for representing knowledge. It provides conceptual models for representing text, including models for typography, layout, writing systems, syntax, dictionaries, semantics, style and genre. Text representation relies on agreed upon codes and rules. Images are represented using different codes, including perceptual, textual, social, and syntagmatic/paradigmatic codes. Both text and images can be described using standards like XML, RDF and MPEG for interpretation and understanding.
Frank Nack discusses audio and emotion recognition from speech. The document covers listening to sounds, producing sounds, and interpreting emotions from acoustic variables in speech like pitch, intensity, speech rate, and voice quality. It also discusses challenges with speech data collection and segmentation, feature extraction, and using classifiers like SVM, neural networks, decision trees, and HMM for emotion recognition. Measurement and benchmarking of audio emotion recognition systems is difficult due to varying conditions and datasets.
The document discusses several applications for analyzing and generating video content including ForkBrowser for browsing large video collections, AUTEUR for automatically generating slapstick video sequences, and additional applications. It provides details on the architectures and techniques used for content representation, narrative planning, and visual design in AUTEUR for computational humor and creativity. The applications require complex, application-dependent content descriptions and are time-critical but allow for flexible video generation and analysis.
This document proposes ORL, an extension of OWL with Horn clause rules. ORL aims to overcome some expressive limitations of OWL, especially regarding properties, while maintaining compatibility with OWL's syntax and semantics. Rules are added as a new type of axiom and are given a formal abstract syntax and model-theoretic semantics as an extension of OWL DL. The addition of rules makes ontology consistency undecidable but provides greater expressive power for modeling relationships between properties. Examples are given and extensions to OWL's XML and RDF syntaxes are discussed to accommodate the new rule constructs.
1. Datamining 2007 antwoordmodel werkcollege-opgaven, week 1
N.B. De modelantwoorden zijn weergegeven in telegramstijl. Van de studenten wordt verwacht dat
zij normale Nederlandse zinnen gebruiken.
1. Mensen zetten data om in kennis, door leerproces of bewustzijn, waaruit mogelijk nieuwe
kennis ontstaat. Voor ML/DM technieken is het lastig om aan te tonen dat zij nieuwe nuttige
kennis hebben afgeleid. (1 punt)
2. Mensen leren doelgericht, met het oog op het verbeteren van prestaties. Bij machines is
doelgerichte manier van leren lastig vast te stellen. (1 punt)
3. Een concept is wat geleerd moet worden, een instantie is een voorbeeld van het concept wat
geleerd moet worden, en attributen zijn de features waarmee instanties omschreven worden.
(goed=3: 1 punt; 0<goed<3: 1/2 punt)
4. • typ- of meetfouten: controle de mogelijke attribuutwaardes handmatig
• dubbele instanties: automatische controle
• opzettelijke fouten: moeilijk op te sporen zonder uitgebreide kennis van de data
• verouderde data; beslissen tot wanneer data nog bruikbaar is
(goed>=3: 1 punt; 0<goed<3: 1/2 punt)
5. Nominale attributen hebben onvergelijkbare strings als waarden, ordinale hebben ook strings
als waarde maar deze kunnen wel worden vergeleken, en numerieke attributen hebben getallen
als waarde. (1 punt)
6. Beide zijn verzamelingen (gelabelde) data. De ene wordt door een classifier gebruikt om
een model te bouwen (trainingdata) en de andere (geheel verschillend van de eerste) wordt
gebruikt om het gebouwde model te evalueren (testdata). (1 punt)
7. • Toenemende beschikbaarheid van data
• Nut van historische data voor het ontdekken van regelmatigheden
• Nut van historische data voor het verbeteren van beslissingsprocessen
(goed>=2: 1 punt; 0<goed<2: 1/2 punt)
8. Machine learning is een belangrijk onderdeel van datamining, maar datamining bestaat uit
meer stappen (onderhoud, data verzamelen, data schonen etc.) (1 punt)
9. • Ontwikkeling van accuratere leeralgorithmes
• Ontwikkeling van leeralgorithmes die diverse databronnen verwerken
• Ontwikkeling van leeralgorithmes die menselijke training gebruiken
• Integratie van leeralgorithmes in data-managementsystemen
• Dataminingtechnologie onder aandacht brengen van grote organisaties
(goed>=3: 1 punt; 0<goed<3: 1/2 punt)
10. Datamining is een nuttige technologie die nog verder kan worden verbeterd door toekomstige
ontwikkelingen. (1/2 punt) (Eigen mening: 1/2 punt).