'Inductive learning' diaporamas de présentation

Models of Teaching

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Motivation

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CSC 480: Artificial Intelligence

CSC 480: Artificial Intelligence. Dr. Franz J. Kurfess Computer Science Department Cal Poly. Introduction Intelligent Agents Search problem solving through search informed search Games games as search problems. Knowledge and Reasoning reasoning agents propositional logic

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Machine Learning

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IT/CS 811 Principles of Machine Learning and Inference

IT/CS 811 Principles of Machine Learning and Inference. 3. Inductive Learning from Examples: Version space learning. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Overview. Instances, concepts and generalization.

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Artificial Intelligence

Artificial Intelligence. Learning from observations Chapter 18, AIMA. Two types of learning in AI. Deductive : Deduce rules/facts from already known rules/facts. (We have already dealt with this) Inductive : Learn new rules/facts from a data set D.

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Introduction to Machine Learning

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Learning from Observations

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Chapter 6: Machine Learning

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Context-Aware Negotiation in E-commerce

Context-Aware Negotiation in E-commerce. Reyhan AYDO ?AN reyhan.aydogan@boun.edu.tr. OUTLINE. Introduction Matchmaking Negotiation Proposed Negotiation Scheme Architecture Learning Discussion. Introduction. Agents for flexible e-commerce applications Two agent roles:

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Thursday, 01 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~

Lecture 08 of 42. Decision Tree Induction and Overfitting. Thursday, 01 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chapter 3.6-3.8, Mitchell. Lecture Outline. Read Sections 3.6-3.8, Mitchell

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Learning From Observations

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Kuang Ru ; Jinan Xu ; Yujie Zhang; Peihao Wu Beijing Jiaotong University

A Method to Construct Chinese-Japanese Named Entity Translation Equivalents Using Monolingual Corpora. Kuang Ru ; Jinan Xu ; Yujie Zhang; Peihao Wu Beijing Jiaotong University. Outline. Introduction & Motivation Proposed Method Experiment & Result Analysis Conclusion.

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Learning from Observations

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Understanding by Design Stage 3: Learning Plan

Understanding by Design Stage 3: Learning Plan. Designing Instruction and Learning Experiences. Stage 3: Designing Instruction and Learning Experiences. What learning experiences and instruction will promote the desired understandings, knowledge, and skills of Stage 1?

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Thoughtful Education Inference

Thoughtful Education Inference. Rockfield Elementary. Goal One-Part 1. The goal for this session is to understand how we currently use inference in our classrooms and prepare to learn the Thoughtful Classroom approach to inference. In this session you will.

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Keywords in TEFL

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CSE 546 Data Mining Machine Learning

CSE 546 Data Mining Machine Learning. Instructor: Pedro Domingos. Logistics. Instructor: Pedro Domingos Email: pedrod@cs Office: CSE 648 Office hours: Wednesdays 3:30-4:20 TA: Hoifung Poon Email: hoifung@cs Office: 220 Office hours: Mondays 1:30-2:20 Web: www.cs.washington.edu/546

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HYGIA

HYGIA. Design and Application of new Artificial Intelligence techniques to the acquisition and use of medical knowledge represented as care pathways. Outline.

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Wednesday, February 2, 2000 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 7. Analytical Learning Discussion (3 of 4): Learning and Knowledge. Wednesday, February 2, 2000 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chown and Dietterich. Lecture Outline. Paper

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