'Concept learning' presentation slideshows

Learning Agents Laboratory Computer Science Department George Mason University

CS 782 Machine Learning. 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. Concept learning from examples.

805 views • 68 slides

Machine Learning

795 views • 61 slides

????

????. ?? ?????????????? ??????????. ??????. ??????? chen_yu@pku.edu.cn Tel ? 82529680 ??????? Tel ? 62763742 wataloo@hotmail.com ????? http://www.icst.pku.edu.cn/course/jiqixuexi/jqxx2011.mht. Ch2 Concept Learning & General-to-Specific Ordering. Introduction to concept learning

738 views • 53 slides

CS 512 Machine Learning

CS 512 Machine Learning. Berrin Yanikoglu Slides are expanded from the Machine Learning-Mitchell book slides Some of the extra slides thanks to T. Jaakkola, MIT and others. CS512-Machine Learning. Please refer to http ://people.sabanciuniv.edu/berrin/cs512/

691 views • 52 slides

Chapter 2 - Concept learning

Chapter 2 - Concept learning. Learning from examples Concept Learning General-to specific ordering of hypotheses Version spaces and candidate elimination algorithm Inductive bias Simple approach Assumption: no noise Illustrates key concepts. Concept learning task.

921 views • 48 slides

NMU Counseling & Consultation Services

562 views • 46 slides

LEARNING

610 views • 43 slides

Old Business

Old Business. E-mails About this course What this course is about And what is not Projects Groups. First Assignment. Find your group members Write a group contract Discuss with your facilitator, sign it and turn-in Due date: Tomorrow A group contract includes Responsibilities

500 views • 43 slides

For Friday

437 views • 38 slides

Machine Learning

429 views • 32 slides

2. Concept Learning

1.2k views • 31 slides

Machine Learning 2D5362

Machine Learning 2D5362. Lecture 2: Concept Learning. Outline. Learning from examples General-to specific ordering of hypotheses Version spaces and candidate elimination algorithm Inductive bias. attributes. instance. Training Examples for Concept Enjoy Sport.

603 views • 31 slides

Continued from Tuesday...

Continued from Tuesday. Study puzzle.

361 views • 30 slides

Machine Learning, Chapter 2: Concept Learning

Overview. Concept Learning Representation Inductive Learning Hypothesis Concept Learning as Search The Structure of the Hypothesis Space Find-S Algorithm Version Space List-Eliminate Algorithm Candidate-Elimination Algorithm Using Partially Learned Concepts Inductive Bias

1.69k views • 26 slides

CS 4700: Foundations of Artificial Intelligence

403 views • 25 slides

Wednesday, 28 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 19 of 42. MAP and MLE continued, Minimum Description Length (MDL). Wednesday, 28 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org Readings for next class: Chapter 5, Mitchell. Lecture Outline.

330 views • 24 slides

ILP for Mathematical Discovery

ILP for Mathematical Discovery. Simon Colton & Stephen Muggleton Computational Bioinformatics Laboratory Imperial College. The Automation of Reasoning. Aims for the talk Discuss a new ILP algorithm (ATF) and its implementation in the HR system Promote maths as a domain for ILP research.

327 views • 21 slides

Concept Learning and Version Spaces

478 views • 17 slides

A Granular Computing Paradigm for Concept Learning

A Granular Computing Paradigm for Concept Learning. Xiaofei Deng Ph.D. Candidate Department of Computer Science. Outline. Concept learning Granular computing A model for concept learning Applications. What is concept learning?. What is a concept? Basic unit of human thought

257 views • 14 slides

Critical Thinking

Education 173 Cognition and Learning in Educational Settings Critical Thinking and Reasoning Fall Quarter, 2007. Critical Thinking. Another Major Category of Cognition What is Critical Thinking? The evaluation of ideas for their quality, especially in judging whether they make sense

248 views • 9 slides