'Hypothesis space' presentation slideshows

Socially Guided Machine Learning

Socially Guided Machine Learning. Andrea L. Thomaz PhD Thesis Defense April 7, 2006. Socially Guided Machine Learning. Andrea L. Thomaz PhD Thesis Defense April 7, 2006. Thesis Committee. Cynthia Breazeal Associate Professor of Media Arts & Sciences, MIT. Rosalind Picard

937 views • 83 slides

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??????. ? ? 2006 ? 6 ? Qing.tao@mail.ia.ac.cn. Outline. Data mining and statistical learning; Some algorithms and its interpretation; Statistical learning algorithms for one-class problems. Challenging problems. We are drowning in information and starving for knowledge !. Data Mining.

1.06k views • 81 slides

Reading the Mind: Cognitive Tasks and fMRI data: the improvement

Reading the Mind: Cognitive Tasks and fMRI data: the improvement. Omer Boehm, David Hardoon and Larry Manevitz IBM Research Center and University of Haifa, University College. London University of Haifa. Cooperators and Data.

668 views • 60 slides

Semi-supervised Structured Prediction Models

729 views • 56 slides

Machine Learning CS 165B Spring 2012

638 views • 53 slides

Cooperating Intelligent Systems

Cooperating Intelligent Systems. 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.

629 views • 51 slides

CS 391L: Machine Learning: Inductive Classification

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User-System I nteractions in Case B ase R easoning

User-System I nteractions in Case B ase R easoning. Sicong Kuang Instructor: Prof. Hector Munoz-Avila CSE 435. Two new communication channels. The ultimate goal of experience management is to achieve a communication of experience between an experience provider and an experience user:

568 views • 49 slides

CSCI 5582 Artificial Intelligence

CSCI 5582 Artificial Intelligence. Lecture 28 Jim Martin. HW 3. On the first set the average accuracy was 87% with 11 submissions at 100%. On the second set the average accuracy was 76% with 2 submissions getting 100% One of those was a rule-based approach

552 views • 47 slides

Explanation-Based Learning

Explanation-Based Learning. (borrowed from mooney et al). Explanation-Based Learning (EBL). One definition: Learning general problem-solving techniques by observing and analyzing solutions to specific problems. SBL (vs. EBL) lots of data (examples).

732 views • 42 slides

CSI 5388:Topics in Machine Learning

CSI 5388:Topics in Machine Learning. Inductive Learning: A Review. Course Outline. Overview Theory Version Spaces Decision Trees Neural Networks Bagging Boosting. Inductive Learning : Overview. Different types of inductive learning:

547 views • 39 slides

Support Vector Machines

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Concept Learnin g

Concept Learnin g. C.C. Fang Reference: Mitchell Machine Learning, Chapter 2. Outline. Concept learning Learning from instances (examples) General-to-specific ordering over hypotheses Version spaces and candidate-elimination algorithm The role of bias. The definition of Concept Learning.

710 views • 35 slides

Geometrization of Inference

Model: { p(x| ? )}. Truth: t(x). t. ??. Geometrization of Inference. Embedding in Hilbert Space. Fisher Information metric automagically induced on the tangent bundle !. The Volume Form as Prior.

395 views • 33 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.

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Announcements

Announcements. List of 5 source for research paper Homework 5 due Tuesday, October 30 Book Review due Tuesday, October 30. Classification problems and Machine Learning. Lecture 10. EnjoySport concept learning task. Given Instances X : Possible days, each described by the attributes

364 views • 26 slides

Decision Tree Learning

Decision Tree Learning. Presented by Ping Zhang Nov. 26th, 2007. Introduction. Decision tree learning is one of the most widely used and practical method for inductive inference

608 views • 22 slides

Decision Tree Learning

Decision Tree Learning. Presented by Ping Zhang Nov. 26th, 2007. Introduction. Decision tree learning is one of the most widely used and practical method for inductive inference

358 views • 22 slides

363 views • 18 slides

Announcements

Announcements. Exam grading Projects Next: Generative Models Chapter 6 Bayesian Learning. Shattering. We say that a set S of examples is shattered by a set of functions H if for every partition of the examples in S into positive and negative examples

265 views • 16 slides