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THE HONG KONG UNIVERSITY OF SCIENCE & TECHNOLOGY CSIT 600N:  Reasoning and Decision under Uncertainty Summer 2010

THE HONG KONG UNIVERSITY OF SCIENCE & TECHNOLOGY CSIT 600N:  Reasoning and Decision under Uncertainty Summer 2010

THE HONG KONG UNIVERSITY OF SCIENCE & TECHNOLOGY CSIT 600N:  Reasoning and Decision under Uncertainty Summer 2010 Nevin L. Zhang Room 3504, phone: 2358-7015, Email: lzhang@cs.ust.hk Home page PMs for Classification PMs for Clustering: Continuous data PMs for Clustering: Discrete data

By issac
(326 views)

The WinMine Toolkit

The WinMine Toolkit

The WinMine Toolkit. Max Chickering. Build Statistical Models From Data. Dependency Networks Bayesian Networks Local Distributions Trees Multinomial / Binary Multinomial Gaussian / Binary Gaussian Log Gaussian / Binary Log Gaussian Complete Tables. Data Processing Tools.

By dante
(300 views)

Bayesian Networks

Bayesian Networks

Bayesian Networks. Team Orange : Harrison, Sheehan, McGregor, & Law. History. Reverend Thomas Bayes (1702-1761) British theologian and mathematician who wrote the basic law of probability (now called Bayes Rule)

By iona
(193 views)

WHY MAKING BAYESIAN NETWORKS BAYESIAN MAKES SENSE.

WHY MAKING BAYESIAN NETWORKS BAYESIAN MAKES SENSE.

WHY MAKING BAYESIAN NETWORKS BAYESIAN MAKES SENSE. Dawn E. Holmes Department of Statistics and Applied Probability University of California, Santa Barbara CA 93106, USA. Subjective Probability. Rational degrees of belief. Keynes ‘s consensual rational degrees of belief.

By artie
(194 views)

AI for Gameplay & Storytelling

AI for Gameplay & Storytelling

AI for Gameplay & Storytelling. Aaron Thibault The Guildhall @ SMU IC2 Institute @ UT-Austin University XXI “Digital Warrior”. 3 Big Topics. 3 X. 12 X. 9 X. 6 X. Artificial Intelligence Gameplay Storytelling. Philosophy Interactivity Messaging. Learning Feedback Loops

By badru
(155 views)

Multicomponent analysis of emotional experience

Multicomponent analysis of emotional experience

Multicomponent analysis of emotional experience. M. Mortillaro University of Milan - Bicocca. emotions as multicomponent processes . Reactions to goal-relevant changes in the environment according to different organismic subsystems, that answer functions reflected in five main components

By barton
(264 views)

Expert Systems and Their Applications

Expert Systems and Their Applications

Expert Systems and Their Applications. John Paxton Montana State University August 14, 2003. Bozeman. Definitions. A model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. (Ignizio)

By sari
(98 views)

Markov Logic: Combining Logic and Probability

Markov Logic: Combining Logic and Probability

Markov Logic: Combining Logic and Probability. Parag Singla Dept. of Computer Science & Engineering Indian Institute of Technology Delhi. Overview. Motivation & Background Markov logic Inference & Learning Abductive Plan Recognition. Social Network and Smoking Behavior. Smoking .

By schuyler
(134 views)

Clustering Beyond K -means

Clustering Beyond K -means

Clustering Beyond K -means. David Kauchak CS 451 – Fall 2013. Administrative. Final project Presentations on Friday 3 minute max 1-2 PowerPoint slides. E-mail me by 9am on Friday What problem you tackled and results Paper and final code submitted on Sunday Final exam next week.

By sivan
(105 views)

Applied Algorithm Lab Wooram Heo

Applied Algorithm Lab Wooram Heo

Toward the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005 . Applied Algorithm Lab Wooram Heo. Outline. Recommemder Systems Problem statement Survey of Recommender systems

By sal
(110 views)

Probabilistic Networks

Probabilistic Networks

Probabilistic Networks. Chapter 14 of Dechter’s CP textbook Speaker: Daniel Geschwender April 1, 2013. Motivation. Hard & soft constraints are known with certainty How to model uncertainty? Probabilistic networks (also belief networks & Bayesian networks) handle uncertainty

By zorana
(126 views)

Accelerating Belief Propagation in Hardware

Accelerating Belief Propagation in Hardware

Accelerating Belief Propagation in Hardware. Skand Hurkat and José Martínez Computer Systems Laboratory Cornell University http ://www.csl.cornell.edu /. The Cornell Team. Prof. José Martínez (PI), Prof. Rajit Manohar @ Computer Systems Lab

By melora
(102 views)

Artificial Intelligence CS 441/541 Fall term, 2011

Artificial Intelligence CS 441/541 Fall term, 2011

Artificial Intelligence CS 441/541 Fall term, 2011. Instructor: Melanie Mitchell (All slides for each class will be available on the course web site before the class). Two-fold nature of AI: Philosophical: “Can machines think, in principle?”

By favian
(144 views)

ELE 523E COMPUTATIONAL NANOELECTRONICS

ELE 523E COMPUTATIONAL NANOELECTRONICS

Mustafa Altun Electronics & Communication Engineering Istanbul Technical University Web: http://www.ecc.itu.edu.tr/. ELE 523E COMPUTATIONAL NANOELECTRONICS. W7-W8:Defects and Reliability, 11/11/2013-18/11/2013. FALL 2013. Outline. Defects in nanoscale Permanent defects

By lew
(123 views)

Intro to Artificial Intelligence CS 171

Intro to Artificial Intelligence CS 171

Intro to Artificial Intelligence CS 171 . Reasoning Under Uncertainty Chapter 13 and 14.1-14.2 Andrew Gelfand 3/1/2011. Today…. Representing uncertainty is useful in knowledge bases Probability provides a coherent framework for uncertainty Review basic concepts in probability

By kedem
(102 views)

Ph.D Milestones

Ph.D Milestones

Ph.D Milestones. Jin Zhao 23 Nov 2012. Timeline. Year. Milestones. Stage. 1. Qualifying Exam. Initial Exploration. Graduate Research Paper (GRP). 2. Topic Formulation. Thesis Proposal. 3. 4. Sub-topic Exploration. 5. 6. Pre-Submission. 7. Wrapping up.

By deo
(118 views)

Latent Tree Models Part IV: Applications

Latent Tree Models Part IV: Applications

AAAI 2014 Tutorial. Latent Tree Models Part IV: Applications. Nevin L. Zhang Dept. of Computer Science & Engineering The Hong Kong Univ. of Sci. & Tech. http://www.cse.ust.hk/~lzhang. Applications of Latent Tree Analysis (LTA). What can LTA be used for:

By theta
(130 views)

Gabriela Moise, Monica Vladoiu, Zoran Constantinescu

Gabriela Moise, Monica Vladoiu, Zoran Constantinescu

GC-MAS - a Multiagent System for Building Creative Groups used in Computer Supported Collaborative Learning. Gabriela Moise, Monica Vladoiu, Zoran Constantinescu . Subject. method for building creative teams, based on unsupervised learning and with support from a multiagent system

By farhani
(111 views)

Bayesian Networks

Bayesian Networks

M.I. Jaime Alfonso Reyes ´Cortés. Bayesian Networks. Es un grafo dirigido acíclico conexo más una distribución de probabilidad sobre sus variables . Red Bayesiana. Existen distintos tipos de redes Bayesianas: Naive Bayes : Redes simples. Forma de “V” => 2^n estados en el nodo inferior

By fay
(136 views)

Evaluating Bayesian networks’ precision for detecting students’ learning styles

Evaluating Bayesian networks’ precision for detecting students’ learning styles

Evaluating Bayesian networks’ precision for detecting students’ learning styles. Andrew Smith Mikhail Simin. Introduction. Learning styles Seeing and hearing; reflecting and acting; etc. Web-based courses Ability to customize content for different students But how? Surveys?

By tariq
(78 views)

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