'Possible assignments' presentation slideshows

NMR Structure Determination

NMR Structure Determination . With The NMR Assignments and Molecular Modeling Tools in Hand: All we need are the experimental constraints Distance constraints between atoms is the primary structure determination factor. Dihedral angles are also an important structural constraint .

1.05k views • 69 slides

CSPs

CSPs. Tamara Berg CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Svetlana Lazebnik , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke Zettlemoyer. Review from last class. What is search for?.

835 views • 66 slides

Clustering Techniques K-Means & EM

Clustering Techniques K-Means & EM. Mario Haddad. What is Clustering?. Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Clustering is unsupervised classification : no predefined classes Typical applications

720 views • 56 slides

Classical Methods for Object Recognition

690 views • 56 slides

New Spectroscopy of Heavy Mesons

New Spectroscopy of Heavy Mesons. Fulvia De Fazio INFN - Bari. How to ( try to ) understand the properties of recently discovered states using the heavy quark limit. Collaborators : P. Colangelo , R. Ferrandes , S. Nicotri , A. Ozpineci , M. Rizzi. Open charm/beauty states.

564 views • 47 slides

Machine Learning and Review

Machine Learning and Review Reading: C. 18 Bayesian Approach Each observed training example can incrementally decrease or increase probability of hypothesis instead of eliminate an hypothesis Prior knowledge can be combined with observed data to determine hypothesis

935 views • 44 slides

Machine Learning and Review

Machine Learning and Review. Reading: C. 18. Bayesian Approach. Each observed training example can incrementally decrease or increase probability of hypothesis instead of eliminate an hypothesis Prior knowledge can be combined with observed data to determine hypothesis

609 views • 44 slides

CSCE 2100: Computing Foundations 1 Combinatorics

615 views • 43 slides

Chapter 8 The principle of Inclusion and Exclusion

Chapter 8 The principle of Inclusion and Exclusion. Yen-Liang Chen Dept of Information Management National Central University. 8.1 The principle of Inclusion and Exclusion. Four sets.

833 views • 35 slides

Continuous Model Synthesis

Continuous Model Synthesis. Paul Merrell and Dinesh Manocha In SIGGRAPH Asia 2008 ? ? : ???. Abstract. Input: 3D polyhedral model Exploits the connectivity between the adjacent boundary features of the input model Output: A model that has similar connected features

456 views • 35 slides

Computational Logic and Cognitive Science: An Overview

496 views • 35 slides

Clustering

665 views • 34 slides

Inferring phylogenetic trees: Maximum likelihood methods

Inferring phylogenetic trees: Maximum likelihood methods. Prof. William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington thabangh@gmail.com. One-minute responses. First part of class was fine. I am struggling with Python.

1.08k views • 26 slides

1,2,3...QAP!

1,2,3...QAP!. Matteo Fischetti (joint work with Michele Monaci and Domenico Salvagnin ) DEI University of Padova. IPDU, Newcastle, July 2011. QAP definition. complete directed graph G=(V,A) and n=|V| facilities to be assigned to its nodes

408 views • 25 slides

Do you want to know what is inside a chip? Vloer 3: THE place to be!!! Leerstoel HC Groep IDR

Do you want to know what is inside a chip? Vloer 3: THE place to be!!! Leerstoel HC Groep IDR. chip. IC-technology, devices and reliability. IC-technology, devices and reliability. Dielectrics Reliability of very thin dielectrics Novel materials to replace SiO 2

426 views • 21 slides

Max-Margin Markov Networks by Ben Taskar, Carlos Guestrin, and Daphne Koller

Max-Margin Markov Networks by Ben Taskar, Carlos Guestrin, and Daphne Koller. Presented by Michael Cafarella CSE574 May 25, 2005. Introduction. Kernel methods (SVMs) and max-margin are terrific for classification No way to model structure, relations

366 views • 17 slides

ANOVA With More Than One IV

ANOVA With More Than One IV. 2-way ANOVA. So far, 1-Way ANOVA, but can have 2 or more IVs. IVs aka Factors . Example: Study aids for exam IV 1: workbook or not IV 2: 1 cup of coffee or not. Main Effects. Main Effects and Interactions.

334 views • 16 slides

N-queens problem

649 views • 5 slides