'Discrete values' presentation slideshows

Logic Circuits and Computer Architecture

Logic Circuits and Computer Architecture. Appendix A Digital Logic Circuits Part 1: Combinational Circuits and Minimization. Discrete. Discrete. Information. Inputs. Discrete. Processing. Outputs. System. System State. Digital System.

1.62k views • 142 slides

Introduction to Machine Learning

Introduction to Machine Learning. Learning. Agent has made observations ( data ) Now must make sense of it ( hypotheses ) Hypotheses alone may be important (e.g., in basic science) For inference (e.g., forecasting) To take sensible actions (decision making)

1.4k views • 70 slides

Chapter 1 Digital Systems and Binary Numbers

Digital Logic Design . Chapter 1 Digital Systems and Binary Numbers . CSE-221 Lectures: 3 hours/week Digital Logic Design Credits: 3

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Chapter 3

Chapter 3. CONSTRAINT SATISFACTION PROBLEMS. Outline. What is a CSP? Posing a CSP Generate-and-test-algorithms Standard backtracking algorithm Consistency algorithm Look-ahead Schemes. What is a CSP?. In constraint satisfaction problems (CSPs), we are given

687 views • 55 slides

Physical Layer: Data and Signals

Physical Layer: Data and Signals. 01204325: Data Communication and Computer Networks. Asst. Prof. Chaiporn Jaikaeo, Ph.D. chaiporn.j@ku.ac.th http://www.cpe.ku.ac.th/~cpj Computer Engineering Department Kasetsart University, Bangkok, Thailand. Outline. Analog and digital data/signals

619 views • 52 slides

Features of JavaBeans

Features of JavaBeans. Support for introspection so that a builder tool can analyze how a bean works Support for customization so that when using an application builder a user can customize the appearance and behavior of a bean Support for events

657 views • 51 slides

?14 ??????????? ????????? ?????? ??? ????

?14 ??????????? ????????? ?????? ??? ????. Network Measurements. Measuring Networks. Degree distributions and power-laws Clustering Coefficient Small world phenomena Components Motifs Homophily. The basic random graph model.

648 views • 50 slides

Discrete and Continuous Distributions

Discrete and Continuous Distributions. G. V. Narayanan. Discrete Probability Distributions. Bernoulli Probability Function Binomial Probability Function HyperGeometric Probability Function Poisson Probability Function. Continuous Probability Distributions. Uniform Probability Function

727 views • 46 slides

Chapter 1 Digital Systems and Numbers System

787 views • 41 slides

Posterior, likelihood, evidence P( ? j | x) = P(x | ? j ) . P ( ? j ) / P(x) Where in case of two categories Posterio

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Posterior, likelihood, evidence

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Strips trigger rate Issues & Can the strips achieve the following proposed trigger parameters

Strips trigger rate Issues & Can the strips achieve the following proposed trigger parameters (proposed following Stanford AUW)?. L0 accept rate: 500 kHz L0 latency: ~ 5-6 m s L1 accept rate: 200 kHz L1 latency: 20 m s. L1 trigger rate capability will depend crucially on expected pileup.

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Grid Generation and Post-Processing for Computational Fluid Dynamics (CFD)

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Introduction to Classification of Remotely Sensed Imagery

489 views • 32 slides

6.1

6.1. Discrete Random Variables. Random Variables. A random variable is a numeric measure of the outcome of a probability experiment Random variables reflect measurements that can change as the experiment is repeated

534 views • 31 slides

Quantum and classical computing

Quantum and classical computing. EECS FER 16.9.2003. Dalibor H RG. How to think?. Review / Classical computing. Classical computing :

659 views • 29 slides

Lecture II-2: Probability Review

Lecture II-2: Probability Review. Lecture Outline: Random variables and probability distributions Functions of a random variable, moments Multivariate probability Marginal and conditional probabilities and moments Multivariate normal distributions

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CSCI 3202: Introduction to AI Decision Trees

CSCI 3202: Introduction to AI Decision Trees. Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell). Outline. Decision Tree Representations ID3 and C4.5 learning algorithms (Quinlan 1986) CART learning algorithm (Breiman et al. 1985) Entropy, Information Gain Overfitting.

389 views • 22 slides

Neural networks for genetic epidemiology: past, present, and future

Neural networks for genetic epidemiology: past, present, and future. Alison A Motsinger-Reif and Marylyn D Ritchie. 2008 July 17. Motivation. Developing new more effective methods for computational analysis of the huge amounts of data that recently became available.

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Empirical Distributions

Empirical Distributions. Empirical Distribution . Using actual data to construct your own function for generation of data for your simulation. Empirical Distribution . Discrete or Continuous Used when any random variable does not fit any known distribution We use the cumulative distribution.

392 views • 7 slides