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Andrew ng machine learning notes pdf

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Andrew ng machine learning notes pdf

Each year, data are obtained from a nationally representative sample of about , interviews on criminal victimization, involving , unique persons in about 95, households. Persons are interviewed on the frequency, characteristics, and consequences of criminal victimization in the United States. The NCVS collects information on nonfatal personal crimes i. Survey respondents provide information about themselves e. For each victimization incident, the NCVS collects information about the offender e.

Data Experts Erika Harrell, Ph. Collection Period In addition to providing annual level and change estimates on criminal victimization, the NCVS is the primary source of information on the nature of criminal victimization incidents.

The NCVS collects information for each victimization incident about the offender e. The NCVS is administered to persons age 12 or older from a nationally representative sample of households in the United States.

The NCVS defines a household as a group of persons who all reside at a sampled address. Persons are considered household members when the sampled address is their usual place of residence at the time of the interview and when they have no usual place of residence elsewhere. First interviews are typically conducted in person with subsequent interviews conducted either in person or by phone.

The sample includes persons living in group quarters e. Victimizations that occurred outside of the United States were excluded from this report.

Estimates in NCVS reports generally use data from the to NCVS data files, weighted to produce annual estimates of victimization for persons age 12 or older living in U.

NCVS data files include person, household, victimization, and incident weights. Person weights provide an estimate of the population represented by each person in the sample. Household weights provide an estimate of the U. After proper adjustment, both household and person weights are also typically used to form the denominator in calculations of crime rates. For personal crimes, the incident weight is derived by dividing the person weight of a victim by the total number of persons victimized during an incident as reported by the respondent.

For property crimes, the incident weight and the household weight are the same because the victim of a property crime is considered to be the household as a whole. The incident weight is most frequently used to calculate estimates of the number of crimes committed against a particular class of victim. Victimization weights used in these analyses account for the number of persons victimized during an incident and for high-frequency repeat victimizations i.

Series victimizations are similar in type but occur with such frequency that a victim is unable to recall each individual event or describe each event in detail. Survey procedures allow NCVS interviewers to identify and classify these similar victimizations as series victimizations and to collect detailed information on only the most recent incident in the series. The weighting counts series victimizations as the actual number of victimizations reported by the victim, up to a maximum of Doing so produces more reliable estimates of crime levels than only counting such victimizations once, while the cap at 10 minimizes the effect of extreme outliers on rates.

According to the data, series victimizations accounted for 1. Standard error computations When national estimates are derived from a sample, as with the NCVS, caution must be used when comparing one estimate to another or when comparing estimates over time. Although one estimate may be larger than another, estimates based on a sample have some degree of sampling error.

The sampling error of an estimate depends on several factors, including the amount of variation in the responses and the size of the sample.

When the sampling error around an estimate is taken into account, estimates that appear different may not be statistically different. One measure of the sampling error associated with an estimate is the standard error.

The standard error may vary from one estimate to the next. Generally, an estimate with a small standard error provides a more reliable approximation of the true value than an estimate with a large standard error. Estimates with relatively larger standard errors are associated with less precision and reliability and should be interpreted with caution.

Generalized variance function GVF parameters and direct variance estimation methods were used to generate standard errors for each point estimate e. To generate standard errors around prevalence estimates, BJS used direct variance estimation methods. The GVFs and direct variance estimation methods take into account aspects of the NCVS complex sample design and represent the curve fitted to a selection of individual standard errors based on the Balanced Repeated Replication BRR technique.

BJS conducted statistical tests to determine whether differences in estimated numbers, percentages, and rates in these reports were statistically significant once sampling error was taken into account.

Using statistical analysis programs developed specifically for the NCVS, all comparisons in the text were tested for significance. The primary test procedure was the Student's t-statistic, which tests the difference between two sample estimates. Unless otherwise noted, the findings described in these reports as higher, lower, or different passed a test at the 0.

Readers should reference figures and tables in these reports for testing on specific findings. Caution is required when comparing estimates not explicitly discussed in these reports. Readers may use the estimates and standard errors of the estimates provided in these reports to generate a confidence interval around the estimate as a measure of the margin of error.

The following example illustrates how standard errors may be used to generate confidence intervals:. Based on the NCVS, the rate of violent victimization reported to police, excluding simple assault, in was 3.

For these reports, BJS also calculated a coefficient of variation CV for all estimates, representing the ratio of the standard error to the estimate. CVs provide another measure of reliability and a means for comparing the precision of estimates across measures with differing levels or metrics.

The NCVS weights include a new adjustment to control household weights to independent housing unit totals available internally within the U. Census Bureau. This new adjustment was applied only to household weights for housing units and does not affect person weights.

Historically, the household weights were controlled to independent totals of the person population. This new weighting adjustment improves upon the historical one and better aligns the number of estimated households in the NCVS with other Census household survey estimates. When making comparisons of property crime at the household level between and prior years, compare victimization or prevalence rates, which are unaffected by this change in weighting methodology because both the numerator and denominator are equally affected.

Comparisons of the number of households that were victimized between and prior years are inappropriate due to this change in weighting methodology. Property crime measured at the person level is unaffected by the change as presented in measures of serious crime.

Methodological changes to the NCVS in Methodological changes implemented in , including the decennial sample redesign that also occurred in , may have affected the crime estimates for that year to such an extent that they are not comparable to estimates from other years. Census Bureau found a high degree of confidence that estimates for through are consistent with and comparable to estimates for and previous years. To permit cross-year comparisons that were inhibited by the sample redesign, BJS created a revised data file.

Estimates for are based on the revised file and replace previously published estimates. Facebook page. Tweets by BJSgov. Total correctional population. Local jail inmates and jail facilities. State and federal prisoners and prison facilities.

Special populations. Community Corrections Probation and Parole. Capital Punishment. State Court Organization. State Court Caseload Statistics. Prosecutors Offices. Indigent Defense Systems. Tribal courts. Criminal Cases. Civil cases. Civil Rights. Crime Type. Violent Crime. Property Crime. White Collar Crime. Drugs and crime. Hate Crime. Identity Theft. Weapon Use. Criminal Justice Data Improvement Program. National Criminal History Improvement Program.

State Justice Statistics Program. Employment and Expenditure. Law Enforcement. Indian Country Justice Statistics. Local Police.

Sheriffs' Offices. Federal Law Enforcement. Tribal Law Enforcement. Campus Law Enforcement. Law Enforcement Training Academies.

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Approximation models have recently been introduced to differential evolution DE to reduce expensive fitness evaluation in function optimization. Depending on the choice of the additional parameters, the strategies may have different levels of efficiency. The present paper introduces an alternative way for reducing function evaluations in differential evolution, which does not require additional control parameter and external archive. The algorithm uses a nearest neighbor in the search population to judge whether a new point is worth evaluating, so that unnecessary evaluations can be avoided. The performance of this new scheme of differential evolution, known as differential evolution with nearest neighbor comparison DE-NNC , is demonstrated and compared with that of standard DE as well as approximation models including differential evolution using k-nearest neighbor predictor DE-kNN , differential evolution using speeded-up k-nearest neighbor estimator DE-EkNN and DE with estimated comparison method through some test functions.

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The federation is composed of the union of the 26 states and the Federal District. It is the largest country to have Portuguese as an official language and the only one in the Americas ; [13] [14] it is also one of the most multicultural and ethnically diverse nations, due to over a century of mass immigration from around the world ; [15] as well as the most populous Roman Catholic-majority country. Brazil remained a Portuguese colony until when the capital of the empire was transferred from Lisbon to Rio de Janeiro. In , the colony was elevated to the rank of kingdom upon the formation of the United Kingdom of Portugal, Brazil and the Algarves.

Introduction

Each year, data are obtained from a nationally representative sample of about , interviews on criminal victimization, involving , unique persons in about 95, households. Persons are interviewed on the frequency, characteristics, and consequences of criminal victimization in the United States. The NCVS collects information on nonfatal personal crimes i. Survey respondents provide information about themselves e. For each victimization incident, the NCVS collects information about the offender e.

Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. Andrew Ng is Chief Scientist at Baidu. The pdf of the book can be provided once the milestone has been set. Here, I am sharing my solutions for the weekly assignments throughout the course. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld.

This species has been frequently introduced to be used as an ornamental and medicinal herb. It has escaped from cultivation, becoming widely naturalized in tro It has escaped from cultivation, becoming widely naturalized in tropical and subtropical regions where it grows as a weed Smith, ; Barrett and Shore; ; Wagner et al. Invasiveness in T. Seed dispersal in this species is by ants which transport seeds relatively short distances. This local seed dispersion favours the establishment of dense populations and increases the likelihood of seed set in this species Barrett, ; Barrett and Shore,

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 В чем же проблема? - Джабба сделал глоток своей жгучей приправы. - Передо мной лежит отчет, из которого следует, что ТРАНСТЕКСТ бьется над каким-то файлом уже восемнадцать часов и до сих пор не вскрыл шифр. Джабба обильно полил приправой кусок пирога на тарелке.

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