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C8100 | Measurement uncertainty is critical to risk assessment and decision making. Organizations make decisions every day based on reports containing quantitative measurement data. If measurement results are not accurate, then decision risks increase. Selecting the wrong suppliers, could result in poor product quality. | |
C8101 | Body parts are not used as standard unit of measurement because length of palm and hand are different for different persons which causes error in measurement. | |
C8102 | The independence between inputs means that each input has a different normalization operation, allowing arbitrary mini-batch sizes to be used. The experimental results show that layer normalization performs well for recurrent neural networks. | |
C8103 | The binomial distribution is a common discrete distribution used in statistics, as opposed to a continuous distribution, such as the normal distribution. | |
C8104 | χ2 can be used to test whether two variables are related or independent from one another or to test the goodness-of-fit between an observed distribution and a theoretical distribution of frequencies. | |
C8105 | General linear modeling in SPSS for Windows The general linear model (GLM) is a flexible statistical model that incorporates normally distributed dependent variables and categorical or continuous independent variables. | |
C8106 | Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff. | |
C8107 | Properties of Log Base 2Zero Exponent Rule : loga 1 = 0.Change of Base Rule : logb (x) = ln x / ln b or logb (x) = log10 x / log10 b.Logb b = 1 Example : log22 = 1.Logb bx = x Example : log22x = x. | |
C8108 | Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. | |
C8109 | 5:4711:51Suggested clip · 87 secondsInterpreting the Odds Ratio in Logistic Regression using SPSS YouTubeStart of suggested clipEnd of suggested clip | |
C8110 | A random process is a time-varying function that assigns the outcome of a random experiment to each time instant: X(t). If one scans all possible outcomes of the underlying random experiment, we shall get an ensemble of signals. | |
C8111 | Advantages and disadvantagesAre simple to understand and interpret. Have value even with little hard data. Help determine worst, best and expected values for different scenarios.Use a white box model. Can be combined with other decision techniques. | |
C8112 | Properties, Uses and Limitations of a Dimensional AnalysisTo check the correctness of a physical equation.To derive the relation between different physical quantities involved in a physical phenomenon.To change from one system of units to another. | |
C8113 | Morpheus: If real is what you can feel, smell, taste and see, then 'real' is simply electrical signals interpreted by your brain. | |
C8114 | Validation and Derivation Procedures serve two different purposes. Validation Procedures compare multiple Question responses for the same patient for the purpose of ensuring that patient data is valid. Derivation Procedures use calculations to derive values from collected data. | |
C8115 | Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks. | |
C8116 | Agents can be grouped into five classes based on their degree of perceived intelligence and capability. Model-based reflex agent. Goal-based agents. Utility-based agent. | |
C8117 | K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique. | |
C8118 | In statistics, the method of moments is a method of estimation of population parameters. It starts by expressing the population moments (i.e., the expected values of powers of the random variable under consideration) as functions of the parameters of interest. The solutions are estimates of those parameters. | |
C8119 | In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters. | |
C8120 | Distributional similarity is the idea that the meaning of words can be understood from their context. This should not be confused with the term distributed representation, which refers to the idea of representing information with relatively dense vectors as opposed to a one-hot representation. | |
C8121 | Factorial analysis of variance (ANOVA) is a statistical procedure that allows researchers to explore the influence of two or more independent variables (factors) on a single dependent variable. | |
C8122 | Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence. P(c|x) is the posterior probability of class (target) given predictor (attribute). | |
C8123 | Data from ordinal or nominal (categorical) variables are not properly analyzed using the theory or tests based on the normal distribution. However, it makes no sense to discuss "sex" (a categorical variable) as a normally distributed variable. | |
C8124 | Generally, a machine learning pipeline describes or models your ML process: writing code, releasing it to production, performing data extractions, creating training models, and tuning the algorithm. An ML pipeline should be a continuous process as a team works on their ML platform. | |
C8125 | While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. | |
C8126 | An Inverted file is an index data structure that maps content to its location within a database file, in a document or in a set of documents. The inverted file is the most popular data structure used in document retrieval systems to support full text search. | |
C8127 | Generalized Linear Models (GLMs) The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only). | |
C8128 | Latent Semantic Analysis is a technique for creating a vector representation of a document. This in turn means you can do handy things like classifying documents to determine which of a set of known topics they most likely belong to. | |
C8129 | Basic steps:Assign a number of points to coordinates in n-dimensional space. Calculate Euclidean distances for all pairs of points. Compare the similarity matrix with the original input matrix by evaluating the stress function. Adjust coordinates, if necessary, to minimize stress. | |
C8130 | Brownian motion lies in the intersection of several important classes of processes. It is a Gaussian Markov process, it has continuous paths, it is a process with stationary independent increments (a Lévy process), and it is a martingale. Several characterizations are known based on these properties. | |
C8131 | Ensemble is a machine learning concept in which multiple models are trained using the same learning algorithm. Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. | |
C8132 | Regression analysis is a form of inferential statistics. The p-values help determine whether the relationships that you observe in your sample also exist in the larger population. The p-value for each independent variable tests the null hypothesis that the variable has no correlation with the dependent variable. | |
C8133 | Decision Tree Splitting Method #1: Reduction in VarianceFor each split, individually calculate the variance of each child node.Calculate the variance of each split as the weighted average variance of child nodes.Select the split with the lowest variance.Perform steps 1-3 until completely homogeneous nodes are achieved. | |
C8134 | Machine Learning(ML) generally means that you're training the machine to do something(here, image processing) by providing set of training data's. | |
C8135 | Chaos theory is an interdisciplinary theory stating that, within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization. | |
C8136 | Anthropology definitions The definition of anthropology is the study of various elements of humans, including biology and culture, in order to understand human origin and the evolution of various beliefs and social customs. An example of someone who studies anthropology is Ruth Benedict. | |
C8137 | Use Simple Random Sampling One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal odds for every member of the population to be chosen as a participant in the study at hand. | |
C8138 | Linear Growth Model Organisms generally grow in spurts that are dependent on both environment and genetics. Under controlled laboratory conditions, however, one can often observe a constant rate of growth. These periods of constant growth are often referred to as the linear portions of the growth curve. | |
C8139 | Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data. | |
C8140 | In spatial analysis, four major problems interfere with an accurate estimation of the statistical parameter: the boundary problem, scale problem, pattern problem (or spatial autocorrelation), and modifiable areal unit problem. In analysis with area data, statistics should be interpreted based upon the boundary. | |
C8141 | In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points. It is often required to interpolate, i.e., estimate the value of that function for an intermediate value of the independent variable. | |
C8142 | To get started, you need to identify the two terms from your binomial (the x and y positions of our formula above) and the power (n) you are expanding the binomial to. For example, to expand (2x-3)³, the two terms are 2x and -3 and the power, or n value, is 3. | |
C8143 | John McCarthy | |
C8144 | So in summary, hidden state is overall state of what we have seen so far. Cell state is selective memory of the past. Both these states are trainable with data. | |
C8145 | Nonparametric statistics should be considered when the sample sizes are small and the underlying distribution is not clear. If it is important to detect small effects, one should be very cautious about one's choice of the test statistic. | |
C8146 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C8147 | Bagging (Bootstrap Aggregating) is an ensemble method. First, we create random samples of the training data set (sub sets of training data set). Then, we build a classifier for each sample. Finally, results of these multiple classifiers are combined using average or majority voting. | |
C8148 | The Relationship Between a CDF and a PDF In technical terms, a probability density function (pdf) is the derivative of a cumulative density function (cdf). Futhermore, the area under the curve of a pdf between negative infinity and x is equal to the value of x on the cdf. | |
C8149 | A frequency count is a measure of the number of times that an event occurs. Thus, a relative frequency of 0.50 is equivalent to a percentage of 50%. | |
C8150 | In statistics, a type of probability distribution in which all outcomes are equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same. | |
C8151 | Interval data is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit. Ratio data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. | |
C8152 | Decision tree classifier – Decision tree classifier is a systematic approach for multiclass classification. It poses a set of questions to the dataset (related to its attributes/features). The decision tree classification algorithm can be visualized on a binary tree. | |
C8153 | It is clear that correlated features means that they bring the same information, so it is logical to remove one of them. | |
C8154 | and is commonly used as an estimator for σ. Nevertheless, S is a biased estimator of σ. | |
C8155 | The maximum entropy principle is defined as modeling a given set of data by finding the highest entropy to satisfy the constraints of our prior knowledge. The maximum entropy model is a conditional probability model p(y|x) that allows us to predict class labels given a set of features for a given data point. | |
C8156 | fX(x) dx For fX(x) to be a proper distribution, it must satisfy the following two conditions: 1. The PDF fX(x) is positive-valued; fX(x) ≥ 0 for all values of x ∈ X. 2. The rule of total probability holds; the total area under fX(x) is 1; ∫ | |
C8157 | The coefficients in a Cox regression relate to hazard; a positive coefficient indicates a worse prognosis and a negative coefficient indicates a protective effect of the variable with which it is associated. | |
C8158 | Knowing the number of scores and ranking them in order from lowest to highest, you can use the formula R = P / 100 (N + 1) to calculate the percentile rank. | |
C8159 | A hypothesis test for a population mean when the population standard deviation, σ, is unknown is conducted in the same way as if the population standard deviation is known. The only difference is that the t-distribution is invoked, instead of the standard normal distribution (z-distribution). | |
C8160 | The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). | |
C8161 | The mean value of x is thus the first moment of its distribution, while the fact that the probability distribution is normalized means that the zeroth moment is always 1. The variance of x is thus the second central moment of the probability distribution when xo is the mean value or first moment. | |
C8162 | An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. The matrix on the left contains numbers, between 0 and 255, which each correspond to the brightness of one pixel in a picture of a face. | |
C8163 | A machine learning task is the type of prediction or inference being made, based on the problem or question that is being asked, and the available data. For example, the classification task assigns data to categories, and the clustering task groups data according to similarity. | |
C8164 | Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled. | |
C8165 | Continuous probability functions are also known as probability density functions. You know that you have a continuous distribution if the variable can assume an infinite number of values between any two values. Continuous variables are often measurements on a scale, such as height, weight, and temperature. | |
C8166 | Therefore, the average running time of QUICKSORT on uniformly distributed permutations (random data) and the expected running time of randomized QUICKSORT are both O(n + n lg n) = O(n lg n). This is the same growth rate as merge sort and heap sort. | |
C8167 | Tips for improving deductive reasoning skillsBe curious.Be observational.Increase your knowledge.Break problems into smaller pieces. | |
C8168 | We will use the RAND() function to generate a random value between 0 and 1 on our Y-axis and then get the inverse of it with the NORM. INV function which will result in our random normal value on the X-axis. Mean – This is the mean of the normal distribution. | |
C8169 | more A symbol for a value we don't know yet. It is usually a letter like x or y. Example: in x + 2 = 6, x is the variable. | |
C8170 | The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. In a financial context, the law of large numbers indicates that a large entity which is growing rapidly cannot maintain that growth pace forever. | |
C8171 | In a hypothesis test, we: Evaluate the null hypothesis, typically denoted with H0. The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality (=, ≤ or ≥) | |
C8172 | In every factor analysis, there are the same number of factors as there are variables. The eigenvalue is a measure of how much of the variance of the observed variables a factor explains. Any factor with an eigenvalue ≥1 explains more variance than a single observed variable. | |
C8173 | How Deep Learning Algorithms WorkMultilayer Perceptron Neural Network (MLPNN) Backpropagation. Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) Generative Adversarial Network (GAN) Restricted Boltzmann Machine (RBM) Deep Belief Network (DBN) | |
C8174 | Different performance metrics are used to evaluate different Machine Learning Algorithms. For now, we will be focusing on the ones used for Classification problems. We can use classification performance metrics such as Log-Loss, Accuracy, AUC(Area under Curve) etc. | |
C8175 | Notice that simple linear regression has k=1 predictor variable, so k+1 = 2. Thus, we get the formula for MSE that we introduced in that context of one predictor. S=√MSE S = M S E estimates σ and is known as the regression standard error or the residual standard error. | |
C8176 | Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. | |
C8177 | Maximum sample rate: This parameter needs to be looked at carefully when an ADC's input channels are multiplexed. For ADCs using flash and SAR (successive approximate register) architectures, the sample rate for each channel can be calculated by dividing the specified sample rate by the number of channels. | |
C8178 | Classification Algorithms in Data Mining. It is one of the Data Mining. That is used to analyze a given data set and takes each instance of it. It assigns this instance to a particular class. So classification is the process to assign class label from a data set whose class label is unknown. | |
C8179 | Graphically, the p value is the area in the tail of a probability distribution. It's calculated when you run hypothesis test and is the area to the right of the test statistic (if you're running a two-tailed test, it's the area to the left and to the right). | |
C8180 | An offset variable is one that is treated like a regression covariate whose parameter is fixed to be 1.0. Offset variables are most often used to scale the modeling of the mean in Poisson regression situations with a log link. | |
C8181 | An IQ (Intelligence Quotient) score from a standardized test of intelligences is a good example of an interval scale score. IQ scores are created so that a score of 100 represents the average IQ of the population and the standard deviation (or average variability) of scores is 15. | |
C8182 | Accuracy reflects how close a measurement is to a known or accepted value, while precision reflects how reproducible measurements are, even if they are far from the accepted value. Measurements that are both precise and accurate are repeatable and very close to true values. | |
C8183 | Definition. A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied. | |
C8184 | There are basically two methods to reduce autocorrelation, of which the first one is most important:Improve model fit. Try to capture structure in the data in the model. If no more predictors can be added, include an AR1 model. | |
C8185 | The Kolmogorov-Smirnov test (K-S) and Shapiro-Wilk (S-W) test are designed to test normality by comparing your data to a normal distribution with the same mean and standard deviation of your sample. If the test is NOT significant, then the data are normal, so any value above . 05 indicates normality. | |
C8186 | The correlation structure between the dependent variables provides additional information to the model which gives MANOVA the following enhanced capabilities: Greater statistical power: When the dependent variables are correlated, MANOVA can identify effects that are smaller than those that regular ANOVA can find. | |
C8187 | The only difference between Greedy BFS and A* BFS is in the evaluation function. For Greedy BFS the evaluation function is f(n) = h(n) while for A* the evaluation function is f(n) = g(n) + h(n). | |
C8188 | Meta-learning, also known as “learning to learn”, intends to design models that can learn new skills or adapt to new environments rapidly with a few training examples. Humans, in contrast, learn new concepts and skills much faster and more efficiently. | |
C8189 | Any study that attempts to predict human behavior will tend to have R-squared values less than 50%. However, if you analyze a physical process and have very good measurements, you might expect R-squared values over 90%. | |
C8190 | Principal component analysis aims at reducing a large set of variables to a small set that still contains most of the information in the large set. The technique of principal component analysis enables us to create and use a reduced set of variables, which are called principal factors. | |
C8191 | The role of sigma in the Gaussian filter is to control the variation around its mean value. So as the Sigma becomes larger the more variance allowed around mean and as the Sigma becomes smaller the less variance allowed around mean. it simply means that we apply a kernel on every pixel in the image. | |
C8192 | The area under the graph is the definite integral. By definition, definite integral is the sum of the product of the lengths of intervals and the height of the function that is being integrated with that interval, which includes the formula of the area of the rectangle. The figure given below illustrates it. | |
C8193 | In OLS regression, a linear relationship between the dependent and independent variable is a must, but in logistic regression, one does not assume such things. The relationship between the dependent and independent variable may be linear or non-linear. | |
C8194 | The amount that the weights are updated during training is referred to as the step size or the “learning rate.” Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. | |
C8195 | Word2Vec takes texts as training data for a neural network. The resulting embedding captures whether words appear in similar contexts. GloVe focuses on words co-occurrences over the whole corpus. Its embeddings relate to the probabilities that two words appear together. | |
C8196 | The desired precision of the estimate (also sometimes called the allowable or acceptable error in the estimate) is half the width of the desired confidence interval. For example if you would like the confidence interval width to be about 0.1 (10%) you would enter a precision of +/- 0.05 (5%). | |
C8197 | In statistics, the likelihood-ratio test assesses the goodness of fit of two competing statistical models based on the ratio of their likelihoods, specifically one found by maximization over the entire parameter space and another found after imposing some constraint. | |
C8198 | Some of the methods commonly used for binary classification are:Decision trees.Random forests.Bayesian networks.Support vector machines.Neural networks.Logistic regression.Probit model. | |
C8199 | Sparse coding is the representation of items by the strong activation of a relatively small set of neurons. For each stimulus, this is a different subset of all available neurons. |
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