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Presented by Sam Clifford at the 2018 UseR conference, Brisbane, Australia. The talk describes the design of SEB113 - Quantitative Methods in Science, a first year statistics/mathematics unit in the Bachelor of Science at Queensland University of Technology. The unit uses RStudio and the tidyverse packages to give students the skills to do meaningful data manipulation and analysis without relying on prior knowledge of advanced mathematics.

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2.mathematics for machine learning

This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.

Principal Component Analysis

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data while retaining most of the variation in the data. It works by transforming the data to a new basis of orthogonal principal components ordered by variance. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA involves calculating the covariance matrix of the data and finding its eigenvectors, which are used as the directions of the new basis. Projecting the data onto this basis gives the principal components.

Naïve Bayes Machine Learning Classification with R Programming: A case study ...

Naïve Bayes Machine Learning Classification with R Programming: A case study ...SubmissionResearchpa

This analytical review paper clearly explains Naïve Bayes machine learning techniques for simple probabilistic classification based on bayes theorem with the assumption of independence between the characteristics using r programming. Although there is large gap between which algorithm is suitable for data analysis when there was large categorical variable to be predict the value in research data. The model is trained in the training data set to make predictions on the test data sets for the implementation of the Naïve Bayes classification. The uniqueness of the technique is that gets new information and tries to make a better forecast by considering the new evidence when the input variable is of largely categorical in nature that is quite similar to how our human mind works while selecting proper judgement from various alternative of choices and can be applied in the neuronal network of the human brain does using r programming. Here researcher takes binary.csv data sets of 400 observations of 4 dependent attributes of educational data sets. Admit is dependent variable of gre, score gpa and rank of previous grade which ultimately determine whether student will be admitted or not for next program. Initially the gra and gpa variables has 0.36 percent significant in the association with rank categorical variable. The box plot and density plot demonstrate the data overlap between admitted and not admitted data sets. The naïve Bayes classification model classify the large data with 0.68 percent for not admitted where as 0.31 percent were admitted. The confusion matrix, and the prediction were calculated with 0.68 percent accuracy when 95 percent confidence interval. Similarly, the training accuracy is increased from 29 percent to 32 percent when naïve Bayes algorithm method as use kernel is equal to TRUE that ultimately decrease misclassification errors in the binary data sets. Yagyanath Rimal. (2019). Naïve Bayes Machine Learning Classification with R Programming: A case study of binary data sets . International Journal on Orange Technologies, 1(2), 27-34. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/358 Pdf Url: https://journals.researchparks.org/index.php/IJOT/article/view/358/347 Paper Url: https://journals.researchparks.org/index.php/IJOT/article/view/358
Off policy evaluation

The document discusses off-policy evaluation techniques for estimating the value of a target policy using data collected from a different behavior policy. It provides an overview of contextual bandit and reinforcement learning settings for off-policy evaluation. Several important estimators are discussed, including importance sampling, direct method, augmented importance sampling, and doubly robust estimators. The document also discusses semiparametric efficiency bounds and how various estimators can achieve these bounds.

Off policy learning

This document provides an overview of off-policy learning. The goal of off-policy learning is to find the optimal policy that maximizes expected outcomes based on historical data where the actions may not match the policy being evaluated. Several approaches to off-policy learning are discussed, including importance weighting, regression of treatment effects, and efficient policy learning with variance reductions. Many open questions are also outlined regarding off-policy learning with complex longitudinal data or unmeasured variables.

Linear Programming Problems with Icosikaipentagonal Fuzzy Number

The objective of this paper is to introduce a new fuzzy number with twenty five points called as Icosikaipentagonal fuzzy number. In which Fuzzy numbers develop a membership function where there are no limitations of any specified form. The aim of this paper is to define Icosikaipentagonal fuzzy number and its some arithmetic operations. Fuzzy Linear Programming problem is one of the active research areas in optimization. Many real world problems are modelled as Fuzzy Linear Programming Problems. Icosikaipentagonal fuzzy number proposed a ranking function to solve fuzzy linear programming problems. Dr. S. Paulraj | G. Tamilarasi "Linear Programming Problems with Icosikaipentagonal Fuzzy Number" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31357.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31357/linear-programming-problems-with-icosikaipentagonal-fuzzy-number/dr-s-paulraj

Explanable models for time series with random forest

Nathalie Vialaneix et Rémi Servien
First PhenoDyn meeting
November 29-30, 2021
Montpellier SupAgro

MATH 107 Great Stories /newtonhelp.com

For more course tutorials visit
www.newtonhelp.com
Curve-fitting Project - Linear Regression Model
A. Summary For this assignment you will be collecting data which exhibits a relatively linear trend, finding the line of best fit, plotting the data and the line, interpreting the slope, and using the linear equation to make

2.mathematics for machine learning

This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.

Principal Component Analysis

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data while retaining most of the variation in the data. It works by transforming the data to a new basis of orthogonal principal components ordered by variance. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA involves calculating the covariance matrix of the data and finding its eigenvectors, which are used as the directions of the new basis. Projecting the data onto this basis gives the principal components.

Naïve Bayes Machine Learning Classification with R Programming: A case study ...

Naïve Bayes Machine Learning Classification with R Programming: A case study ...SubmissionResearchpa

This analytical review paper clearly explains Naïve Bayes machine learning techniques for simple probabilistic classification based on bayes theorem with the assumption of independence between the characteristics using r programming. Although there is large gap between which algorithm is suitable for data analysis when there was large categorical variable to be predict the value in research data. The model is trained in the training data set to make predictions on the test data sets for the implementation of the Naïve Bayes classification. The uniqueness of the technique is that gets new information and tries to make a better forecast by considering the new evidence when the input variable is of largely categorical in nature that is quite similar to how our human mind works while selecting proper judgement from various alternative of choices and can be applied in the neuronal network of the human brain does using r programming. Here researcher takes binary.csv data sets of 400 observations of 4 dependent attributes of educational data sets. Admit is dependent variable of gre, score gpa and rank of previous grade which ultimately determine whether student will be admitted or not for next program. Initially the gra and gpa variables has 0.36 percent significant in the association with rank categorical variable. The box plot and density plot demonstrate the data overlap between admitted and not admitted data sets. The naïve Bayes classification model classify the large data with 0.68 percent for not admitted where as 0.31 percent were admitted. The confusion matrix, and the prediction were calculated with 0.68 percent accuracy when 95 percent confidence interval. Similarly, the training accuracy is increased from 29 percent to 32 percent when naïve Bayes algorithm method as use kernel is equal to TRUE that ultimately decrease misclassification errors in the binary data sets. Yagyanath Rimal. (2019). Naïve Bayes Machine Learning Classification with R Programming: A case study of binary data sets . International Journal on Orange Technologies, 1(2), 27-34. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/358 Pdf Url: https://journals.researchparks.org/index.php/IJOT/article/view/358/347 Paper Url: https://journals.researchparks.org/index.php/IJOT/article/view/358
Off policy evaluation

The document discusses off-policy evaluation techniques for estimating the value of a target policy using data collected from a different behavior policy. It provides an overview of contextual bandit and reinforcement learning settings for off-policy evaluation. Several important estimators are discussed, including importance sampling, direct method, augmented importance sampling, and doubly robust estimators. The document also discusses semiparametric efficiency bounds and how various estimators can achieve these bounds.

Off policy learning

This document provides an overview of off-policy learning. The goal of off-policy learning is to find the optimal policy that maximizes expected outcomes based on historical data where the actions may not match the policy being evaluated. Several approaches to off-policy learning are discussed, including importance weighting, regression of treatment effects, and efficient policy learning with variance reductions. Many open questions are also outlined regarding off-policy learning with complex longitudinal data or unmeasured variables.

Linear Programming Problems with Icosikaipentagonal Fuzzy Number

The objective of this paper is to introduce a new fuzzy number with twenty five points called as Icosikaipentagonal fuzzy number. In which Fuzzy numbers develop a membership function where there are no limitations of any specified form. The aim of this paper is to define Icosikaipentagonal fuzzy number and its some arithmetic operations. Fuzzy Linear Programming problem is one of the active research areas in optimization. Many real world problems are modelled as Fuzzy Linear Programming Problems. Icosikaipentagonal fuzzy number proposed a ranking function to solve fuzzy linear programming problems. Dr. S. Paulraj | G. Tamilarasi "Linear Programming Problems with Icosikaipentagonal Fuzzy Number" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31357.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31357/linear-programming-problems-with-icosikaipentagonal-fuzzy-number/dr-s-paulraj

Explanable models for time series with random forest

Nathalie Vialaneix et Rémi Servien
First PhenoDyn meeting
November 29-30, 2021
Montpellier SupAgro

MATH 107 Great Stories /newtonhelp.com

For more course tutorials visit
www.newtonhelp.com
Curve-fitting Project - Linear Regression Model
A. Summary For this assignment you will be collecting data which exhibits a relatively linear trend, finding the line of best fit, plotting the data and the line, interpreting the slope, and using the linear equation to make

Data Structures 2004

This document contains lecture notes from a data structures course taught by Sanjay Goel at JIIT in 2004. The notes summarize 7 lectures that covered topics like what is engineering, representing real-world objects in computer memory using data structures, designing data structures for storing polynomials and number sequences, and algorithms for operations like deleting elements from linked lists and updating matching indices between collections. The lectures included in-class exercises, questions, and programming assignments to reinforce the concepts through practical examples and problems.

Pca

PCA (Principal Component Analysis) is a technique used to simplify complex data sets by reducing their dimensionality. It transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides background on concepts like variance, covariance, and eigenvalues that are important to understanding PCA. It also includes an example of using PCA to analyze student data and identify the most important parameters to describe students.

Graph Neural Network for Phenotype Prediction

This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.

Spsshelp 100608163328-phpapp01

This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.

Solving dynamics problems with matlab

This document provides an introduction to solving dynamics problems using MATLAB. It discusses numerical calculations, writing scripts, defining functions, graphics, symbolic calculations, differentiation, integration, and solving equations in MATLAB. It also contains sample dynamics problems from the textbook Engineering Mechanics: Dynamics that are solved using MATLAB. The problems cover kinematics and kinetics of particles and rigid bodies, as well as vibration analysis. The goal is to illustrate how computational tools like MATLAB can be used to aid in learning dynamics, but only after mastering the fundamentals through analytical problem solving.

Differences-in-Differences

The document discusses differences-in-differences (DID) estimation and provides examples of its application. It first reviews DID and its identifying assumptions. It then analyzes a study that uses DID to examine whether governments allocate more resources to councils controlled by their party. Next, it uses DID graphs and linear regression models to estimate the causal effects of electronic voting on voter turnout and spoiled votes in Kyoto, Japan. Standard errors in DID estimation and the synthetic control method are also briefly discussed.

PCA

Principal Component Analysis (PCA) is a technique used to simplify complex data sets by identifying patterns in the data and expressing it in such a way to highlight similarities and differences. It works by subtracting the mean from the data, calculating the covariance matrix, and determining the eigenvectors and eigenvalues to form a feature vector representing the data in a lower dimensional space. PCA can be used to represent image data as a one dimensional vector by stacking the pixel rows of an image and applying this analysis to multiple images.

Principal component analysis and lda

PCA and LDA are dimensionality reduction techniques. PCA transforms variables into uncorrelated principal components while maximizing variance. It is unsupervised. LDA finds axes that maximize separation between classes while minimizing within-class variance. It is supervised and finds axes that separate classes well. The document provides mathematical explanations of how PCA and LDA work including calculating covariance matrices, eigenvalues, eigenvectors, and transformations.

La statistique et le machine learning pour l'intégration de données de la bio...

This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.

Intuition – Based Teaching Mathematics for Engineers

It is suggested to teach Mathematics for engineers
based on development of mathematical intuition, thus, combining
conceptual and operational approaches. It is proposed to teach
main mathematical concepts based on discussion of carefully
selected case studies following solving of algorithmically generated
problems to help mastering appropriate mathematical tools.
The former component helps development of mathematical intuition;
the latter applies means of adaptive instructional technology
to improvement of operational skills. Proposed approach is applied
to teaching uniform convergence and to knowledge generation
using Computer Science object-oriented methodology.

ALTERNATIVE METHOD TO LINEAR CONGRUENCE

This document presents an algebraic algorithm for solving linear congruences and applies it to cryptography. It begins with background information on linear congruences and number theory. It then describes developing an algebraic algorithm that converts linear congruences into linear equations to solve them algebraically. This is a simpler approach than existing methods. Examples are provided to validate the algorithm. The paper also demonstrates applying the algorithm to solve linear congruences involved in the RSA public key cryptography system.

Reproducibility and differential analysis with selfish

Selfish is a Python tool for identifying differentially interacting chromatin regions from Hi-C contact maps of two conditions with no replicates. It begins by distance-correcting the interaction frequencies. It then computes Gaussian filters over neighboring bins to capture spatial dependencies. It compares the evolution of these filters between conditions and assigns p-values assuming Gaussian differences. Selfish is faster than existing methods and shows enrichment for epigenetic markers near differential regions. However, its statistical justification could be improved as it does not model overdispersion like other methods.

Kernel methods for data integration in systems biology

Séminaire Key Initiative for Data Science, Montpellier https://muse.edu.umontpellier.fr/key-initiatives-muse/data-life-sciences/
18 octobre 2019

Principal Component Analysis and Clustering

Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components

Lect4 principal component analysis-I

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data by transforming it to a new coordinate system. It works by finding the principal components - linear combinations of variables with the highest variance - and using those to project the data to a lower dimensional space. PCA is useful for visualizing high-dimensional data, reducing dimensions without much loss of information, and finding patterns. It involves calculating the covariance matrix and solving the eigenvalue problem to determine the principal components.

Chap011

This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.

Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...

This presentation is about Grouping and Displaying Data to Convey Meaning: Tables and Graphs
Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.

Presentation1

This document discusses innovative practices in teaching discrete structures/mathematics and data structures to undergraduate computer science students. It describes course structures at various universities and suggests focusing discrete mathematics on fundamental concepts like logic, proofs, and counting before more advanced topics. For data structures, it recommends teaching implementation to build understanding but also focusing on usage. Projects, multimedia, and games are presented as motivating teaching techniques. Historical sources are proposed to provide context for abstract concepts.

Selective inference and single-cell differential analysis

This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.

Decision Tree Algorithm Implementation Using Educational Data

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

Machine learning ppt unit one syllabuspptx

This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.

Machine Learning: Foundations Course Number 0368403401

This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.

Data Structures 2004

This document contains lecture notes from a data structures course taught by Sanjay Goel at JIIT in 2004. The notes summarize 7 lectures that covered topics like what is engineering, representing real-world objects in computer memory using data structures, designing data structures for storing polynomials and number sequences, and algorithms for operations like deleting elements from linked lists and updating matching indices between collections. The lectures included in-class exercises, questions, and programming assignments to reinforce the concepts through practical examples and problems.

Pca

PCA (Principal Component Analysis) is a technique used to simplify complex data sets by reducing their dimensionality. It transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. The document provides background on concepts like variance, covariance, and eigenvalues that are important to understanding PCA. It also includes an example of using PCA to analyze student data and identify the most important parameters to describe students.

Graph Neural Network for Phenotype Prediction

This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.

Spsshelp 100608163328-phpapp01

This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.

Solving dynamics problems with matlab

This document provides an introduction to solving dynamics problems using MATLAB. It discusses numerical calculations, writing scripts, defining functions, graphics, symbolic calculations, differentiation, integration, and solving equations in MATLAB. It also contains sample dynamics problems from the textbook Engineering Mechanics: Dynamics that are solved using MATLAB. The problems cover kinematics and kinetics of particles and rigid bodies, as well as vibration analysis. The goal is to illustrate how computational tools like MATLAB can be used to aid in learning dynamics, but only after mastering the fundamentals through analytical problem solving.

Differences-in-Differences

The document discusses differences-in-differences (DID) estimation and provides examples of its application. It first reviews DID and its identifying assumptions. It then analyzes a study that uses DID to examine whether governments allocate more resources to councils controlled by their party. Next, it uses DID graphs and linear regression models to estimate the causal effects of electronic voting on voter turnout and spoiled votes in Kyoto, Japan. Standard errors in DID estimation and the synthetic control method are also briefly discussed.

PCA

Principal Component Analysis (PCA) is a technique used to simplify complex data sets by identifying patterns in the data and expressing it in such a way to highlight similarities and differences. It works by subtracting the mean from the data, calculating the covariance matrix, and determining the eigenvectors and eigenvalues to form a feature vector representing the data in a lower dimensional space. PCA can be used to represent image data as a one dimensional vector by stacking the pixel rows of an image and applying this analysis to multiple images.

Principal component analysis and lda

PCA and LDA are dimensionality reduction techniques. PCA transforms variables into uncorrelated principal components while maximizing variance. It is unsupervised. LDA finds axes that maximize separation between classes while minimizing within-class variance. It is supervised and finds axes that separate classes well. The document provides mathematical explanations of how PCA and LDA work including calculating covariance matrices, eigenvalues, eigenvectors, and transformations.

La statistique et le machine learning pour l'intégration de données de la bio...

This document summarizes a presentation on using statistics and machine learning for integrating high-throughput biological data. It discusses how biological data is large in volume, multi-scaled and heterogeneous in type, creating bottlenecks for analysis. It presents different methods for integrating multiple data tables, including multiple kernel learning to combine similarity matrices. An example application to TARA Oceans data is described, identifying Rhizaria abundance as structuring ocean differences. Interpretability of results is discussed along with prospects for deep learning and predicting phenotypes while understanding relationships.

Intuition – Based Teaching Mathematics for Engineers

It is suggested to teach Mathematics for engineers
based on development of mathematical intuition, thus, combining
conceptual and operational approaches. It is proposed to teach
main mathematical concepts based on discussion of carefully
selected case studies following solving of algorithmically generated
problems to help mastering appropriate mathematical tools.
The former component helps development of mathematical intuition;
the latter applies means of adaptive instructional technology
to improvement of operational skills. Proposed approach is applied
to teaching uniform convergence and to knowledge generation
using Computer Science object-oriented methodology.

ALTERNATIVE METHOD TO LINEAR CONGRUENCE

This document presents an algebraic algorithm for solving linear congruences and applies it to cryptography. It begins with background information on linear congruences and number theory. It then describes developing an algebraic algorithm that converts linear congruences into linear equations to solve them algebraically. This is a simpler approach than existing methods. Examples are provided to validate the algorithm. The paper also demonstrates applying the algorithm to solve linear congruences involved in the RSA public key cryptography system.

Reproducibility and differential analysis with selfish

Selfish is a Python tool for identifying differentially interacting chromatin regions from Hi-C contact maps of two conditions with no replicates. It begins by distance-correcting the interaction frequencies. It then computes Gaussian filters over neighboring bins to capture spatial dependencies. It compares the evolution of these filters between conditions and assigns p-values assuming Gaussian differences. Selfish is faster than existing methods and shows enrichment for epigenetic markers near differential regions. However, its statistical justification could be improved as it does not model overdispersion like other methods.

Kernel methods for data integration in systems biology

Séminaire Key Initiative for Data Science, Montpellier https://muse.edu.umontpellier.fr/key-initiatives-muse/data-life-sciences/
18 octobre 2019

Principal Component Analysis and Clustering

Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components

Lect4 principal component analysis-I

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of data by transforming it to a new coordinate system. It works by finding the principal components - linear combinations of variables with the highest variance - and using those to project the data to a lower dimensional space. PCA is useful for visualizing high-dimensional data, reducing dimensions without much loss of information, and finding patterns. It involves calculating the covariance matrix and solving the eigenvalue problem to determine the principal components.

Chap011

This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.

Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...

This presentation is about Grouping and Displaying Data to Convey Meaning: Tables and Graphs
Contents were taken from Statistics for Management by Levin & Rubin.
Presentation includes,
How can we Arrange Data?
Raw Data
Arranging Data using Data Array & Frequency Distribution
Constructing a Frequency Distribution
Graphing Frequency Distributions
It also covers some solved examples of it.

Presentation1

This document discusses innovative practices in teaching discrete structures/mathematics and data structures to undergraduate computer science students. It describes course structures at various universities and suggests focusing discrete mathematics on fundamental concepts like logic, proofs, and counting before more advanced topics. For data structures, it recommends teaching implementation to build understanding but also focusing on usage. Projects, multimedia, and games are presented as motivating teaching techniques. Historical sources are proposed to provide context for abstract concepts.

Selective inference and single-cell differential analysis

This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.

Decision Tree Algorithm Implementation Using Educational Data

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

Data Structures 2004

Data Structures 2004

Pca

Pca

Graph Neural Network for Phenotype Prediction

Graph Neural Network for Phenotype Prediction

Spsshelp 100608163328-phpapp01

Spsshelp 100608163328-phpapp01

Solving dynamics problems with matlab

Solving dynamics problems with matlab

Differences-in-Differences

Differences-in-Differences

PCA

PCA

Principal component analysis and lda

Principal component analysis and lda

La statistique et le machine learning pour l'intégration de données de la bio...

La statistique et le machine learning pour l'intégration de données de la bio...

Intuition – Based Teaching Mathematics for Engineers

Intuition – Based Teaching Mathematics for Engineers

ALTERNATIVE METHOD TO LINEAR CONGRUENCE

ALTERNATIVE METHOD TO LINEAR CONGRUENCE

Reproducibility and differential analysis with selfish

Reproducibility and differential analysis with selfish

Kernel methods for data integration in systems biology

Kernel methods for data integration in systems biology

Principal Component Analysis and Clustering

Principal Component Analysis and Clustering

Lect4 principal component analysis-I

Lect4 principal component analysis-I

Chap011

Chap011

Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...

Grouping and Displaying Data to Convey Meaning: Tables & Graphs chapter_2 _fr...

Presentation1

Presentation1

Selective inference and single-cell differential analysis

Selective inference and single-cell differential analysis

Decision Tree Algorithm Implementation Using Educational Data

Decision Tree Algorithm Implementation Using Educational Data

Machine learning ppt unit one syllabuspptx

This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It discusses common machine learning applications and challenges. Key topics covered include linear regression, classification, clustering, neural networks, bias-variance tradeoff, and model selection. Evaluation techniques like training error, validation error, and test error are also summarized.

Machine Learning: Foundations Course Number 0368403401

This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.

Machine Learning: Foundations Course Number 0368403401

This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.

Machine Learning: Foundations Course Number 0368403401

This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.

Technology Lesson Plan Assignment: Quadratice Functions

This technology-infused lesson plan teaches 9th grade students about quadratic functions through a week-long unit. Students will learn to solve and graph quadratic equations and functions algebraically and graphically using tools like graphing calculators, online graphing calculators, and SMART Notebook software. Formative and summative assessments include group presentations and a unit test on quadratic functions. The lesson incorporates student-centered learning and supports various learning styles.

Ict Tools In Mathematics Instruction

A presentation made at All Nations University, Koforidua on 12/3/10 to mark Ghana\'s National Math Day

EE-232-LEC-01 Data_structures.pptx

The document provides biographical and professional details about Engr. Dr. Sohaib Manzoor. It lists his educational qualifications including a BS in electrical engineering, an MS in electrical and electronics engineering, and a PhD in information and communication engineering. It also outlines his work experience as a lecturer at Mirpur University of Science and Technology, Pakistan. Additionally, it lists his skills, contact information, hobbies and some academic and non-academic achievements.

Automatically Answering And Generating Machine Learning Final Exams

This document introduces a new dataset of machine learning final exams from MIT, Harvard, and Cornell consisting of 646 questions. It aims to test if machines can learn machine learning by having models answer questions from actual university final exams in the subject. These questions are longer, more complex, and cover a broader range of machine learning topics compared to typical problem sets. Several baseline models are evaluated on the dataset, with the best model found to perform at a human level. The full dataset and code are made publicly available to assess new language models and advance automatic problem-solving abilities in machine learning.

AlgorithmsModelsNov13.pptx

The document provides an overview of topics to be covered in a data analytics training, including a review of previous concepts and an introduction to new topics. It discusses the data science process, linear regression, k-means clustering, k-nearest neighbors (k-NN) classification, and provides examples of applying these machine learning algorithms to real datasets. Sample R code is also included to demonstrate k-means and k-NN algorithms on synthetic data. The goal is for students to gain hands-on experience applying different analytical techniques through worked examples and exercises using real data.

[DOLAP2023] The Whys and Wherefores of Cubes

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking "why does a measure show these values?". Specifically, we propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between a measure and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.

An alternative learning experience in transition level mathematics

QUT Mathematical Sciences Seminar series, November 1 2013
Traditionally at QUT, mathematics and statistics are taught using a face-to-face lecture/tutorial model involving large lecture classes for around 1/2 to 3/4 of the time and smaller group tutorials for the remainder of the time. This is also one of the main models for teaching at other campus-based institutions. Recently, in response to (learning) technology advances and changes in the ways learners seek education, QUT has made a significant commitment to a “Digital Transformation” project across the university. In this seminar I will present a technical overview, with some demonstrations, of a pilot project that seeks to investigate how digital transformation might work in a QUT mathematics or statistics subject. In particular, I will discuss the use of tablet PC technology and specialist software to produce video learning packages. This approach has been trialled in a transition level mathematics unit this semester. I will also cover integration of these learning packages with QUTs Learning Management System “Blackboard”. This seminar is a technical preview to another talk I will give early in the new year that will look at the impact of the altered learning experience on student outcomes, feedback and the unit itself.

4.80 sy it

The document provides details of the syllabus for the third semester B.Sc. Information Technology course at the University of Mumbai. It includes information on 5 theory courses - Logic and Discrete Mathematics, Computer Graphics, Advanced SQL, Object Oriented Programming with C++, and Modern Operating Systems. For each theory course, it lists the course code, number of lectures per week, unit topics, expected learning outcomes and reference books. It also provides details of the corresponding practical/lab courses including expected experiments and assignments.

ch12lectPP420

- Many children struggle with math, with around 28% of 4th graders and 27% of 8th graders performing below basic level in key areas like numbers and operations.
- Effective math instruction incorporates principles like setting high standards, engaging instruction, and representing numerical concepts visually. It also explicitly teaches key components like problem solving, communication, and applying math to everyday situations.
- Teachers should use instructional methods that make math concrete, like manipulatives, and teach basic math facts through programs that emphasize varied activities to build engagement and mastery.

22_RepeatedMeasuresDesign_Complete.pptx

1) The document describes a unit on repeated measures designs, including a review of standard repeated measures analyses using linear models and multi-level modeling, as well as an alternative approach.
2) Key features of repeated measures designs are discussed, such as having more than one observation per participant. Advantages and challenges like order effects are also reviewed.
3) Methods for analyzing repeated measures data using linear models by first transforming the data into wide format using differences and averages are described and compared to a multi-level modeling approach.

Rd1 r17a19 datawarehousing and mining_cap617t_cap617

Some other areas where we can apply data mining in University are:
1. Predicting student performance and identifying at-risk students to provide early interventions.
2. Analyzing course evaluations and feedback to identify strengths and weaknesses in teaching methods.
3. Examining enrollment and registration patterns to understand student preferences and inform course scheduling.
4. Mining alumni data to understand career paths, further education choices and how well programs prepare students.
5. Analyzing library usage data to optimize resources, collections and services based on user needs.
6. Applying clustering and segmentation to understand different student profiles and tailor support services.
7. Mining online learning platforms to understand engagement, predict dropouts

Course Syllabus For Operations Management

This document provides course syllabi for the Operations Management and Management Information Systems department across 5 years of study. It outlines 24 courses covering topics such as mathematics, statistics, programming, databases, supply chain management, e-commerce, project management, and decision support systems. The courses aim to give students foundational knowledge and skills in OM/MIS and its applications to business management.

313 IDS _Course_Introduction_PPT.pptx

This document provides information for the course "Introduction to Data Science" (ITEC-313) at Jazan University. The course is a required 3 credit hour course consisting of 2 hours of theory and 2 hours of lab per week. The course objectives are to describe data science and the needed skill sets, understand the data science process and how its components interact, carry out basic statistical modeling and analysis, and apply the data science process in a case study. Topics covered include data collection/integration, exploratory data analysis, predictive/descriptive modeling, and effective communication. The course aims to equip students with basic data science principles, concepts, techniques and tools. It will be assessed through assignments, exams, quizzes

A data science observatory based on RAMP - rapid analytics and model prototyping

RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

This document discusses various statistical analysis and feature engineering techniques that can be used for model building in machine learning algorithms. It describes how proper feature extraction through techniques like correlation analysis, principal component analysis, recursive feature elimination, and feature importance can help improve the accuracy of machine learning models. The document provides examples of applying different feature selection methods like univariate selection, recursive feature elimination, and principal component analysis on a diabetes dataset. It also explains the mathematics behind principal component analysis and how feature importance is estimated using an extra trees classifier. Overall, the document emphasizes how statistical analysis and feature engineering are important for effective model building in machine learning.

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.

Machine learning ppt unit one syllabuspptx

Machine learning ppt unit one syllabuspptx

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Machine Learning: Foundations Course Number 0368403401

Technology Lesson Plan Assignment: Quadratice Functions

Technology Lesson Plan Assignment: Quadratice Functions

Ict Tools In Mathematics Instruction

Ict Tools In Mathematics Instruction

EE-232-LEC-01 Data_structures.pptx

EE-232-LEC-01 Data_structures.pptx

Automatically Answering And Generating Machine Learning Final Exams

Automatically Answering And Generating Machine Learning Final Exams

AlgorithmsModelsNov13.pptx

AlgorithmsModelsNov13.pptx

[DOLAP2023] The Whys and Wherefores of Cubes

[DOLAP2023] The Whys and Wherefores of Cubes

An alternative learning experience in transition level mathematics

An alternative learning experience in transition level mathematics

4.80 sy it

4.80 sy it

ch12lectPP420

ch12lectPP420

22_RepeatedMeasuresDesign_Complete.pptx

22_RepeatedMeasuresDesign_Complete.pptx

Rd1 r17a19 datawarehousing and mining_cap617t_cap617

Rd1 r17a19 datawarehousing and mining_cap617t_cap617

Course Syllabus For Operations Management

Course Syllabus For Operations Management

313 IDS _Course_Introduction_PPT.pptx

313 IDS _Course_Introduction_PPT.pptx

A data science observatory based on RAMP - rapid analytics and model prototyping

A data science observatory based on RAMP - rapid analytics and model prototyping

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...

A Independência da América Espanhola LAPBOOK.pdf

Lapbook sobre independência da América Espanhola.

World environment day ppt For 5 June 2024

for world environment day

A Strategic Approach: GenAI in Education

Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.

writing about opinions about Australia the movie

writing about opinions about Australia the movie

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...

ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf

it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.

Digital Artifact 1 - 10VCD Environments Unit

Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design

South African Journal of Science: Writing with integrity workshop (2024)

South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa

A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.How to Build a Module in Odoo 17 Using the Scaffold Method

Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.

Film vocab for eal 3 students: Australia the movie

film vocab esl

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection

https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdC1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx

C1 Rubenstein

Your Skill Boost Masterclass: Strategies for Effective Upskilling

Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan

Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.How to Fix the Import Error in the Odoo 17

An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.

Liberal Approach to the Study of Indian Politics.pdf

The Best topic of my Interest.

Introduction to AI for Nonprofits with Tapp Network

Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.

CACJapan - GROUP Presentation 1- Wk 4.pdf

Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.

A Survey of Techniques for Maximizing LLM Performance.pptx

A Survey of Techniques for Maximizing LLM Performance

The Diamonds of 2023-2024 in the IGRA collection

A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.

A Independência da América Espanhola LAPBOOK.pdf

A Independência da América Espanhola LAPBOOK.pdf

World environment day ppt For 5 June 2024

World environment day ppt For 5 June 2024

A Strategic Approach: GenAI in Education

A Strategic Approach: GenAI in Education

writing about opinions about Australia the movie

writing about opinions about Australia the movie

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf

ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf

ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf

Digital Artifact 1 - 10VCD Environments Unit

Digital Artifact 1 - 10VCD Environments Unit

South African Journal of Science: Writing with integrity workshop (2024)

South African Journal of Science: Writing with integrity workshop (2024)

How to Build a Module in Odoo 17 Using the Scaffold Method

How to Build a Module in Odoo 17 Using the Scaffold Method

Film vocab for eal 3 students: Australia the movie

Film vocab for eal 3 students: Australia the movie

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...

C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx

C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx

Your Skill Boost Masterclass: Strategies for Effective Upskilling

Your Skill Boost Masterclass: Strategies for Effective Upskilling

How to Fix the Import Error in the Odoo 17

How to Fix the Import Error in the Odoo 17

Liberal Approach to the Study of Indian Politics.pdf

Liberal Approach to the Study of Indian Politics.pdf

Introduction to AI for Nonprofits with Tapp Network

Introduction to AI for Nonprofits with Tapp Network

CACJapan - GROUP Presentation 1- Wk 4.pdf

CACJapan - GROUP Presentation 1- Wk 4.pdf

The basics of sentences session 6pptx.pptx

The basics of sentences session 6pptx.pptx

A Survey of Techniques for Maximizing LLM Performance.pptx

A Survey of Techniques for Maximizing LLM Performance.pptx

The Diamonds of 2023-2024 in the IGRA collection

The Diamonds of 2023-2024 in the IGRA collection

- 1. Classes without dependencies Teaching the tidyverse to ﬁrst year science students Sam Clifford, Iwona Czaplinski, Brett Fyﬁeld, Sama Low-Choy, Belinda Spratt, Amy Stringer, Nicholas Tierney 2018-07-12
- 2. The student body’s got a bad preparation SEB113 a core unit in QUT’s 2013 redesign of Bachelor of Science Introduce key math/stats concepts needed for ﬁrst year science OP 13 cutoff (ATAR 65) Assumed knowledge: Intermediate Mathematics Some calculus and statistics Not formally required Diagnostic test and weekly prep material Basis for further study in disciplines (explicit or embedded) Still needs to be a self-contained unit that teaches skills
- 3. What they need is adult education Engaging students with use of maths/stats in science Build good statistical habits from the start Have students doing analysis that is relevant to their needs as quickly as possible competently with skills that can be built on Introduction to programming reproducibility separating analysis from the raw data ﬂexibility beyond menus correcting mistakes becomes easier
- 4. You go back to school Bad old days Manual calculation of test statistics Reliance on statistical tables Don’t want to replicate senior high school study Reduce reliance on point and click software that only does everything students need right now (Excel, Minitab) Students don’t need to become R developers Focus on functionality rather than directly controlling every element, e.g. LATEXvs Word
- 5. It’s a bad situation Initial course development was not tidy New B Sc course brought forward Grab bag of topics at request of science academics Difﬁcult to ﬁnd tutors who could think outside “traditional” stat. ed. very low student satisfaction initially Rapid and radical redesign required tidyverse an integrated suite focused on transforming data frames Vectorisation > loops RStudio > JGR > Rgui.exe
- 6. What you want is an adult education (Oh yeah!) Compassion and support for learners Problem- and model-based Technology should support learning goals Go further, quicker by not focussing on mechanical calculations Workﬂow based on functions rather than element manipulation Statistics is an integral part of science Statistics isn’t about generating p values see Cobb in Wasserstein and Lazar [2016]
- 7. Machines do the work so people have time to think – IBM (1967) All models are wrong, but some are useful – Box (1987)
- 8. Now here we go dropping science, dropping it all over Within context of scientiﬁc method: Aims Methods and Materials 1. Get data/model into an analysis environment 2. Data munging Results 3. Exploration of data/model 4. Compute model 5. Model diagnostics Conclusion 6. Interpret meaning of results
- 9. I said you wanna be startin’ somethin’ Redesign around ggplot2 ggplot2 introduced us to tidy data requirements Redesign based on Year 11 summer camp This approach not covered by textbooks at the time Tried using JGR and Plot Builder for one semester Extension to wider tidyverse Replace unrelated packages/functions with uniﬁed approach Focus on what you want rather than directly coding how to do it Good effort-reward with limited expertise
- 10. Summer(ise) loving, had me a blast; summer(ise) loving, happened so fast R is a giant calculator that can operate on objects ggplot() requires a data frame object dplyr::summarise() to summarise a column variable dplyr::group_by() to do summary according to speciﬁed structure Copy-paste or looping not guaranteed to be MECE Group-level summary stats leads to potential statistical models Easier, less error prone, than repeated usage of =AVERAGE()
- 11. We want the funk(tional programming paradigm) Tidy data as observations of variables with structure [Wickham, 2014b] R as functional programming [Wickham, 2014a] Actions on entire objects to do things to data and return useful information Students enter understanding functions like y(x) = x2 function takes input function returns output e.g. mean(x) = i xi/n Week 4: writing functions to solve calculus problems magrittr::%>% too conceptually similar to ggplot2::+ for novices to grasp in ﬁrst course
- 12. Like Frankie sang, I did it my way What’s the mean gas mileage for each engine geometry and transmission type for the 32 cars listed in 1974 Motor Trends magazine? Loops For each of the pre-computed number of groups, subset, summarise and store how you want tapply() INDEX a list of k vectors, 1 summary FUNction, returns k-dimensional array dplyr specify grouping variables and which sum- mary statistics, returns tidy data frame ready for model/plot
- 13. Night of the living baseheads Like all procedural languages, plot() has one giant list of arguments Focus is on how plot is drawn rather than what you want to plot Inefﬁciency of keystrokes re-stating the things being plotted setting up plot axis limits loop counters for small multiples, etc.
- 14. Toot toot, chugga chugga, big red car Say we want to plot cars’ fuel efﬁciency against weight library(tidyverse) data(mtcars) mtcars <- mutate( mtcars, l100km = 235.2146/mpg, wt_T = wt/2.2046, am = factor(am, levels = c(0,1), labels=c("Auto", "Manual")), vs = factor(vs, levels = c(0,1), labels=c("V","S"))) plot(y=mtcars$l100km, x=mtcars$wt_T) 1.0 1.5 2.0 2.5 101520 mtcars$wt_T mtcars$l100km Fairly quick to say what goes on x and y axes More arguments → better graph xlim, ylim xlab, ylab main type, pch What if we want to see how it varies with engine geometry transmission type
- 15. The wisdom of the fool won’t set you free yrange <- range(mtcars$l100km) xrange <- range(mtcars$wt_T) levs <- expand.grid(vs = c("V", "S"), am = c("Auto", "Manual")) par(mfrow = c(2,2)) for (i in 1:nrow(levs)){ dat_to_plot <- merge(levs[i, ], mtcars) plot(dat_to_plot$l100km ~ dat_to_plot$wt_T, pch=16, xlab="Weight (t)", xlim=xrange, ylab="Fuel efﬁciency (L/100km)", ylim=yrange, main = sprintf("%s-%s", levs$am[i], levs$vs[i]))} 1.0 1.5 2.0 2.5 101520 Auto−V Weight (t) Fuelefficiency(L/100km) 1.0 1.5 2.0 2.5 101520 Auto−S Weight (t) Fuelefficiency(L/100km) 1.0 1.5 2.0 2.5 101520 Manual−V Weight (t) Fuelefficiency(L/100km) 1.0 1.5 2.0 2.5 101520 Manual−S Weight (t) Fuelefficiency(L/100km) ggplot(data = mtcars, aes(x = wt_T, y = l100km)) + geom_point() + facet_grid(am ~ vs) + theme_bw() + xlab("Weight (t)") + ylab("Fuel efﬁciency (L/100km)") V S AutoManual 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5 10 15 20 10 15 20 Weight (t) Fuelefficiency(L/100km)
- 16. One, two, princes kneel before you Both approaches do the same thing Idea base ggplot2 Plot variables Specify vectors Coordinate system de- ﬁned by variables Small multiples Loops, subsets, par facet_grid Common axes Pre-computed Inherited from data V/S A/M annotation Strings Inherited from data Axis labels Per axis set For whole plot Focus on putting things on the page vs representing variables
- 17. I got a grammar Hazel and a grammar Tilly Plots are built from [Wickham, 2010] data – which variables are mapped to aesthetic elements geometry – how do we draw the data? annotations – what is the context of these shapes? Build more complex plots by adding commands and layering elements, rather than by stacking individual points and lines e.g. make a scatter plot, THEN add a trend line (with inherited x, y), THEN facet by grouping variable, THEN change axis information
- 18. When I’m good, I’m very good; but when I’m bad, I’m better Want to make good plots as soon as possible Learning about Tufte’s principles [Tufte, 1983, Pantoliano, 2012] Discuss what makes a plot good and bad Seeing how ggplot2 code translates into graphical elements Week 2 workshop has students making best and worst plots for a data set, e.g.
- 19. Sie ist ein Model und sie sieht gut aus Make use of broom package to get model summaries Get data frames rather than summary.lm() text vomit tidy() parameter estimates CIs t test info [Greenland et al., 2016] glance() everything else ggplot2::fortify() regression diagnostic info instead of plot.lm() stat_qq(aes(x=.stdresid)) for residual quantiles geom_point(aes(x=.ﬁtted, y=.resid)) for ﬁtted vs residuals
- 20. When you hear some feedback keep going take it higher Positives More conﬁdence and students see use of maths/stats in science Students enjoy group discussions in workshops Some students continue using R over Excel in future units Labs can be done online in own time Negatives Request for more face to face help rather than online Labs can be done online in own time (but are they?) Downloading of slides rather than attending/watching lectures
- 21. Things can only get better Focus on what you want from R rather than how you do it representing variables graphically summarising over structure in data tidiers for models Statistics embedded in scientiﬁc theory [Diggle and Chetwynd, 2011] Problem-based learning groups of novices supervised by tutors discussion of various approaches
- 22. Peter J. Diggle and Amanda G. Chetwynd. Statistics and Scientiﬁc Method: An Introduction for Students and Researchers. Oxford University Press, 2011. Sander Greenland, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin, Charles Poole, Steven N. Goodman, and Douglas G. Altman. Statistical tests, p values, conﬁdence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31(4):337–350, apr 2016. URL https://doi.org/10.1007/s10654-016-0149-3. Mike Pantoliano. Data visualization principles: Lessons from Tufte, 2012. URL https: //moz.com/blog/data-visualization-principles-lessons-from-tufte. Edward Tufte. The Visual Display of Quantitative Information. Graphics Press, 1983. Ronald L. Wasserstein and Nicole A. Lazar. The ASA's statement on p-values: Context, process, and purpose. The American Statistician, 70 (2):129–133, Apr 2016. URL https://doi.org/10.1080/00031305.2016.1154108.
- 23. H. Wickham. Advanced R. Chapman & Hall/CRC The R Series. Taylor & Francis, 2014a. ISBN 9781466586963. URL https://books.google.com.au/books?id=PFHFNAEACAAJ. Hadley Wickham. A layered grammar of graphics. Journal of Computational and Graphical Statistics, 19(1):3–28, 2010. doi: 10.1198/jcgs.2009.07098. Hadley Wickham. Tidy data. Journal of Statistical Software, 59(1):1–23, 2014b. ISSN 1548-7660. URL https://www.jstatsoft.org/index.php/jss/article/view/v059i10.