Ayasdi Energy Summit, September 2014, Gunnar Carlsson
Download as pptx, pdf4 likes1,259 views
Ayasdi utilizes topological data analysis (TDA) to offer insights from complex data, emphasizing that real-world data does not conform to traditional models. Their approach focuses on segmentation, feature extraction, and anomaly detection while overcoming limitations of linear and black-box methods. Key properties of TDA include coordinate freeness, deformation invariance, and compressed representation, which enable the exploration of underlying relationships within the data.
1 of 17
Downloaded 29 times
Ad
Recommended
2011 Big Data - Bigger Problems for Drug Discovery and Development
2011 Big Data - Bigger Problems for Drug Discovery and DevelopmentAyasdi
?
This document summarizes a presentation about using topological data analysis to solve problems in drug discovery and development. It discusses the challenges of big data in this field, such as scalability issues and a lack of integrated tools. It then describes how the Ayasdi Iris platform uses principles of topology to identify subtle patterns in large, complex datasets. Examples are given of how Ayasdi has used topological data analysis for patient stratification and to analyze next-generation sequencing data.Identification of Brain Regions Related to Alzheimers' Diseases using MRI Ima...
Identification of Brain Regions Related to Alzheimers' Diseases using MRI Ima...Association of Scientists, Developers and Faculties
?
The paper discusses a novel computer-aided diagnosis (CAD) system for early detection of Alzheimer's disease (AD) through MRI images using eigenbrain and k-means clustering techniques. The study highlights the importance of accurate identification of brain regions affected by AD and achieves a competitive classification accuracy of 92.36%. It aims to aid clinicians in the diagnosis process without replacing them, presenting a systematic approach to feature extraction and classification methods based on prior literature.Energy-based Model for Out-of-Distribution Detection in Deep Medical Image Se...
Energy-based Model for Out-of-Distribution Detection in Deep Medical Image Se...Seunghyun Hwang
?
The document presents a novel energy-based model (EBM) for out-of-distribution (OOD) detection in deep medical image segmentation, specifically targeting left ventricular endocardial segmentation in MRI images. The proposed method jointly trains a segmentation model and the EBM to enhance both discriminative and generative properties, successfully addressing OOD detection challenges while maintaining segmentation performance for in-distribution data. Results demonstrate the model's capability to reliably detect OOD data and improve automatic quality control in medical imaging.Robust face recognition by applying partitioning around medoids over eigen fa...
Robust face recognition by applying partitioning around medoids over eigen fa...ijcsa
?
The document presents a novel approach to robust face recognition using an unsupervised learning methodology that integrates partitioning around medoids with eigen faces and fisher faces. This method aims to enhance the system's resilience against variations such as changes in expressions, emotions, and illumination, thus improving efficiency in real-time applications. Experimental results indicate that the proposed method achieves higher accuracy and reduced cost and time compared to traditional face recognition techniques.[db tech showcase Tokyo 2018] #dbts2018 #B16 『The Basics of Machine Learning』
[db tech showcase Tokyo 2018] #dbts2018 #B16 『The Basics of Machine Learning』Insight Technology, Inc.
?
This document provides an overview of a presentation on machine learning basics by Heli Helskyaho. It includes biographical information about Helskyaho, such as her education, work experience, awards, and publications. The bulk of the document consists of slides from the presentation, which cover topics like definitions of machine learning and big data, challenges in working with big data, common machine learning use cases, when to use machine learning, key machine learning concepts like algorithms, models, features, supervised vs. unsupervised learning, and examples of specific machine learning algorithms.Comprehensive Survey of Data Classification & Prediction Techniques
Comprehensive Survey of Data Classification & Prediction Techniquesijsrd.com
?
This paper presents a literature survey of modern data classification and prediction algorithms, highlighting their importance in real-world applications like disease prediction. It discusses various methods such as decision trees, naive Bayes classifiers, and random trees, focusing on their strengths and applications in data mining. The conclusion indicates a proposal for a modified decision tree method in a future paper.Stephen Pinfield: Research Data Management and Libraries: Work in Progress
Stephen Pinfield: Research Data Management and Libraries: Work in ProgressBodleian Libraries Staff Development
?
This document discusses the evolving role of libraries in research data management (RDM), emphasizing the complexities and challenges associated with RDM as a 'wicked' problem that involves multiple stakeholders, drivers, and processes. It outlines the frameworks needed for effective RDM, highlighting components such as data storage, security, and preservation while also examining the leadership and collaborative skills required within academic institutions. The document calls for adapting library services to better support the RDM needs of researchers and to navigate the changing landscape of data management.Machine Learning with Ayasdi
Machine Learning with AyasdiAyasdi
?
This document discusses machine learning and topological data analysis using Ayasdi software. It provides an introduction to Ayasdi and topological data analysis, and explains how Ayasdi uses machine learning techniques like neural networks and random forests along with topological data analysis to better understand, segment, and act on data. Specifically, Ayasdi provides clearer segmentation of data and better identification of errors compared to traditional machine learning approaches. The goal is to help people leverage their data more effectively.MLconf NYC Pek Lum
MLconf NYC Pek LumMLconf
?
This document discusses topological data analysis (TDA) and its applications. It provides a timeline of Ayasdi, a company that uses TDA. Key points:
1. TDA uses the shape of data to find unknown phenomena without preconceived models. It is a combination of math, computer science, and user experience.
2. Ayasdi software automates TDA and provides intuitive visualizations to make it accessible without coding. This allows insights from data to be more democratized.
3. A case study used TDA to analyze cancer data from TCGA and identify subpopulations across 12 cancer types. It revealed p53 mutations are common between triple negative breast and ovarian cancers.Causal discovery
Causal discoverydagunisa
?
This document summarizes the research topics and projects of the Data Analytics Group (DAG) at the University of South Australia. It discusses DAG's focus on relationship discovery, data integration and quality, text mining, outlier detection, causal modeling and reasoning, and sensor data management for water utilities. Key points include DAG having 5 academics, 4 postdocs and 15 students, $1.5M in funding, and projects with industry partners on topics like causal inference, identity resolution, medication safety surveillance, and water treatment performance assessment.APS GDS data science talk by Trevor Rhone
APS GDS data science talk by Trevor RhoneTrevorDavidRhone
?
This document provides an introduction to using data science for physicists. It discusses what data science is, why physicists should care about it, and how to do it. The key aspects of data science covered are getting data, data visualization, model selection, validation and exploitation. Machine learning models like regression, classification and neural networks are explained. The document concludes with resources for learning data science and a case study applying it to study magnetic properties of materials.Fairness in Machine Learning
Fairness in Machine LearningDelip Rao
?
The document discusses the importance of fairness in machine learning, highlighting challenges like bias in data and the need for proper evaluation metrics. It emphasizes the necessity of understanding the impact of sensitive attributes and the implications of fairness in decision-making processes. The text also provides resources and a reading list for further exploration of fairness in data-driven applications.Data Education project briefing for Royal Society
Data Education project briefing for Royal SocietyKate Farrell
?
The document outlines the Data Education in Schools project, which aims to enhance data education among 121,000 learners in the Edinburgh city region. It emphasizes the importance of data literacy, defining it as the ability to engage with data ethically and effectively, and provides a curricular framework for teaching data skills from early childhood to secondary school. The project involves developing teaching materials, workshops for educators, and integrating data understanding across various subjects to foster informed data citizens and professionals.Data Mining : Concepts and Techniques
Data Mining : Concepts and TechniquesDeepaR42
?
This document provides an introduction to data mining. It discusses the evolution of data mining technology, defines what data mining is, and outlines common data mining tasks like classification, clustering, and association rule discovery. The document also examines the KDD process, different types of data that can be mined, and major issues in data mining like scalability, handling diverse data types, and integrating discovered knowledge.Oracle openworld-presentation
Oracle openworld-presentationDr. Neil Brittliff
?
The document outlines Dr. Neil Brittliff's approach to leveraging big data within the context of law enforcement and criminal intelligence, focusing on a schema-last approach to data management. It discusses the challenges of data cleansing and transformation in the intelligence life-cycle and highlights the benefits of improved data integration and analysis through advanced technological solutions. The material emphasizes the need for efficient data ingestion processes and the importance of collaboration among various agencies to enhance crime analysis in Australia.Optimizing Classification Models for Autism Spectrum Disorder(ASD) Detection ...
Optimizing Classification Models for Autism Spectrum Disorder(ASD) Detection ...RafizKhan
?
This document presents research on optimizing machine learning classification models for early detection of autism spectrum disorder (ASD) across age groups. The study demonstrates that the random forest algorithm achieves the highest classification accuracy (99.89%) compared to other models like SVM, SMO, and KNN, underscoring the significance of data preprocessing techniques in improving diagnostic accuracy. Challenges such as limited dataset diversity and the need for more comprehensive data sources are also discussed to enhance future ASD diagnostic tools.Becoming Datacentric
Becoming DatacentricTimothy Cook
?
The document discusses the principles of transitioning to a data-centric approach within organizations, emphasizing the need for sharable, structured, and semantic data models. It highlights challenges such as data migration costs, the importance of context in data, and the fallacies surrounding big data. Additionally, it introduces the Structured Semantic Shareable Model (S3M), which integrates technologies to enhance data processing and modeling, ensuring future-proof solutions in data management.Hanaa phd presentation 14-4-2017
Hanaa phd presentation 14-4-2017Aboul Ella Hassanien
?
This document presents an intelligent visualization framework for multi-dimensional data sets. The framework includes pre-processing, feature selection, classification, rule refinement, and visualization phases. In the feature selection phase, principal component analysis and rough sets are used to select important features. Classification is done using rough set rules generation. The rules are then refined using entropy and genetic algorithms. Finally, the refined rules and reducts are visualized using nodes, edges, charts and grids to help experts understand the data. Experimental results on breast cancer and prostate cancer data sets demonstrate the performance of the approach.Data Science: Origins, Methods, Challenges and the future?
Data Science: Origins, Methods, Challenges and the future?Cagatay Turkay
?
The document explores the origins, methods, and challenges of data science, defining it as a systematic study focused on extracting knowledge from data. It discusses the data science process, which involves formulating questions, data collection, cleaning, analysis, and communication of findings, while highlighting the complexities faced in the field such as heterogeneous data and the curse of dimensionality. The document also emphasizes the increasing importance of predictive analytics and visualization tools in data science.DATA MINING - CHARACTERISTICS and APPLICATION
DATA MINING - CHARACTERISTICS and APPLICATIONMD.ANISUR RAHMAN
?
The document discusses data mining and machine learning, defining data mining as the extraction of useful information from vast datasets and machine learning as the development of algorithms that enhance through data experience. It highlights key characteristics of structured data such as dimensionality, sparsity, resolution, and distribution, along with common data quality issues like noise, outliers, and duplicates. Additionally, it emphasizes the importance of data visualization for understanding trends and informs various application areas including market analysis, healthcare, and manufacturing.Exploratory Data Analysis - NIST eHandbook of Statistical Methods-out.pdf
Exploratory Data Analysis - NIST eHandbook of Statistical Methods-out.pdflsharkey602
?
The document outlines the principles and techniques of Exploratory Data Analysis (EDA), emphasizing its philosophy over classical and Bayesian methods. EDA focuses on maximizing insight from data through graphical techniques, allowing analysts to uncover underlying structures and better understand the data without imposing preconceived models. It distinguishes EDA from summary statistics, which merely provide passive numerical reductions of historical data.Big Data - A view
Big Data - A viewDansk BiblioteksCenter
?
The document discusses the concept of big data, highlighting its significance, applications, and challenges in various sectors, particularly in agriculture and health. It also outlines the big data value chain, including data origins, collection, storage, analytics, and consumer interactions. Moreover, it presents the initiatives and educational programs at DTU Compute related to data science and big data solutions in urban settings.Unit 1.pptx
Unit 1.pptxDrThenmozhiSPESUMCA
?
This document outlines the course structure for a structured data analytics course. It is a 3 credit course that includes practice sessions, assessments like quizzes and assignments. The course covers topics like data wrangling, classification/regression algorithms, association analysis, time series, and recommender systems. It also discusses popular machine learning algorithms and reading resources. Finally, it provides an overview of the first unit which is on data analytics from a business perspective.Data mining
Data miningInternational Islamic University
?
Data mining is the process of extracting valuable information and patterns from large datasets, utilizing various techniques such as clustering and classification. It is distinguished from machine learning as it focuses on discovering understandable knowledge from extensive, noisy databases, whereas machine learning aims to improve performance based on learned examples. Data warehousing enhances data mining potential by providing a centralized, consistent, and efficient environment for data analysis.Public Data and Data Mining Competitions - What are Lessons?
Public Data and Data Mining Competitions - What are Lessons?Gregory Piatetsky-Shapiro
?
The document discusses the evolution and significance of data mining and analytics, highlighting key competitions like the KDD Cup and Netflix Prize that have shaped the field. It emphasizes the importance of data preparation, model simplicity, and the right questions in predictive modeling. Additionally, it outlines the rise of public data initiatives and the impact of big data on various industries and applications.Top (10) challenging problems in data mining
Top (10) challenging problems in data miningAhmedasbasb
?
This document outlines the top 10 challenging problems in data mining, as presented by Dr. Ali Haroun. It introduces data mining and some common techniques. The top 10 problems are then each described in one or two paragraphs: (1) developing a unifying theory of data mining, (2) scaling up for high dimensional and high speed data, (3) mining sequence and time series data, (4) mining complex knowledge from complex data, (5) data mining in a network setting, (6) distributed data mining and mining multi-agent data, (7) data mining for biological and environmental problems, (8) data mining process-related problems, (9) security, privacy, and data integrity, and (Overview of Data Cleaning.pdf
Overview of Data Cleaning.pdfSheetalDandge
?
Data cleaning is a vital process in machine learning that ensures data accuracy and consistency, directly impacting model performance. It involves steps such as handling missing data, removing duplicates, correcting errors, and addressing outliers. While crucial, data cleaning can be time-consuming and may lead to errors if not conducted carefully, emphasizing the need for a systematic approach.KLIP2Data voor de herinrichting van R4 West en Oost
KLIP2Data voor de herinrichting van R4 West en Oostjacoba18
?
Ontwikkeling van een centrale GIS-databank voor de periodieke import van KLIP-data, BIM- en CAD-modellen van het R4-project en bijhorende field app om de databank zowel online als offline te raadplegen en bij te werken. More Related Content
Similar to Ayasdi Energy Summit, September 2014, Gunnar Carlsson (20)
MLconf NYC Pek Lum
MLconf NYC Pek LumMLconf
?
This document discusses topological data analysis (TDA) and its applications. It provides a timeline of Ayasdi, a company that uses TDA. Key points:
1. TDA uses the shape of data to find unknown phenomena without preconceived models. It is a combination of math, computer science, and user experience.
2. Ayasdi software automates TDA and provides intuitive visualizations to make it accessible without coding. This allows insights from data to be more democratized.
3. A case study used TDA to analyze cancer data from TCGA and identify subpopulations across 12 cancer types. It revealed p53 mutations are common between triple negative breast and ovarian cancers.Causal discovery
Causal discoverydagunisa
?
This document summarizes the research topics and projects of the Data Analytics Group (DAG) at the University of South Australia. It discusses DAG's focus on relationship discovery, data integration and quality, text mining, outlier detection, causal modeling and reasoning, and sensor data management for water utilities. Key points include DAG having 5 academics, 4 postdocs and 15 students, $1.5M in funding, and projects with industry partners on topics like causal inference, identity resolution, medication safety surveillance, and water treatment performance assessment.APS GDS data science talk by Trevor Rhone
APS GDS data science talk by Trevor RhoneTrevorDavidRhone
?
This document provides an introduction to using data science for physicists. It discusses what data science is, why physicists should care about it, and how to do it. The key aspects of data science covered are getting data, data visualization, model selection, validation and exploitation. Machine learning models like regression, classification and neural networks are explained. The document concludes with resources for learning data science and a case study applying it to study magnetic properties of materials.Fairness in Machine Learning
Fairness in Machine LearningDelip Rao
?
The document discusses the importance of fairness in machine learning, highlighting challenges like bias in data and the need for proper evaluation metrics. It emphasizes the necessity of understanding the impact of sensitive attributes and the implications of fairness in decision-making processes. The text also provides resources and a reading list for further exploration of fairness in data-driven applications.Data Education project briefing for Royal Society
Data Education project briefing for Royal SocietyKate Farrell
?
The document outlines the Data Education in Schools project, which aims to enhance data education among 121,000 learners in the Edinburgh city region. It emphasizes the importance of data literacy, defining it as the ability to engage with data ethically and effectively, and provides a curricular framework for teaching data skills from early childhood to secondary school. The project involves developing teaching materials, workshops for educators, and integrating data understanding across various subjects to foster informed data citizens and professionals.Data Mining : Concepts and Techniques
Data Mining : Concepts and TechniquesDeepaR42
?
This document provides an introduction to data mining. It discusses the evolution of data mining technology, defines what data mining is, and outlines common data mining tasks like classification, clustering, and association rule discovery. The document also examines the KDD process, different types of data that can be mined, and major issues in data mining like scalability, handling diverse data types, and integrating discovered knowledge.Oracle openworld-presentation
Oracle openworld-presentationDr. Neil Brittliff
?
The document outlines Dr. Neil Brittliff's approach to leveraging big data within the context of law enforcement and criminal intelligence, focusing on a schema-last approach to data management. It discusses the challenges of data cleansing and transformation in the intelligence life-cycle and highlights the benefits of improved data integration and analysis through advanced technological solutions. The material emphasizes the need for efficient data ingestion processes and the importance of collaboration among various agencies to enhance crime analysis in Australia.Optimizing Classification Models for Autism Spectrum Disorder(ASD) Detection ...
Optimizing Classification Models for Autism Spectrum Disorder(ASD) Detection ...RafizKhan
?
This document presents research on optimizing machine learning classification models for early detection of autism spectrum disorder (ASD) across age groups. The study demonstrates that the random forest algorithm achieves the highest classification accuracy (99.89%) compared to other models like SVM, SMO, and KNN, underscoring the significance of data preprocessing techniques in improving diagnostic accuracy. Challenges such as limited dataset diversity and the need for more comprehensive data sources are also discussed to enhance future ASD diagnostic tools.Becoming Datacentric
Becoming DatacentricTimothy Cook
?
The document discusses the principles of transitioning to a data-centric approach within organizations, emphasizing the need for sharable, structured, and semantic data models. It highlights challenges such as data migration costs, the importance of context in data, and the fallacies surrounding big data. Additionally, it introduces the Structured Semantic Shareable Model (S3M), which integrates technologies to enhance data processing and modeling, ensuring future-proof solutions in data management.Hanaa phd presentation 14-4-2017
Hanaa phd presentation 14-4-2017Aboul Ella Hassanien
?
This document presents an intelligent visualization framework for multi-dimensional data sets. The framework includes pre-processing, feature selection, classification, rule refinement, and visualization phases. In the feature selection phase, principal component analysis and rough sets are used to select important features. Classification is done using rough set rules generation. The rules are then refined using entropy and genetic algorithms. Finally, the refined rules and reducts are visualized using nodes, edges, charts and grids to help experts understand the data. Experimental results on breast cancer and prostate cancer data sets demonstrate the performance of the approach.Data Science: Origins, Methods, Challenges and the future?
Data Science: Origins, Methods, Challenges and the future?Cagatay Turkay
?
The document explores the origins, methods, and challenges of data science, defining it as a systematic study focused on extracting knowledge from data. It discusses the data science process, which involves formulating questions, data collection, cleaning, analysis, and communication of findings, while highlighting the complexities faced in the field such as heterogeneous data and the curse of dimensionality. The document also emphasizes the increasing importance of predictive analytics and visualization tools in data science.DATA MINING - CHARACTERISTICS and APPLICATION
DATA MINING - CHARACTERISTICS and APPLICATIONMD.ANISUR RAHMAN
?
The document discusses data mining and machine learning, defining data mining as the extraction of useful information from vast datasets and machine learning as the development of algorithms that enhance through data experience. It highlights key characteristics of structured data such as dimensionality, sparsity, resolution, and distribution, along with common data quality issues like noise, outliers, and duplicates. Additionally, it emphasizes the importance of data visualization for understanding trends and informs various application areas including market analysis, healthcare, and manufacturing.Exploratory Data Analysis - NIST eHandbook of Statistical Methods-out.pdf
Exploratory Data Analysis - NIST eHandbook of Statistical Methods-out.pdflsharkey602
?
The document outlines the principles and techniques of Exploratory Data Analysis (EDA), emphasizing its philosophy over classical and Bayesian methods. EDA focuses on maximizing insight from data through graphical techniques, allowing analysts to uncover underlying structures and better understand the data without imposing preconceived models. It distinguishes EDA from summary statistics, which merely provide passive numerical reductions of historical data.Big Data - A view
Big Data - A viewDansk BiblioteksCenter
?
The document discusses the concept of big data, highlighting its significance, applications, and challenges in various sectors, particularly in agriculture and health. It also outlines the big data value chain, including data origins, collection, storage, analytics, and consumer interactions. Moreover, it presents the initiatives and educational programs at DTU Compute related to data science and big data solutions in urban settings.Unit 1.pptx
Unit 1.pptxDrThenmozhiSPESUMCA
?
This document outlines the course structure for a structured data analytics course. It is a 3 credit course that includes practice sessions, assessments like quizzes and assignments. The course covers topics like data wrangling, classification/regression algorithms, association analysis, time series, and recommender systems. It also discusses popular machine learning algorithms and reading resources. Finally, it provides an overview of the first unit which is on data analytics from a business perspective.Data mining
Data miningInternational Islamic University
?
Data mining is the process of extracting valuable information and patterns from large datasets, utilizing various techniques such as clustering and classification. It is distinguished from machine learning as it focuses on discovering understandable knowledge from extensive, noisy databases, whereas machine learning aims to improve performance based on learned examples. Data warehousing enhances data mining potential by providing a centralized, consistent, and efficient environment for data analysis.Public Data and Data Mining Competitions - What are Lessons?
Public Data and Data Mining Competitions - What are Lessons?Gregory Piatetsky-Shapiro
?
The document discusses the evolution and significance of data mining and analytics, highlighting key competitions like the KDD Cup and Netflix Prize that have shaped the field. It emphasizes the importance of data preparation, model simplicity, and the right questions in predictive modeling. Additionally, it outlines the rise of public data initiatives and the impact of big data on various industries and applications.Top (10) challenging problems in data mining
Top (10) challenging problems in data miningAhmedasbasb
?
This document outlines the top 10 challenging problems in data mining, as presented by Dr. Ali Haroun. It introduces data mining and some common techniques. The top 10 problems are then each described in one or two paragraphs: (1) developing a unifying theory of data mining, (2) scaling up for high dimensional and high speed data, (3) mining sequence and time series data, (4) mining complex knowledge from complex data, (5) data mining in a network setting, (6) distributed data mining and mining multi-agent data, (7) data mining for biological and environmental problems, (8) data mining process-related problems, (9) security, privacy, and data integrity, and (Overview of Data Cleaning.pdf
Overview of Data Cleaning.pdfSheetalDandge
?
Data cleaning is a vital process in machine learning that ensures data accuracy and consistency, directly impacting model performance. It involves steps such as handling missing data, removing duplicates, correcting errors, and addressing outliers. While crucial, data cleaning can be time-consuming and may lead to errors if not conducted carefully, emphasizing the need for a systematic approach.Recently uploaded (20)
KLIP2Data voor de herinrichting van R4 West en Oost
KLIP2Data voor de herinrichting van R4 West en Oostjacoba18
?
Ontwikkeling van een centrale GIS-databank voor de periodieke import van KLIP-data, BIM- en CAD-modellen van het R4-project en bijhorende field app om de databank zowel online als offline te raadplegen en bij te werken. 最新版美国加利福尼亚大学旧金山法学院毕业证（鲍颁尝补飞厂贵毕业证书）定制
最新版美国加利福尼亚大学旧金山法学院毕业证（鲍颁尝补飞厂贵毕业证书）定制taqyea
?
一比一还原加利福尼亚大学旧金山法学院毕业证/UCLawSF毕业证书2025原版【q薇1954292140】我们专业办理澳洲大学毕业证成绩单，美国大学毕业证成绩单,英国大学毕业证成绩单，加拿大大学毕业证成绩单，新加坡大学毕业证成绩单，新西兰大学毕业证成绩单，韩国大学毕业证成绩单，日本大学毕业证成绩单。
【复刻一套加利福尼亚大学旧金山法学院毕业证成绩单信封等材料最强攻略,Buy University of California College of the Law, San Francisco Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
加利福尼亚大学旧金山法学院成绩单能够体现您的的学习能力，包括加利福尼亚大学旧金山法学院课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【主营项目】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理毕业证|办理文凭: 买大学毕业证|买大学文凭【q薇1954292140】加利福尼亚大学旧金山法学院学位证明书如何办理申请？
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理美国成绩单加利福尼亚大学旧金山法学院毕业证【q薇1954292140】国外大学毕业证, 文凭办理, 国外文凭办理, 留信网认证
三.材料咨询办理、认证咨询办理请加学历顾问【微信:1954292140】加利福尼亚大学旧金山法学院毕业证购买指大学文凭购买，毕业证办理和文凭办理。学院文凭定制，学校原版文凭补办，扫描件文凭定做，100%文凭复刻。Artigo - Playing to Win.planejamento docx
Artigo - Playing to Win.planejamento docxKellyXavier15
?
Excelente artifo para quem está iniciando processo de aquisi??ode planejamento estratégicoVerweven van EM Legacy en OTL-data bij AWV
Verweven van EM Legacy en OTL-data bij AWVjacoba18
?
Het project heeft als doel legacy- en OTL-data samen te brengen in één consistente, OTL-conforme databank. In deze sessie laten we zien hoe we data vergelijken, verschillen helder rapporteren aan de business en feedback op een geautomatiseerde manier verwerken.QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELSAmeya Patekar
?
QUALITATIVE EXPLANATORY VARIABLES REGRESSION MODELS
最新版美国史蒂文斯理工学院毕业证（厂滨罢毕业证书）原版定制
最新版美国史蒂文斯理工学院毕业证（厂滨罢毕业证书）原版定制Taqyea
?
鉴于此，定制史蒂文斯理工学院学位证书提升履历【q薇1954292140】原版高仿史蒂文斯理工学院毕业证(SIT毕业证书)可先看成品样本【q薇1954292140】帮您解决在美国史蒂文斯理工学院未毕业难题，美国毕业证购买，美国文凭购买，【q微1954292140】美国文凭购买，美国文凭定制，美国文凭补办。专业在线定制美国大学文凭，定做美国本科文凭，【q微1954292140】复制美国Stevens Institute of Technology completion letter。在线快速补办美国本科毕业证、硕士文凭证书，购买美国学位证、史蒂文斯理工学院Offer，美国大学文凭在线购买。
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
【复刻一套史蒂文斯理工学院毕业证成绩单信封等材料最强攻略,Buy Stevens Institute of Technology Transcripts】
购买日韩成绩单、英国大学成绩单、美国大学成绩单、澳洲大学成绩单、加拿大大学成绩单（q微1954292140）新加坡大学成绩单、新西兰大学成绩单、爱尔兰成绩单、西班牙成绩单、德国成绩单。成绩单的意义主要体现在证明学习能力、评估学术背景、展示综合素质、提高录取率，以及是作为留信认证申请材料的一部分。
史蒂文斯理工学院成绩单能够体现您的的学习能力，包括史蒂文斯理工学院课程成绩、专业能力、研究能力。（q微1954292140）具体来说，成绩报告单通常包含学生的学习技能与习惯、各科成绩以及老师评语等部分，因此，成绩单不仅是学生学术能力的证明，也是评估学生是否适合某个教育项目的重要依据！All the DataOps, all the paradigms .
All the DataOps, all the paradigms .Lars Albertsson
?
Data warehouses, lakes, lakehouses, streams, fabrics, hubs, vaults, and meshes. We sometimes choose deliberately, sometimes influenced by trends, yet often get an organic blend. But the choices have orders of magnitude in impact on operations cost and iteration speed. Let's dissect the paradigms and their operational aspects once and for all.REGRESSION DIAGNOSTIC II: HETEROSCEDASTICITY
REGRESSION DIAGNOSTIC II: HETEROSCEDASTICITYAmeya Patekar
?
REGRESSION DIAGNOSTIC II: HETEROSCEDASTICITY
reporting monthly for genset & Air Compressor.pptx
reporting monthly for genset & Air Compressor.pptxdacripapanjaitan
?
Genset data
spm monthly by sap
- GENSET DATA MONTHLY
- Analyst data for genset
- preventive genset
- warming up genset
Compressor Data
- Material genset
- PM Compressor
- logsheet compressor
- Data compressorData Visualisation in data science for students
Data Visualisation in data science for studentsconfidenceascend
?
Data visualisation is explained in a simple manner.SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptx
SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptxbhavaniteacher99
?
Unlock the power of data with “SQL Demystified” — your perfect beginner-friendly guide to mastering Structured Query Language (SQL)! Whether you're a student, aspiring analyst, or a professional stepping into the world of data, this detailed and practical PowerPoint presentation simplifies the fundamentals and builds your SQL skills step-by-step.
? What’s Inside?
Introduction to SQL and its real-world importance
Understanding database structures: tables, columns & relationships
Core SQL queries: SELECT, FROM, WHERE, ORDER BY
Hands-on examples and practice exercises
Advanced SQL: JOIN, subqueries, and aggregate functions
Data manipulation: INSERT, UPDATE, DELETE
Performance tips and indexing basics
SQL security best practices and learning paths for future growth
? Who is it for?
Beginners eager to learn SQL
Data science and computer students
Business professionals and analysts
Anyone curious about how databases work
? Features:
Clear visuals and examples
Easy-to-follow explanations
Real-life applications in banking, healthcare, e-commerce, and more
Reusable and editable presentation file (PPTX format)
Take your first step toward data mastery with confidence. Learn SQL the smart way — with structure, clarity, and purpose.Data Warehousing and Analytics IFI Techsolutions .pptx
Data Warehousing and Analytics IFI Techsolutions .pptxIFI Techsolutions
?
IFI Techsolutions is a leading cloud solutions and managed services provider. We are an Azure Expert Managed Service Provider (AEMSP) and boast multiple advanced specializations in Infra and Database Migration to Microsoft Azure, Migrate Enterprise Apps to Microsoft Azure, and Microsoft Azure Virtual Desktop.
Simplify SQL migration to Azure with Microsoft’s DMA tool—assess, plan, and move your data seamlessly with expert support from IFI Tech.
FME Beyond Data Processing: Creating a Dartboard Accuracy App
FME Beyond Data Processing: Creating a Dartboard Accuracy Appjacoba18
?
Bij Nordend vroegen we ons af of FME ons kan helpen bepalen waar we moeten mikken voor de hoogste score, gebaseerd op onze nauwkeurigheid. We tonen hoe we met FME Flow, een zelfgemaakte WMS-dartbordlaag en geanalyseerde gooiresultaten optimale mikpunten berekenden.Ad
Ayasdi Energy Summit, September 2014, Gunnar Carlsson
- 1. Energy Gunnar Carlsson President & Co-Founder, Ayasdi
- 2. AYASDI Company Timeline 2000 2005 2008 2010 2013 “ Ayasdi’s approach is using Topological Data Analysis one of the top 10 innovations developed at DARPA in the last decade. ” Tony Tether, Director Defense Advanced Research Projects Agency (2001-2009)
- 3. Data has shape, Shape has meaning.
- 5. Clustering
- 6. v v v v v Circular
- 7. Limitations with Current Methods Statistics Machine Learning ? Hypothesis focused ? Model Driven ? Formula Driven ? Black-box analytics Miss subtle signals Missed systematic phenomena
- 8. Real Data = Real Complex ? Real World Data does not adhere to models ? Deep analysis requires taking the “model” assumption out of the equation Ayasdi Core analyzes the data you have, not the data you want to have.
- 9. Ayasdi’s Approach Key Takeaways 1. Segmentation ? Ex: Understanding how differences in completion can impact recovery 1. Subtle Feature Extraction ? Ex: identifying additional geological features that play a role in predicting recovery 1. Anomaly Detection ? Ex: Understanding state changes in SAGD wells Statistics Machine Learning Topological Data Analysis
- 10. Key Properties of TDA 1 Coordinate Freeness Source Agnostic
- 11. Key Properties of TDA 2 Deformation Invariance Noise & Null Tolerant
- 12. Key Properties of TDA 3 Compressed Representation Expose All Signals
- 13. Mapping F
- 14. 14 Network Orientation The shape of the network shows underlying properties of data that yield insights and meaning Nodes are groups of similar objects Edges connect similar nodes Colors let you see values of interest Position of a node on the screen doesn’t matter
- 15. Relationships between diabetic, pre-diabetic and healthy populations Glucose Level Insulin Response Healthy Pre-Diabetic Overt-Diabetic
- 16. Analyzing Breast Cancer Data Death Survive d Relapsed No Relapse
- 17. Model Validation Understanding failure and improving performance Colored by model prediction Colored by actual outcomes Survival Low High
Editor's Notes
- #3: Introduce slide with the PUNCHLINE: AYASDI is a new old company – still growing and dynamically adapting to use cases, but founded on an established and proven technology 狠狠撸 intended to establish institutional validity from both intellectual (Stanford / DARPA / NSF / Gunnar) and financial (Khosla / Floodgate / IVP / GE / Citi) standpoint Narrative: 2000 Initial Research – NSF funds Stanford Math Professor Gunnar Carlsson to conduct theoretical research into computational topology 2005 DARPA – DARPA invests in applying TDA to massive, complex, multi-modal DoD data 2008 AYASDI founded to explore commercial applications with DARPA and IARPA funding 2010 Floodgate / Ovitz (Seed) –Floodgate and private investors provide seed capital to develop platform 2013 Khosla / IVP / Citi / GE (Growth) – Khosla Ventures and Institutional Venture Partners lead growth financing rounds, scale for institutional deployment
- #5: Linear example Data has shape, shape has meaning
- #6: Clustering Example
- #7: Circle example
- #8: Need real life examples for oil & Gas
- #9: Real Data is complex and unpredictable? it doesn’t adhere to models and form shapes nicely Ayasdi ? transition to the flexibility of tda The actual data that youhave, not the data that can fit in your own model
- #10: Title of use cases needs work….
- #14: Change title? ask Gunnar
- #15: A node is a group of data points that are similar to each other. Edges connect nodes with data points in common. The position of a node on the screen doesn’t matter – the network is not a plot, there are no axes. What matters is how a node is connected to the nodes around it, and the network as a whole. We use color to explore correlations between data aspects (attributes/features/columns) and the structure of the network.
- #16: Glucose Level Insulin Response Healthy Pre-diabetic Overt-diabetic
- #17: Glucose Level Insulin Response Healthy Pre-diabetic Overt-diabetic
- #18: NB: this is an Iris 2 network and if we can find the data we will work to replace it. Data Patients coming into an emergent care center are assessed for a number of different factors – did they come in on their own? Are they actively bleeding? Can they answer questions? Network built on that worksheet information Meta data composed of 1) model prediction, and 2) actual outcome Network Each node is a group of patients similar to each other Red nodes indicate poor survival, blue nodes indicate good survival, either predicted (left) or actual (right) Punchline: the flare at the top is a group of patients for whom the model fails to predict poor outcomes. We can explore the statistics of this flare, and find that the key distinguishing feature is missing information, from one section of the worksheet in particular.