180 Designing Machine Learning Systems with Python Criteria for Multi-purpose Projects

What is involved in Machine learning

Find out what the related areas are that Machine learning connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Machine learning thinking-frame.

How far is your company on its Designing Machine Learning Systems with Python journey?

Take this short survey to gauge your organization’s progress toward Designing Machine Learning Systems with Python leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Machine learning related domains to cover and 180 essential critical questions to check off in that domain.

The following domains are covered:

Machine learning, Temporal difference learning, Similarity learning, Computational learning theory, AT&T Labs, Artificial neural network, Inductive programming, Oracle Corporation, Feature learning, Machine ethics, Generalized linear model, Time series, Density estimation, Computational anatomy, DNA sequence, Learning to rank, Pattern recognition, Netflix Prize, Empirical risk minimization, Dimensionality reduction, Artificial immune system, Microsoft Cognitive Toolkit, Semi-supervised learning, Genetic algorithm, Email filtering, Vinod Khosla, Sentiment analysis, Recurrent neural network, Machine perception, Ensemble Averaging, Data collection, Linear classifier, Vapnik–Chervonenkis theory, Data analytics, Google APIs, Data science, Sensitivity and specificity, Multi-label classification, Association rule learning, Oracle Data Mining, Structural health monitoring, Decision tree learning, Object recognition, Naive Bayes classifier, Data breach, Decision tree, User behavior analytics, Artificial Intelligence, Conditional random field, Text corpus, International Conference on Machine Learning, Active learning, Logistic regression, K-nearest neighbors algorithm, Learning classifier system, Autonomous car, Convolutional neural network, Knowledge discovery:

Machine learning Critical Criteria:

Pilot Machine learning issues and interpret which customers can’t participate in Machine learning because they lack skills.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– What are our needs in relation to Machine learning skills, labor, equipment, and markets?

– Do the Machine learning decisions we make today help people and the planet tomorrow?

– What are your most important goals for the strategic Machine learning objectives?

Temporal difference learning Critical Criteria:

Detail Temporal difference learning quality and do something to it.

– Which individuals, teams or departments will be involved in Machine learning?

– Does the Machine learning task fit the clients priorities?

– What is our Machine learning Strategy?

Similarity learning Critical Criteria:

Participate in Similarity learning tactics and point out improvements in Similarity learning.

– Who will be responsible for deciding whether Machine learning goes ahead or not after the initial investigations?

– What is our formula for success in Machine learning ?

Computational learning theory Critical Criteria:

Contribute to Computational learning theory projects and describe the risks of Computational learning theory sustainability.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Machine learning services/products?

– What other jobs or tasks affect the performance of the steps in the Machine learning process?

– How will you know that the Machine learning project has been successful?

AT&T Labs Critical Criteria:

Shape AT&T Labs governance and find answers.

– Where do ideas that reach policy makers and planners as proposals for Machine learning strengthening and reform actually originate?

– How do we Improve Machine learning service perception, and satisfaction?

– How do we manage Machine learning Knowledge Management (KM)?

Artificial neural network Critical Criteria:

Mine Artificial neural network adoptions and pioneer acquisition of Artificial neural network systems.

– Does Machine learning systematically track and analyze outcomes for accountability and quality improvement?

– What new services of functionality will be implemented next with Machine learning ?

– Who are the people involved in developing and implementing Machine learning?

Inductive programming Critical Criteria:

Understand Inductive programming leadership and reinforce and communicate particularly sensitive Inductive programming decisions.

Oracle Corporation Critical Criteria:

Chat re Oracle Corporation results and ask what if.

– Which customers cant participate in our Machine learning domain because they lack skills, wealth, or convenient access to existing solutions?

– To what extent does management recognize Machine learning as a tool to increase the results?

– How do we go about Securing Machine learning?

Feature learning Critical Criteria:

Wrangle Feature learning goals and define what do we need to start doing with Feature learning.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Machine learning in a volatile global economy?

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Machine learning processes?

– Among the Machine learning product and service cost to be estimated, which is considered hardest to estimate?

Machine ethics Critical Criteria:

Give examples of Machine ethics projects and simulate teachings and consultations on quality process improvement of Machine ethics.

– What are our best practices for minimizing Machine learning project risk, while demonstrating incremental value and quick wins throughout the Machine learning project lifecycle?

– Will Machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– What are our Machine learning Processes?

Generalized linear model Critical Criteria:

Air ideas re Generalized linear model goals and find the ideas you already have.

– At what point will vulnerability assessments be performed once Machine learning is put into production (e.g., ongoing Risk Management after implementation)?

– Do several people in different organizational units assist with the Machine learning process?

– Do we have past Machine learning Successes?

Time series Critical Criteria:

See the value of Time series engagements and report on the economics of relationships managing Time series and constraints.

– Do Machine learning rules make a reasonable demand on a users capabilities?

– Have all basic functions of Machine learning been defined?

Density estimation Critical Criteria:

Read up on Density estimation visions and define what our big hairy audacious Density estimation goal is.

– Is Machine learning dependent on the successful delivery of a current project?

– What potential environmental factors impact the Machine learning effort?

Computational anatomy Critical Criteria:

Sort Computational anatomy failures and work towards be a leading Computational anatomy expert.

– Does Machine learning include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?

– What are the short and long-term Machine learning goals?

– Is the scope of Machine learning defined?

DNA sequence Critical Criteria:

Derive from DNA sequence visions and slay a dragon.

– Are assumptions made in Machine learning stated explicitly?

– What are the Essentials of Internal Machine learning Management?

Learning to rank Critical Criteria:

Demonstrate Learning to rank risks and cater for concise Learning to rank education.

– What will be the consequences to the business (financial, reputation etc) if Machine learning does not go ahead or fails to deliver the objectives?

– How do mission and objectives affect the Machine learning processes of our organization?

– What is the source of the strategies for Machine learning strengthening and reform?

Pattern recognition Critical Criteria:

Closely inspect Pattern recognition outcomes and describe which business rules are needed as Pattern recognition interface.

– Think of your Machine learning project. what are the main functions?

Netflix Prize Critical Criteria:

Coach on Netflix Prize goals and point out Netflix Prize tensions in leadership.

– In the case of a Machine learning project, the criteria for the audit derive from implementation objectives. an audit of a Machine learning project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Machine learning project is implemented as planned, and is it working?

– How do we ensure that implementations of Machine learning products are done in a way that ensures safety?

– What are current Machine learning Paradigms?

Empirical risk minimization Critical Criteria:

Disseminate Empirical risk minimization outcomes and find the ideas you already have.

– Think about the kind of project structure that would be appropriate for your Machine learning project. should it be formal and complex, or can it be less formal and relatively simple?

– Are we Assessing Machine learning and Risk?

Dimensionality reduction Critical Criteria:

Generalize Dimensionality reduction governance and prioritize challenges of Dimensionality reduction.

– What tools do you use once you have decided on a Machine learning strategy and more importantly how do you choose?

– How to deal with Machine learning Changes?

Artificial immune system Critical Criteria:

Analyze Artificial immune system issues and achieve a single Artificial immune system view and bringing data together.

– What are the record-keeping requirements of Machine learning activities?

Microsoft Cognitive Toolkit Critical Criteria:

Be responsible for Microsoft Cognitive Toolkit planning and clarify ways to gain access to competitive Microsoft Cognitive Toolkit services.

– Think about the people you identified for your Machine learning project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?

– How do you determine the key elements that affect Machine learning workforce satisfaction? how are these elements determined for different workforce groups and segments?

– Do we monitor the Machine learning decisions made and fine tune them as they evolve?

Semi-supervised learning Critical Criteria:

Have a meeting on Semi-supervised learning planning and inform on and uncover unspoken needs and breakthrough Semi-supervised learning results.

– For your Machine learning project, identify and describe the business environment. is there more than one layer to the business environment?

– What will drive Machine learning change?

Genetic algorithm Critical Criteria:

Study Genetic algorithm strategies and create a map for yourself.

– What tools and technologies are needed for a custom Machine learning project?

– Is Machine learning Realistic, or are you setting yourself up for failure?

Email filtering Critical Criteria:

Focus on Email filtering governance and suggest using storytelling to create more compelling Email filtering projects.

– Is there a Machine learning Communication plan covering who needs to get what information when?

– Is the Machine learning organization completing tasks effectively and efficiently?

– What sources do you use to gather information for a Machine learning study?

Vinod Khosla Critical Criteria:

Have a session on Vinod Khosla outcomes and document what potential Vinod Khosla megatrends could make our business model obsolete.

– what is the best design framework for Machine learning organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– Have the types of risks that may impact Machine learning been identified and analyzed?

Sentiment analysis Critical Criteria:

Have a round table over Sentiment analysis quality and develop and take control of the Sentiment analysis initiative.

– How representative is twitter sentiment analysis relative to our customer base?

– What are internal and external Machine learning relations?

– How will you measure your Machine learning effectiveness?

– Are there Machine learning Models?

Recurrent neural network Critical Criteria:

Distinguish Recurrent neural network tactics and probe the present value of growth of Recurrent neural network.

– What are the Key enablers to make this Machine learning move?

Machine perception Critical Criteria:

Nurse Machine perception decisions and assess and formulate effective operational and Machine perception strategies.

– Consider your own Machine learning project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

– What are the key elements of your Machine learning performance improvement system, including your evaluation, organizational learning, and innovation processes?

Ensemble Averaging Critical Criteria:

Focus on Ensemble Averaging decisions and correct Ensemble Averaging management by competencies.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Machine learning processes?

– Why should we adopt a Machine learning framework?

Data collection Critical Criteria:

Inquire about Data collection outcomes and tour deciding if Data collection progress is made.

– Were changes made during the file extract period to how the data are processed, such as changes to mode of data collection, changes to instructions for completing the application form, changes to the edit, changes to classification codes, or changes to the query system used to retrieve the data?

– Does the design of the program/projects overall data collection and reporting system ensure that, if implemented as planned, it will collect and report quality data?

– How is source data collected (paper questionnaire, computer assisted person interview, computer assisted telephone interview, web data collection form)?

– What should I consider in selecting the most resource-effective data collection design that will satisfy all of my performance or acceptance criteria?

– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?

– Do we double check that the data collected follows the plans and procedures for data collection?

– Do data reflect stable and consistent data collection processes and analysis methods over time?

– What is the definitive data collection and what is the legacy of said collection?

– Do you have policies and procedures which direct your data collection process?

– Do we use controls throughout the data collection and management process?

– Do you define jargon and other terminology used in data collection tools?

– How can the benefits of Big Data collection and applications be measured?

– Do you use the same data collection methods for all sites?

– Do you clearly document your data collection methods?

– Is our data collection and acquisition optimized?

Linear classifier Critical Criteria:

Bootstrap Linear classifier leadership and adopt an insight outlook.

– Are we making progress? and are we making progress as Machine learning leaders?

Vapnik–Chervonenkis theory Critical Criteria:

Do a round table on Vapnik–Chervonenkis theory tasks and check on ways to get started with Vapnik–Chervonenkis theory.

– Does Machine learning appropriately measure and monitor risk?

– How can we improve Machine learning?

Data analytics Critical Criteria:

Audit Data analytics tasks and drive action.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Can we be rewired to use the power of data analytics to improve our management of human capital?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– Which departments in your organization are involved in using data technologies and data analytics?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– Social Data Analytics Are you integrating social into your business intelligence?

– How will we insure seamless interoperability of Machine learning moving forward?

– what is the difference between Data analytics and Business Analytics If Any?

– Does your organization have a strategy on big data or data analytics?

– What are our tools for big data analytics?

Google APIs Critical Criteria:

Define Google APIs outcomes and get the big picture.

– How do we know that any Machine learning analysis is complete and comprehensive?

– How to Secure Machine learning?

Data science Critical Criteria:

Concentrate on Data science leadership and display thorough understanding of the Data science process.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Machine learning?

Sensitivity and specificity Critical Criteria:

Focus on Sensitivity and specificity planning and know what your objective is.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Machine learning process. ask yourself: are the records needed as inputs to the Machine learning process available?

– What are the barriers to increased Machine learning production?

– Is Machine learning Required?

Multi-label classification Critical Criteria:

Participate in Multi-label classification issues and define what our big hairy audacious Multi-label classification goal is.

– What is the total cost related to deploying Machine learning, including any consulting or professional services?

– How can the value of Machine learning be defined?

Association rule learning Critical Criteria:

X-ray Association rule learning engagements and catalog Association rule learning activities.

– Do those selected for the Machine learning team have a good general understanding of what Machine learning is all about?

– What are the long-term Machine learning goals?

Oracle Data Mining Critical Criteria:

Apply Oracle Data Mining tasks and display thorough understanding of the Oracle Data Mining process.

– How does the organization define, manage, and improve its Machine learning processes?

– How would one define Machine learning leadership?

– How do we maintain Machine learnings Integrity?

Structural health monitoring Critical Criteria:

Reconstruct Structural health monitoring governance and raise human resource and employment practices for Structural health monitoring.

– Is maximizing Machine learning protection the same as minimizing Machine learning loss?

Decision tree learning Critical Criteria:

Reconstruct Decision tree learning issues and develop and take control of the Decision tree learning initiative.

Object recognition Critical Criteria:

Have a round table over Object recognition governance and summarize a clear Object recognition focus.

– How likely is the current Machine learning plan to come in on schedule or on budget?

Naive Bayes classifier Critical Criteria:

Systematize Naive Bayes classifier tactics and develop and take control of the Naive Bayes classifier initiative.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Machine learning. How do we gain traction?

Data breach Critical Criteria:

Frame Data breach issues and balance specific methods for improving Data breach results.

– One day; you may be the victim of a data breach and need to answer questions from customers and the press immediately. Are you ready for each possible scenario; have you decided on a communication plan that reduces the impact on your support team while giving the most accurate information to the data subjects? Who is your company spokesperson and will you be ready even if the breach becomes public out of usual office hours?

– Have policies and procedures been established to ensure the continuity of data services in an event of a data breach, loss, or other disaster (this includes a disaster recovery plan)?

– What staging or emergency preparation for a data breach or E-Discovery could be established ahead of time to prepare or mitigate a data breach?

– Would you be able to notify a data protection supervisory authority of a data breach within 72 hours?

– Data breach notification: what to do when your personal data has been breached?

– Do you have a communication plan ready to go after a data breach?

– What business benefits will Machine learning goals deliver if achieved?

– How does the GDPR affect policy surrounding data breaches?

– Are you sure you can detect data breaches?

– Who is responsible for a data breach?

Decision tree Critical Criteria:

Apply Decision tree engagements and grade techniques for implementing Decision tree controls.

– Do you monitor the effectiveness of your Machine learning activities?

User behavior analytics Critical Criteria:

Bootstrap User behavior analytics failures and assess and formulate effective operational and User behavior analytics strategies.

– What prevents me from making the changes I know will make me a more effective Machine learning leader?

– Risk factors: what are the characteristics of Machine learning that make it risky?

– Can we do Machine learning without complex (expensive) analysis?

Artificial Intelligence Critical Criteria:

Focus on Artificial Intelligence tactics and test out new things.

– Is Supporting Machine learning documentation required?

– Who needs to know about Machine learning ?

Conditional random field Critical Criteria:

Model after Conditional random field quality and explain and analyze the challenges of Conditional random field.

– Have you identified your Machine learning key performance indicators?

– Does our organization need more Machine learning education?

Text corpus Critical Criteria:

Confer over Text corpus tasks and adjust implementation of Text corpus.

– What are the success criteria that will indicate that Machine learning objectives have been met and the benefits delivered?

– Does Machine learning create potential expectations in other areas that need to be recognized and considered?

– Does Machine learning analysis isolate the fundamental causes of problems?

International Conference on Machine Learning Critical Criteria:

Canvass International Conference on Machine Learning risks and probe International Conference on Machine Learning strategic alliances.

– Can Management personnel recognize the monetary benefit of Machine learning?

Active learning Critical Criteria:

Demonstrate Active learning management and clarify ways to gain access to competitive Active learning services.

– How do your measurements capture actionable Machine learning information for use in exceeding your customers expectations and securing your customers engagement?

Logistic regression Critical Criteria:

Set goals for Logistic regression failures and raise human resource and employment practices for Logistic regression.

– Do we all define Machine learning in the same way?

K-nearest neighbors algorithm Critical Criteria:

Probe K-nearest neighbors algorithm visions and gather K-nearest neighbors algorithm models .

Learning classifier system Critical Criteria:

Interpolate Learning classifier system risks and overcome Learning classifier system skills and management ineffectiveness.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Machine learning models, tools and techniques are necessary?

Autonomous car Critical Criteria:

Reorganize Autonomous car planning and modify and define the unique characteristics of interactive Autonomous car projects.

– What role does communication play in the success or failure of a Machine learning project?

Convolutional neural network Critical Criteria:

Generalize Convolutional neural network governance and spearhead techniques for implementing Convolutional neural network.

– What are your results for key measures or indicators of the accomplishment of your Machine learning strategy and action plans, including building and strengthening core competencies?

Knowledge discovery Critical Criteria:

Focus on Knowledge discovery failures and be persistent.

– What vendors make products that address the Machine learning needs?

– What are the usability implications of Machine learning actions?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Designing Machine Learning Systems with Python Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | http://theartofservice.com



Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Machine learning External links:

Machine Learning
http://Ad · www.sas.com/machine-learning

Appen: high-quality training data for machine learning

Machine Learning | Coursera

Temporal difference learning External links:

[PDF]Proximal Gradient Temporal Difference Learning …

[PDF]Multi Stage Temporal Difference Learning for 2048 …

Richard Sutton on Temporal Difference Learning – YouTube

Similarity learning External links:

[PDF]Similarity Learning with (or without) Convolutional …

Similarity Learning of Manifold Data.

[PDF]Deep Unsupervised Similarity Learning using …

Computational learning theory External links:

Computational Learning Theory: PAC Learning

[PDF]Computational Learning Theory – PAC Learning

COLT 2015 – Computational Learning Theory

Artificial neural network External links:

Stock market index prediction using artificial neural network

What is bias in artificial neural network? – Quora


Inductive programming External links:

[PDF]Inductive Programming: A Survey of Program …

Inductive programming
http://Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete specifications, such as input/output examples or constraints.

[PDF]An introduction to inductive programming – …

Oracle Corporation External links:

ORCL : Summary for Oracle Corporation – Yahoo Finance

Oracle: Beefing Up In IaaS – Oracle Corporation …

Oracle Corporation Common Stock (ORCL) – NASDAQ.com

Feature learning External links:

[1605.09782] Adversarial Feature Learning – arXiv

[PDF]PointNet++: Deep Hierarchical Feature Learning on …

Unsupervised Feature Learning and Deep Learning Tutorial

Machine ethics External links:

[PDF]Intelligence Explosion and Machine Ethics

Generalized linear model External links:

R: Fit a Negative Binomial Generalized Linear Model

[PDF]Generalized Linear Model Theory – Princeton University

[PDF]SAS Software to Fit the Generalized Linear Model

Time series External links:

[PDF]Time Series Analysis and Its Applications: With R …

[PDF]Time series Forecasting using Holt-Winters …

[PDF]Time Series Analysis and Forecasting – Cengage

Density estimation External links:

[PDF]L7: Kernel density estimation

What is kernel density estimation? – Quora

Spectral Density Estimation / Spectral Analysis | STAT 510

Computational anatomy External links:

MeCA research group » Methods and Computational Anatomy

[PDF]Manual Computational Anatomy Toolbox – CAT12 – …

Computational Anatomy of Visual Neglect | Cerebral …

DNA sequence External links:

DNA Sequence Assembly | HHMI BioInteractive

How to Get a tRNA Sequence from a DNA Sequence | …

Learning to rank External links:

[PDF]Learning to Rank (part 2) – Filip Radlinski

Learning to rank – dl.acm.org

Microsoft Learning to Rank Datasets – Microsoft Research

Pattern recognition External links:

Pattern Recognition | MarketSmith

Mike the Knight Potion Practice: Pattern Recognition

Pattern Recognition | Board Game Mechanic | …

Netflix Prize External links:

Netflix Prize: FAQ

From the Labs: Winning the Netflix Prize – YouTube

Netflix Prize – Official Site

Empirical risk minimization External links:

[PDF]Empirical Risk Minimization and Optimization 1 …

10: Empirical Risk Minimization – Cornell University

Dimensionality reduction External links:

Dimensionality Reduction and Feature Extraction – …

Why is dimensionality reduction useful? – Quora

Artificial immune system External links:

Artificial immune system for fixed head hydrothermal …

[PDF]Artificial Immune System Approach for Air Comb at …
https://ti.arc.nasa.gov/m/pub-archive/1309h/1309 (Kaneshige).pdf

Microsoft Cognitive Toolkit External links:

Microsoft Cognitive Toolkit (CNTK) Demo – YouTube

The Microsoft Cognitive Toolkit | Microsoft Docs

Microsoft Cognitive Toolkit

Semi-supervised learning External links:

Semi-supervised Learning explained – YouTube

Title: Semi-Supervised Learning with Deep Generative Models

[PDF]Semi-Supervised Learning with Generative …

Genetic algorithm External links:

genetic algorithm cars – rednuht.org

Genetic Algorithm – MATLAB & Simulink

[PDF]Genetic Algorithm for Solving Simple Mathematical …

Email filtering External links:

The Anti-Spam and Email Filtering Experts | Roaring Penguin

Student Email – Email Filtering | Pierce College District

SpamExperts | Email Filtering & Archiving Solutions

Vinod Khosla External links:

2017 Book Recommendations: – Vinod Khosla – Medium

Vinod Khosla – Forbes

Vinod Khosla (@vkhosla) | Twitter

Sentiment analysis External links:

Social Sentiment Analysis – Real Time Sentiment
http://Ad · go.nuvi.com/sentiment/real-time

Sentiment Analysis | Lexalytics

Sentiment Analysis – Brandwatch

Recurrent neural network External links:

MariFlow – Self-Driving Mario Kart w/Recurrent Neural Network

How to build a Recurrent Neural Network in TensorFlow (1/7)

Machine perception External links:


Machine Perception & Cognitive Robotics Laboratory – …

sunzuolei (Machine Perception and Interaction Group) · GitHub

Ensemble Averaging External links:

ECE-340: L27 – Ensemble Averaging (00.45.54) – YouTube

[PDF]Ensemble Averaging – Department of Civil Engineering

Data collection External links:

Welcome | Data Collection

Civil Rights Data Collection

Linear classifier External links:

[PDF]A Linear Classifier Based on Entity Recognition …

Data analytics External links:

What is Data Analytics? – Definition from Techopedia

What is Data Analytics? – Definition from Techopedia

Google APIs External links:

Google APIs Explorer – code.google.com

Google APIs Explorer

Is there a link to the “latest” jQuery library on Google APIs?

Data science External links:

AnacondaCON ’18 | Data Science Conference

Data Science Masters Program | Duke University

What is data science – Data Science at NYU

Sensitivity and specificity External links:

[PDF]Sensitivity and specificity of information criteria

Sensitivity and Specificity – Emory University

Sensitivity and Specificity – Aaron Swanson, PT

Multi-label classification External links:

[PDF]Multiclass and Multi-label Classification

Association rule learning External links:

Association Rule Learning Task – GM-RKB

Oracle Data Mining External links:

oracle data mining « Oralytics

8 Text Mining Using Oracle Data Mining – Oracle Help Center

Data Profiling With Oracle Data Mining – DZone Big Data

Structural health monitoring External links:

Structural health monitoring
http://The process of implementing a damage detection and characterization strategy for engineering structures is referred to as Structural Health Monitoring (SHM). Here damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system’s performance.

Structural Health Monitoring: SAGE Journals

Structural Health Monitoring | Intelligent Structures

Decision tree learning External links:

Lecture 11: Decision Tree Learning – Imperial College London

[PDF]Decision Tree Learning on Very Large Data Sets

[PDF]Decision Tree Learning – University of Wisconsin …

Object recognition External links:

Lecture 6: Object recognition Flashcards | Quizlet

nyris – visual product search engine & object recognition …

Visual object recognition (eBook, 2011) [WorldCat.org]

Data breach External links:

Equifax Data Breach FAQs | TransUnion

What is data breach? – Definition from WhatIs.com

Equifax data breach: What you need to know – Sep. 8, 2017

Decision tree External links:

What is a Decision Tree Diagram | Lucidchart

[PDF]Decision Tree for Summary Rating Discussions

Decision Tree Analysis – Decision Skills from MindTools.com

User behavior analytics External links:

IBM QRadar User Behavior Analytics – Overview – United …

Network User Monitoring with User Behavior Analytics | …

Veriato Recon | User Behavior Analytics Software

Artificial Intelligence External links:

Robotics & Artificial Intelligence ETF – Global X Funds

Simple examples of Artificial Intelligence – Stack Exchange

Conditional random field External links:

[PDF]Conditional Random Field Autoencoders for …

[PDF]A Conditional Random Field Word Segmenter for …

Text corpus External links:

What is TEXT CORPUS? What does TEXT CORPUS mean? …

ERIC – A Text Corpus Approach to an Analysis of the …

The Electronic Text Corpus of Sumerian Royal Inscriptions

International Conference on Machine Learning External links:

International Conference on Machine Learning – 10times

International Conference on Machine Learning – Home | Facebook

Active learning External links:

What is Active Learning? | Center for Educational Innovation

Active Learning | CRLT

Active Learning Strategies | Center for Teaching & Learning

Logistic regression External links:

[PDF]11 Logistic Regression – Interpreting Parameters

Multinomial Logistic Regression – IDRE Stats

Logistic Regression – University of South Florida

K-nearest neighbors algorithm External links:

Using the k-Nearest Neighbors Algorithm in R « Web Age …

Learning classifier system External links:

[PDF]A Learning Classifier System with Mutual-Information …

Autonomous car External links:

What is an Autonomous Car? – Definition from …

Convolutional neural network External links:

Convolutional Neural Network – MATLAB & Simulink

[PDF]1 Deep Convolutional Neural Network for Inverse …

[PDF]Multi-Task Convolutional Neural Network for Pose …

Knowledge discovery External links:

[PDF]Knowledge Discovery with Visual Informatics – …

Data Mining – Knowledge Discovery – Tutorials Point

Knowledge Discovery and Data Mining – IBM