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 133 essential critical questions to check off in that domain.
The following domains are covered:
Machine learning, Financial market, Ensemble learning, KXEN Inc., Topic modeling, Search algorithm, Generalized linear model, Oracle Data Mining, Image de-noising, Empirical risk minimization, Probably approximately correct learning, Support vector machine, Sparse coding, Developmental robotics, Probability theory, Affective computing, International Conference on Machine Learning, Network simulation, Independent component analysis, Expectation–maximization algorithm, Reinforcement learning, Operational definition, Decision tree, ECML PKDD, Artificial Intelligence, Object recognition, Vinod Khosla, Principal component analysis, Logic programming, Bias-variance dilemma, K-nearest neighbors algorithm, Functional programming, Evolutionary algorithm, Conference on Neural Information Processing Systems, Association rule learning, Temporal difference learning, Time complexity, Machine perception, Anomaly detection, Graphical model, Data analysis, Knowledge discovery, Structured prediction, Automated theorem proving, Predictive analytics, Structural health monitoring, AT&T Labs, Online advertising, Algorithmic bias, Hierarchical clustering, Machine learning control, Non-negative matrix factorization, Cluster analysis, Robot learning, Neural network, Random variables, GNU Octave, Mathematical optimization, Linear discriminant analysis, Recurrent neural network, Optical character recognition, Statistical learning theory:
Machine learning Critical Criteria:
Design Machine learning strategies and oversee Machine learning requirements.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– How do we know that any Machine learning analysis is complete and comprehensive?
– What business benefits will Machine learning goals deliver if achieved?
Financial market Critical Criteria:
Grade Financial market tasks and assess what counts with Financial market that we are not counting.
– Where do ideas that reach policy makers and planners as proposals for Machine learning strengthening and reform actually originate?
– Is Supporting Machine learning documentation required?
– Who sets the Machine learning standards?
Ensemble learning Critical Criteria:
Unify Ensemble learning goals and finalize the present value of growth of Ensemble learning.
– 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?
– Risk factors: what are the characteristics of Machine learning that make it risky?
– Have the types of risks that may impact Machine learning been identified and analyzed?
KXEN Inc. Critical Criteria:
Incorporate KXEN Inc. goals and use obstacles to break out of ruts.
– What other jobs or tasks affect the performance of the steps in the Machine learning process?
– Do we monitor the Machine learning decisions made and fine tune them as they evolve?
– What are internal and external Machine learning relations?
Topic modeling Critical Criteria:
Focus on Topic modeling outcomes and observe effective Topic modeling.
– 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?
– Is Machine learning dependent on the successful delivery of a current project?
– Who are the people involved in developing and implementing Machine learning?
Search algorithm Critical Criteria:
Define Search algorithm leadership and develop and take control of the Search algorithm initiative.
– Will new equipment/products be required to facilitate Machine learning delivery for example is new software needed?
– How will we insure seamless interoperability of Machine learning moving forward?
– What will drive Machine learning change?
Generalized linear model Critical Criteria:
Judge Generalized linear model visions and handle a jump-start course to Generalized linear model.
– What sources do you use to gather information for a Machine learning study?
– What about Machine learning Analysis of results?
Oracle Data Mining Critical Criteria:
Accumulate Oracle Data Mining engagements and perfect Oracle Data Mining conflict management.
– Will Machine learning have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Why is Machine learning important for you now?
Image de-noising Critical Criteria:
Consolidate Image de-noising decisions and optimize Image de-noising leadership as a key to advancement.
– 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?
– Who will be responsible for deciding whether Machine learning goes ahead or not after the initial investigations?
Empirical risk minimization Critical Criteria:
Design Empirical risk minimization planning and get the big picture.
– What threat is Machine learning addressing?
Probably approximately correct learning Critical Criteria:
Derive from Probably approximately correct learning visions and grade techniques for implementing Probably approximately correct learning controls.
– What will be the consequences to the business (financial, reputation etc) if Machine learning does not go ahead or fails to deliver the objectives?
– Who needs to know about Machine learning ?
Support vector machine Critical Criteria:
Distinguish Support vector machine leadership and grade techniques for implementing Support vector machine controls.
– What tools do you use once you have decided on a Machine learning strategy and more importantly how do you choose?
– Who will be responsible for documenting the Machine learning requirements in detail?
Sparse coding Critical Criteria:
Examine Sparse coding projects and be persistent.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Machine learning processes?
– Is Machine learning Realistic, or are you setting yourself up for failure?
Developmental robotics Critical Criteria:
Talk about Developmental robotics strategies and question.
– Who will be responsible for making the decisions to include or exclude requested changes once Machine learning is underway?
– What is our Machine learning Strategy?
Probability theory Critical Criteria:
Air ideas re Probability theory issues and oversee implementation of Probability theory.
– 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?
– What are the key elements of your Machine learning performance improvement system, including your evaluation, organizational learning, and innovation processes?
– How to Secure Machine learning?
Affective computing Critical Criteria:
Coach on Affective computing results and question.
– What are the record-keeping requirements of Machine learning activities?
– Do we all define Machine learning in the same way?
International Conference on Machine Learning Critical Criteria:
See the value of International Conference on Machine Learning failures and correct better engagement with International Conference on Machine Learning results.
– Can Management personnel recognize the monetary benefit of Machine learning?
– How can you measure Machine learning in a systematic way?
– Are we Assessing Machine learning and Risk?
Network simulation Critical Criteria:
Interpolate Network simulation visions and gather practices for scaling Network simulation.
– Is the Machine learning organization completing tasks effectively and efficiently?
– Why are Machine learning skills important?
Independent component analysis Critical Criteria:
Grasp Independent component analysis decisions and explain and analyze the challenges of Independent component analysis.
– Does Machine learning systematically track and analyze outcomes for accountability and quality improvement?
– How important is Machine learning to the user organizations mission?
– Are there Machine learning problems defined?
Expectation–maximization algorithm Critical Criteria:
Reorganize Expectation–maximization algorithm issues and get out your magnifying glass.
– Which individuals, teams or departments will be involved in Machine learning?
– Which Machine learning goals are the most important?
Reinforcement learning Critical Criteria:
Map Reinforcement learning projects and report on setting up Reinforcement learning without losing ground.
– How do we keep improving Machine learning?
– How can we improve Machine learning?
Operational definition Critical Criteria:
Start Operational definition risks and devote time assessing Operational definition and its risk.
– How do we Improve Machine learning service perception, and satisfaction?
– How can the value of Machine learning be defined?
Decision tree Critical Criteria:
Grade Decision tree visions and find out what it really means.
– What are the success criteria that will indicate that Machine learning objectives have been met and the benefits delivered?
ECML PKDD Critical Criteria:
Huddle over ECML PKDD tactics and handle a jump-start course to ECML PKDD.
– How do we measure improved Machine learning service perception, and satisfaction?
– What vendors make products that address the Machine learning needs?
Artificial Intelligence Critical Criteria:
Dissect Artificial Intelligence leadership and suggest using storytelling to create more compelling Artificial Intelligence projects.
– Is there a Machine learning Communication plan covering who needs to get what information when?
Object recognition Critical Criteria:
Have a session on Object recognition outcomes and give examples utilizing a core of simple Object recognition skills.
– 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?
– How can skill-level changes improve Machine learning?
Vinod Khosla Critical Criteria:
Chart Vinod Khosla engagements and don’t overlook the obvious.
– What are the top 3 things at the forefront of our Machine learning agendas for the next 3 years?
– How will you measure your Machine learning effectiveness?
Principal component analysis Critical Criteria:
Probe Principal component analysis planning and get out your magnifying glass.
– 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?
– How do mission and objectives affect the Machine learning processes of our organization?
– What is our formula for success in Machine learning ?
Logic programming Critical Criteria:
Weigh in on Logic programming leadership and stake your claim.
– What are specific Machine learning Rules to follow?
Bias-variance dilemma Critical Criteria:
Mine Bias-variance dilemma leadership and define what our big hairy audacious Bias-variance dilemma goal is.
K-nearest neighbors algorithm Critical Criteria:
Collaborate on K-nearest neighbors algorithm engagements and inform on and uncover unspoken needs and breakthrough K-nearest neighbors algorithm results.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Machine learning processes?
Functional programming Critical Criteria:
Win new insights about Functional programming quality and look at the big picture.
– Are assumptions made in Machine learning stated explicitly?
– Why should we adopt a Machine learning framework?
Evolutionary algorithm Critical Criteria:
Paraphrase Evolutionary algorithm planning and forecast involvement of future Evolutionary algorithm projects in development.
Conference on Neural Information Processing Systems Critical Criteria:
Adapt Conference on Neural Information Processing Systems visions and perfect Conference on Neural Information Processing Systems conflict management.
Association rule learning Critical Criteria:
Do a round table on Association rule learning goals and pay attention to the small things.
– What are your current levels and trends in key measures or indicators of Machine learning product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– What are the barriers to increased Machine learning production?
Temporal difference learning Critical Criteria:
Collaborate on Temporal difference learning tasks and correct Temporal difference learning management by competencies.
– Are there recognized Machine learning problems?
– What are current Machine learning Paradigms?
Time complexity Critical Criteria:
Set goals for Time complexity adoptions and observe effective Time complexity.
Machine perception Critical Criteria:
Scrutinze Machine perception management and find the ideas you already have.
– Why is it important to have senior management support for a Machine learning project?
Anomaly detection Critical Criteria:
Define Anomaly detection issues and devote time assessing Anomaly detection and its risk.
– 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?
– What are the long-term Machine learning goals?
Graphical model Critical Criteria:
Investigate Graphical model tactics and intervene in Graphical model processes and leadership.
– What are the disruptive Machine learning technologies that enable our organization to radically change our business processes?
– What is Effective Machine learning?
Data analysis Critical Criteria:
Debate over Data analysis issues and oversee Data analysis requirements.
– Which customers cant participate in our Machine learning domain because they lack skills, wealth, or convenient access to existing solutions?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What are some real time data analysis frameworks?
Knowledge discovery Critical Criteria:
Exchange ideas about Knowledge discovery results and probe Knowledge discovery strategic alliances.
– Do several people in different organizational units assist with the Machine learning process?
Structured prediction Critical Criteria:
Depict Structured prediction tactics and create Structured prediction explanations for all managers.
– At what point will vulnerability assessments be performed once Machine learning is put into production (e.g., ongoing Risk Management after implementation)?
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Machine learning services/products?
– Does Machine learning appropriately measure and monitor risk?
Automated theorem proving Critical Criteria:
Pilot Automated theorem proving adoptions and ask questions.
– How do we Identify specific Machine learning investment and emerging trends?
– Have all basic functions of Machine learning been defined?
Predictive analytics Critical Criteria:
Talk about Predictive analytics decisions and change contexts.
– How do we ensure that implementations of Machine learning products are done in a way that ensures safety?
– What are direct examples that show predictive analytics to be highly reliable?
– What are the Key enablers to make this Machine learning move?
Structural health monitoring Critical Criteria:
Discourse Structural health monitoring issues and innovate what needs to be done with Structural health monitoring.
– How can you negotiate Machine learning successfully with a stubborn boss, an irate client, or a deceitful coworker?
AT&T Labs Critical Criteria:
Canvass AT&T Labs leadership and spearhead techniques for implementing AT&T Labs.
Online advertising Critical Criteria:
Participate in Online advertising results and display thorough understanding of the Online advertising process.
– How do senior leaders actions reflect a commitment to the organizations Machine learning values?
– Is a Machine learning Team Work effort in place?
Algorithmic bias Critical Criteria:
Align Algorithmic bias projects and frame using storytelling to create more compelling Algorithmic bias projects.
Hierarchical clustering Critical Criteria:
Chart Hierarchical clustering outcomes and assess what counts with Hierarchical clustering that we are not counting.
– Are accountability and ownership for Machine learning clearly defined?
Machine learning control Critical Criteria:
Be clear about Machine learning control projects and modify and define the unique characteristics of interactive Machine learning control projects.
– How does the organization define, manage, and improve its Machine learning processes?
– What are the business goals Machine learning is aiming to achieve?
Non-negative matrix factorization Critical Criteria:
Canvass Non-negative matrix factorization risks and know what your objective is.
– Do those selected for the Machine learning team have a good general understanding of what Machine learning is all about?
– What is the total cost related to deploying Machine learning, including any consulting or professional services?
Cluster analysis Critical Criteria:
Rank Cluster analysis visions and visualize why should people listen to you regarding Cluster analysis.
– Does our organization need more Machine learning education?
– What are our Machine learning Processes?
Robot learning Critical Criteria:
Mine Robot learning projects and secure Robot learning creativity.
– What knowledge, skills and characteristics mark a good Machine learning project manager?
Neural network Critical Criteria:
Chat re Neural network governance and find the essential reading for Neural network researchers.
Random variables Critical Criteria:
Model after Random variables results and report on the economics of relationships managing Random variables and constraints.
– Meeting the challenge: are missed Machine learning opportunities costing us money?
GNU Octave Critical Criteria:
Judge GNU Octave management and find the ideas you already have.
– What are all of our Machine learning domains and what do they do?
Mathematical optimization Critical Criteria:
Start Mathematical optimization planning and research ways can we become the Mathematical optimization company that would put us out of business.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Machine learning process?
– What are the usability implications of Machine learning actions?
Linear discriminant analysis Critical Criteria:
Grasp Linear discriminant analysis tasks and ask what if.
Recurrent neural network Critical Criteria:
Match Recurrent neural network strategies and test out new things.
– Does Machine learning create potential expectations in other areas that need to be recognized and considered?
– What tools and technologies are needed for a custom Machine learning project?
Optical character recognition Critical Criteria:
Collaborate on Optical character recognition failures and mentor Optical character recognition customer orientation.
– 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?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Machine learning?
Statistical learning theory Critical Criteria:
Extrapolate Statistical learning theory projects and correct Statistical learning theory management by competencies.
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 | 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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Machine learning External links:
DataRobot – Automated Machine Learning for Predictive …
Microsoft Azure Machine Learning Studio
Machine Learning Mastery – Official Site
Financial market External links:
Financial Market Research Reports & Whitepapers | ResearchPool
Crestmont Research: Financial Market and Economic …
Notes From the Rabbit Hole, a unique financial market …
Ensemble learning External links:
[PDF]Online Ensemble Learning: An Empirical Study
Must-Know: What is the idea behind ensemble learning?
Scalable data analytics for ensemble learning
Topic modeling External links:
Topic Modeling – Text Mining with R
Topic modeling bibliography – Cornell University
Search algorithm External links:
How Google Search Works | Search Algorithms
Depth First Search Algorithm – YouTube
[PDF]The A* Search Algorithm – Duke University
Generalized linear model External links:
[PDF]Random generalized linear model: a highly accurate …
[PDF]The Poisson-Weibull Generalized Linear Model for …
[PDF]SAS Software to Fit the Generalized Linear Model – …
Oracle Data Mining External links:
Oracle Data Mining Concepts, 11g Release 1 (11.1)
Oracle Data Mining Concepts, 10g Release 2 (10.2)
Image de-noising External links:
PRESENTATION ON IMAGE DE-NOISING – YouTube
[PDF]IMAGE DE-NOISING TECHNIQUES: A REVIEW PAPER
Empirical risk minimization External links:
[PDF]Differentially Private Empirical Risk Minimization
[PDF]Private Empirical Risk Minimization, Revisited
[PDF]Empirical Risk Minimization and Optimization – …
Probably approximately correct learning External links:
CiteSeerX — Probably Approximately Correct Learning
[PDF]Probably Approximately Correct Learning – III
Support vector machine External links:
Support Vector Machine – Python Tutorial
Proximal Support Vector Machine Home Page
Lagrangian Support Vector Machine Home Page
Developmental robotics External links:
Developmental Robotics | The MIT Press
Developmental Robotics News – Home | Facebook
People – Developmental Robotics Lab
Probability theory External links:
[PDF]Probability Theory 1 Lecture Notes – …
www.math.cornell.edu/~web6720/MATH 6710 notes.pdf
Probability theory – ScienceDaily
Affective computing External links:
Affective Computing Flashcards | Quizlet
Affective Computing: The Power of Emotion Analytics – …
Affective Computing – Gartner IT Glossary
International Conference on Machine Learning External links:
International Conference on Machine Learning – Home | Facebook
International Conference on Machine Learning – 10times
Network simulation External links:
[PDF]Network Simulation Tool for Project 25: Inter-RF …
Network Simulation | Penn College
Network Simulation to test SNMP – Quora
Independent component analysis External links:
[PDF]INDEPENDENT COMPONENT ANALYSIS WITH …
What is Independent Component Analysis?
Reinforcement learning External links:
Advanced AI: Deep Reinforcement Learning in Python | Udemy
Operational definition External links:
Operational Definition – Template & Example
[PDF]Operational Definitions – KIPBS – KIPBS | Welcome
Decision tree External links:
[PDF]Decision Tree for Summary Rating Discussions
“The Good Wife” The Decision Tree (TV Episode 2013) – IMDb
ECML PKDD External links:
ECML PKDD – Home | Facebook
Artificial Intelligence External links:
Artificial Intelligence for B2B Sales | Collective[i]
RPA and Artificial Intelligence Summit 2017 – Official Site
Object recognition External links:
[1412.7755] Multiple Object Recognition with Visual Attention
Object Recognition Software
Object Recognition – MATLAB & Simulink – MathWorks
Vinod Khosla External links:
Vinod Khosla (@vkhosla) | Twitter
Principal component analysis External links:
11.1 – Principal Component Analysis (PCA) Procedure | …
[PDF]PRINCIPAL COMPONENT ANALYSIS – SAS Support
Principal Component Analysis | Quantdare
Logic programming External links:
Logic programming (eBook, 1991) [WorldCat.org]
Logic programming (Book, 1991) [WorldCat.org]
[PDF]Introduction to Logic Programming
www.eng.ucy.ac.cy/theocharides/Courses/ECE317/Logic Programming 1.pdf
Bias-variance dilemma External links:
[PDF]A Bias-Variance Dilemma in Joint Diagonalization …
Difference between bias-variance dilemma and overfitting
Bias-Variance Dilemma – YouTube
K-nearest neighbors algorithm External links:
Using the k-Nearest Neighbors Algorithm in R « Web Age …
Functional programming External links:
[PDF]Functional Programming in Java
Evolutionary algorithm External links:
Phd Thesis Evolutionary Algorithm
“Evolutionary Algorithm Sandbox: A Web-Based …
Evolutionary Algorithms | InTechOpen
Conference on Neural Information Processing Systems External links:
Conference on Neural Information Processing Systems
Temporal difference learning External links:
Temporal difference learning and TD-gammon (1995) – …
Time complexity External links:
differences between time complexity and space complexity?
The Role of Reading Time Complexity and Reading Speed …
Machine perception External links:
Machine Perception Research | ECE | Virginia Tech
Machine Perception – Research at Google
Anomaly detection External links:
Anodot | Automated anomaly detection system and real …
Practical Machine Learning: A New Look at Anomaly Detection
Data analysis External links:
LZ Retailytics – Must-Have Retail Data Analysis Platform
Data Analysis Examples – IDRE Stats
AnswerMiner – Data analysis made easy
Knowledge discovery External links:
ERIC – Data Mining and Knowledge Discovery., Annual …
[PDF]Knowledge Discovery for Characterizing Team …
Structured prediction External links:
[PDF]End-to-End Learning for Structured Prediction …
Automated theorem proving External links:
Automated Theorem Proving – ScienceDirect
Predictive analytics External links:
Customer Analytics & Predictive Analytics Tools for …
Predictive Analytics Software, Social Listening | NewBrand
Inventory Optimization for Retail | Predictive Analytics
Structural health monitoring External links:
Structural Health Monitoring | SAGE Publications Ltd
Structural health monitoring (eBook, 2006) [WorldCat.org]
Structural Health Monitoring. (eBook, 2010) [WorldCat.org]
Online advertising External links:
US Big Ads – Free Online Advertising, US Free Classified Ads
Denver Online Advertising Agency | Booyah
Hierarchical clustering External links:
ERIC – U-Statistic Hierarchical Clustering, …
14.4 – Agglomerative Hierarchical Clustering | STAT 505
Non-negative matrix factorization External links:
[1701.00016] Non-Negative Matrix Factorization Test …
CiteSeerX — Algorithms for Non-negative Matrix Factorization
[PDF]When Does Non-Negative Matrix Factorization Give a …
Cluster analysis External links:
Chapter 9: Cluster analysis Flashcards | Quizlet
ERIC – Multivariate Cluster Analysis., 1971-Feb
How to do a cluster analysis of data in Excel – Updated 2017
Neural network External links:
Neural Network Console
SUPPORT – Neural Network Console
Neural Network Libraries
Random variables External links:
[PPT]Discrete Random Variables and Probability …
Random variables (Book, 1975) [WorldCat.org]
Discrete and Continuous Random Variables
GNU Octave External links:
GNU Octave – Official Site
GNU Octave: Plot Annotations
GNU Octave – Plotting – univie.ac.at
Mathematical optimization External links:
Title: Mathematical optimization for packing problems – …
Mathematical optimization — NYU Scholars
Linear discriminant analysis External links:
Linear Discriminant Analysis versus Logistic Regression…
9.2.2 – Linear Discriminant Analysis | STAT 897D
Recurrent neural network External links:
How to build a Recurrent Neural Network in TensorFlow (1/7)
Particle Learning and Gated Recurrent Neural Network …
Optical character recognition External links:
Best Optical Character Recognition (OCR) Software – …
Statistical learning theory External links:
Syllabus for Statistical Learning Theory
SVM Support Vector Machine Statistical Learning Theory