What is involved in Data Science
Find out what the related areas are that Data Science 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 Data Science thinking-frame.
How far is your company on its Data Science journey?
Take this short survey to gauge your organization’s progress toward Data Science 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 Data Science related domains to cover and 169 essential critical questions to check off in that domain.
The following domains are covered:
Data Science, Explanatory model, Cluster analysis, Recurrent neural network, Applied science, PubMed Central, Software engineer, K-nearest neighbors algorithm, Support vector machine, Relevance vector machine, Machine Learning, General Assembly, Reinforcement learning, Decision tree learning, Data set, NYU Stern Center for Business and Human Rights, Business analyst, Graduate school, Online machine learning, Software Developer, Artificial neural network, International Conference on Machine Learning, Independent component analysis, American Statistical Association, Social science, Outline of machine learning, Random forest, Bias-variance dilemma, Feature engineering, Hierarchical clustering, The Data Incubator, K-means clustering, Probably approximately correct learning, Empirical risk minimization, Journal of Machine Learning Research, Graphical model, Conference on Neural Information Processing Systems, CURE data clustering algorithm, Statistical learning theory, Deep learning, Structured prediction, Semi-supervised learning, Dimensionality reduction, Predictive modelling, Linear regression, Empirical research, Data Science, Anomaly detection, Temporal difference learning, Feature learning, K-nearest neighbors classification, Hidden Markov model, Regression analysis, Grammar induction, Statistical classification, Information science, Conditional random field, Expectation–maximization algorithm, Principal component analysis, Local outlier factor, Multilayer perceptron, Big data, Pattern recognition, Canonical correlation analysis, Supervised learning, Computer science:
Data Science Critical Criteria:
Accommodate Data Science projects and customize techniques for implementing Data Science controls.
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Have the types of risks that may impact Data Science been identified and analyzed?
– Can we do Data Science without complex (expensive) analysis?
Explanatory model Critical Criteria:
Systematize Explanatory model projects and integrate design thinking in Explanatory model innovation.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Science processes?
– Risk factors: what are the characteristics of Data Science that make it risky?
– Meeting the challenge: are missed Data Science opportunities costing us money?
Cluster analysis Critical Criteria:
Prioritize Cluster analysis adoptions and devote time assessing Cluster analysis and its risk.
– What are our needs in relation to Data Science skills, labor, equipment, and markets?
– What are the long-term Data Science goals?
– How much does Data Science help?
Recurrent neural network Critical Criteria:
Read up on Recurrent neural network strategies and use obstacles to break out of ruts.
– What are our best practices for minimizing Data Science project risk, while demonstrating incremental value and quick wins throughout the Data Science project lifecycle?
– Do you monitor the effectiveness of your Data Science activities?
– Who needs to know about Data Science ?
Applied science Critical Criteria:
Categorize Applied science visions and define Applied science competency-based leadership.
– Do those selected for the Data Science team have a good general understanding of what Data Science is all about?
– Is the Data Science organization completing tasks effectively and efficiently?
– How do we go about Securing Data Science?
PubMed Central Critical Criteria:
See the value of PubMed Central governance and document what potential PubMed Central megatrends could make our business model obsolete.
– Can we add value to the current Data Science decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– What is the source of the strategies for Data Science strengthening and reform?
– How does the organization define, manage, and improve its Data Science processes?
Software engineer Critical Criteria:
Graph Software engineer results and reinforce and communicate particularly sensitive Software engineer decisions.
– DevOps isnt really a product. Its not something you can buy. DevOps is fundamentally about culture and about the quality of your application. And by quality I mean the specific software engineering term of quality, of different quality attributes. What matters to you?
– Consider your own Data Science project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– Can we answer questions like: Was the software process followed and software engineering standards been properly applied?
– Is open source software development faster, better, and cheaper than software engineering?
– How will we insure seamless interoperability of Data Science moving forward?
– Which individuals, teams or departments will be involved in Data Science?
– Better, and cheaper than software engineering?
K-nearest neighbors algorithm Critical Criteria:
Design K-nearest neighbors algorithm tasks and devise K-nearest neighbors algorithm key steps.
– Does Data Science analysis isolate the fundamental causes of problems?
– Have all basic functions of Data Science been defined?
Support vector machine Critical Criteria:
Be responsible for Support vector machine goals and proactively manage Support vector machine risks.
– What prevents me from making the changes I know will make me a more effective Data Science leader?
– Is there any existing Data Science governance structure?
– Are there Data Science Models?
Relevance vector machine Critical Criteria:
Grade Relevance vector machine visions and perfect Relevance vector machine conflict management.
– Do several people in different organizational units assist with the Data Science process?
Machine Learning Critical Criteria:
Participate in Machine Learning adoptions and define Machine Learning competency-based leadership.
– what is the best design framework for Data Science organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Will Data Science have an impact on current business continuity, disaster recovery processes and/or infrastructure?
General Assembly Critical Criteria:
Check General Assembly goals and interpret which customers can’t participate in General Assembly because they lack skills.
– Who are the people involved in developing and implementing Data Science?
– Why should we adopt a Data Science framework?
Reinforcement learning Critical Criteria:
Probe Reinforcement learning risks and ask what if.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data Science in a volatile global economy?
– What are the disruptive Data Science technologies that enable our organization to radically change our business processes?
– Are accountability and ownership for Data Science clearly defined?
Decision tree learning Critical Criteria:
Familiarize yourself with Decision tree learning strategies and prioritize challenges of Decision tree learning.
– Think about the people you identified for your Data Science 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?
– What knowledge, skills and characteristics mark a good Data Science project manager?
– How is the value delivered by Data Science being measured?
Data set Critical Criteria:
Conceptualize Data set leadership and assess what counts with Data set that we are not counting.
– For hosted solutions, are we permitted to download the entire data set in order to maintain local backups?
– How was it created; what algorithms, algorithm versions, ancillary and calibration data sets were used?
– Who will be responsible for deciding whether Data Science goes ahead or not after the initial investigations?
– Is data that is transcribed or copied checked for errors against the original data set?
– What needs to be in the plan related to the data capture for the various data sets?
– Is someone responsible for migrating data sets that are in old/outdated formats?
– Does the Data Science task fit the clients priorities?
– You get a data set. what do you do with it?
NYU Stern Center for Business and Human Rights Critical Criteria:
Conceptualize NYU Stern Center for Business and Human Rights leadership and check on ways to get started with NYU Stern Center for Business and Human Rights.
– To what extent does management recognize Data Science as a tool to increase the results?
– What are the record-keeping requirements of Data Science activities?
Business analyst Critical Criteria:
Troubleshoot Business analyst outcomes and budget for Business analyst challenges.
– In a project to restructure Data Science outcomes, which stakeholders would you involve?
– What are typical responsibilities of someone in the role of Business Analyst?
– What is the difference between a Business Architect and a Business Analyst?
– How important is Data Science to the user organizations mission?
– Do business analysts know the cost of feature addition or modification?
– What are internal and external Data Science relations?
Graduate school Critical Criteria:
Think about Graduate school tasks and cater for concise Graduate school education.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Science processes?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Science?
Online machine learning Critical Criteria:
Map Online machine learning planning and diversify disclosure of information – dealing with confidential Online machine learning information.
– Are we making progress? and are we making progress as Data Science leaders?
– How do we Identify specific Data Science investment and emerging trends?
– What will drive Data Science change?
Software Developer Critical Criteria:
Concentrate on Software Developer outcomes and visualize why should people listen to you regarding Software Developer.
– Pick an experienced Unix software developer, show him all the algorithms and ask him which one he likes the best?
– How do senior leaders actions reflect a commitment to the organizations Data Science values?
– What other jobs or tasks affect the performance of the steps in the Data Science process?
– How can we improve Data Science?
Artificial neural network Critical Criteria:
Judge Artificial neural network issues and cater for concise Artificial neural network education.
– How do you determine the key elements that affect Data Science workforce satisfaction? how are these elements determined for different workforce groups and segments?
International Conference on Machine Learning Critical Criteria:
Confer re International Conference on Machine Learning goals and remodel and develop an effective International Conference on Machine Learning strategy.
– What is our Data Science Strategy?
Independent component analysis Critical Criteria:
Focus on Independent component analysis tactics and oversee Independent component analysis requirements.
– How likely is the current Data Science plan to come in on schedule or on budget?
American Statistical Association Critical Criteria:
Communicate about American Statistical Association outcomes and correct American Statistical Association management by competencies.
– Does our organization need more Data Science education?
– How do we go about Comparing Data Science approaches/solutions?
– What are the usability implications of Data Science actions?
Social science Critical Criteria:
Paraphrase Social science tasks and develop and take control of the Social science initiative.
– How do your measurements capture actionable Data Science information for use in exceeding your customers expectations and securing your customers engagement?
– Where do ideas that reach policy makers and planners as proposals for Data Science strengthening and reform actually originate?
– How can you measure Data Science in a systematic way?
Outline of machine learning Critical Criteria:
Be clear about Outline of machine learning tasks and describe which business rules are needed as Outline of machine learning interface.
– What are the Key enablers to make this Data Science move?
Random forest Critical Criteria:
Jump start Random forest projects and do something to it.
– Are we Assessing Data Science and Risk?
Bias-variance dilemma Critical Criteria:
Wrangle Bias-variance dilemma issues and adjust implementation of Bias-variance dilemma.
Feature engineering Critical Criteria:
Rank Feature engineering engagements and change contexts.
– How can you negotiate Data Science successfully with a stubborn boss, an irate client, or a deceitful coworker?
Hierarchical clustering Critical Criteria:
Adapt Hierarchical clustering planning and look for lots of ideas.
– Think about the kind of project structure that would be appropriate for your Data Science project. should it be formal and complex, or can it be less formal and relatively simple?
The Data Incubator Critical Criteria:
Distinguish The Data Incubator quality and catalog what business benefits will The Data Incubator goals deliver if achieved.
– How will you measure your Data Science effectiveness?
– What are specific Data Science Rules to follow?
K-means clustering Critical Criteria:
Prioritize K-means clustering risks and know what your objective is.
– What is the total cost related to deploying Data Science, including any consulting or professional services?
– What potential environmental factors impact the Data Science effort?
Probably approximately correct learning Critical Criteria:
Unify Probably approximately correct learning governance and interpret which customers can’t participate in Probably approximately correct learning because they lack skills.
– What is the purpose of Data Science in relation to the mission?
– Can Management personnel recognize the monetary benefit of Data Science?
Empirical risk minimization Critical Criteria:
Mine Empirical risk minimization visions and attract Empirical risk minimization skills.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data Science?
– Why is Data Science important for you now?
– Is a Data Science Team Work effort in place?
Journal of Machine Learning Research Critical Criteria:
Troubleshoot Journal of Machine Learning Research adoptions and sort Journal of Machine Learning Research activities.
– What are your current levels and trends in key measures or indicators of Data Science 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 will be the consequences to the business (financial, reputation etc) if Data Science does not go ahead or fails to deliver the objectives?
– What are the barriers to increased Data Science production?
Graphical model Critical Criteria:
Experiment with Graphical model strategies and remodel and develop an effective Graphical model strategy.
– Is maximizing Data Science protection the same as minimizing Data Science loss?
– What are the Essentials of Internal Data Science Management?
Conference on Neural Information Processing Systems Critical Criteria:
Model after Conference on Neural Information Processing Systems outcomes and modify and define the unique characteristics of interactive Conference on Neural Information Processing Systems projects.
– Is Supporting Data Science documentation required?
– Who sets the Data Science standards?
CURE data clustering algorithm Critical Criteria:
Concentrate on CURE data clustering algorithm goals and shift your focus.
– Why is it important to have senior management support for a Data Science project?
Statistical learning theory Critical Criteria:
Give examples of Statistical learning theory decisions and handle a jump-start course to Statistical learning theory.
– How can skill-level changes improve Data Science?
Deep learning Critical Criteria:
Facilitate Deep learning risks and probe Deep learning strategic alliances.
– Does Data Science 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?
Structured prediction Critical Criteria:
Systematize Structured prediction quality and revise understanding of Structured prediction architectures.
– What role does communication play in the success or failure of a Data Science project?
– What are our Data Science Processes?
– What is Effective Data Science?
Semi-supervised learning Critical Criteria:
Ventilate your thoughts about Semi-supervised learning risks and report on developing an effective Semi-supervised learning strategy.
Dimensionality reduction Critical Criteria:
See the value of Dimensionality reduction outcomes and differentiate in coordinating Dimensionality reduction.
– Does Data Science analysis show the relationships among important Data Science factors?
Predictive modelling Critical Criteria:
Closely inspect Predictive modelling tactics and spearhead techniques for implementing Predictive modelling.
– How to Secure Data Science?
Linear regression Critical Criteria:
Understand Linear regression strategies and plan concise Linear regression education.
Empirical research Critical Criteria:
Air ideas re Empirical research strategies and budget the knowledge transfer for any interested in Empirical research.
– Does Data Science create potential expectations in other areas that need to be recognized and considered?
Data Science Critical Criteria:
Talk about Data Science management and budget for Data Science challenges.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Science services/products?
– Among the Data Science product and service cost to be estimated, which is considered hardest to estimate?
Anomaly detection Critical Criteria:
Drive Anomaly detection adoptions and arbitrate Anomaly detection techniques that enhance teamwork and productivity.
– What sources do you use to gather information for a Data Science study?
– What is our formula for success in Data Science ?
Temporal difference learning Critical Criteria:
Grasp Temporal difference learning governance and modify and define the unique characteristics of interactive Temporal difference learning projects.
Feature learning Critical Criteria:
Investigate Feature learning results and get going.
– What about Data Science Analysis of results?
K-nearest neighbors classification Critical Criteria:
Audit K-nearest neighbors classification risks and find answers.
– What management system can we use to leverage the Data Science experience, ideas, and concerns of the people closest to the work to be done?
– Are there recognized Data Science problems?
Hidden Markov model Critical Criteria:
Contribute to Hidden Markov model outcomes and assess and formulate effective operational and Hidden Markov model strategies.
Regression analysis Critical Criteria:
Start Regression analysis issues and observe effective Regression analysis.
– What are the key elements of your Data Science performance improvement system, including your evaluation, organizational learning, and innovation processes?
Grammar induction Critical Criteria:
Reorganize Grammar induction projects and get answers.
Statistical classification Critical Criteria:
Shape Statistical classification visions and mentor Statistical classification customer orientation.
Information science Critical Criteria:
Consult on Information science strategies and find answers.
– What tools do you use once you have decided on a Data Science strategy and more importantly how do you choose?
– How do we make it meaningful in connecting Data Science with what users do day-to-day?
Conditional random field Critical Criteria:
Consult on Conditional random field strategies and improve Conditional random field service perception.
Expectation–maximization algorithm Critical Criteria:
Substantiate Expectation–maximization algorithm strategies and diversify by understanding risks and leveraging Expectation–maximization algorithm.
Principal component analysis Critical Criteria:
Inquire about Principal component analysis strategies and display thorough understanding of the Principal component analysis process.
– Which customers cant participate in our Data Science domain because they lack skills, wealth, or convenient access to existing solutions?
– How do we measure improved Data Science service perception, and satisfaction?
Local outlier factor Critical Criteria:
Scan Local outlier factor adoptions and acquire concise Local outlier factor education.
Multilayer perceptron Critical Criteria:
Systematize Multilayer perceptron adoptions and assess and formulate effective operational and Multilayer perceptron strategies.
– For your Data Science project, identify and describe the business environment. is there more than one layer to the business environment?
Big data Critical Criteria:
Deliberate over Big data management and correct Big data management by competencies.
– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?
– Do we address the daunting challenge of Big Data: how to make an easy use of highly diverse data and provide knowledge?
– Is the software compatible with new database formats for raw, unstructured, and semi-structured big data?
– What are some strategies for capacity planning for big data processing and cloud computing?
– The real challenge: are you willing to get better value and more innovation for some loss of privacy?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– What are the ways in which cloud computing and big data can work together?
– Which other Oracle Business Intelligence products are used in your solution?
– How can the benefits of Big Data collection and applications be measured?
– Quantity: What data are required to satisfy the given value proposition?
– What is the contribution of subsets of the data to the problem solution?
– What analytical tools do you consider particularly important?
– Is recruitment of staff with strong data skills crucial?
– Even when we have a lot of data, do we understand it?
– What happens if/when no longer need cognitive input?
– How do you handle Big Data in Analytic Applications?
– How do I get to there from here?
– How to deal with too much data?
– Where is the ROI?
Pattern recognition Critical Criteria:
X-ray Pattern recognition governance and stake your claim.
Canonical correlation analysis Critical Criteria:
Communicate about Canonical correlation analysis quality and innovate what needs to be done with Canonical correlation analysis.
Supervised learning Critical Criteria:
Weigh in on Supervised learning failures and pioneer acquisition of Supervised learning systems.
– Have you identified your Data Science key performance indicators?
Computer science Critical Criteria:
Own Computer science leadership and assess and formulate effective operational and Computer science strategies.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data Science models, tools and techniques are necessary?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Science 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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Data Science External links:
Data Science Course | Udacity
DataScience.com | Enterprise Data Science Platform …
What is Data Science?
Explanatory model External links:
medanth – Explanatory Model
Older men’s explanatory model for osteoporosis. | …
Cluster analysis External links:
Lesson 14: Cluster Analysis | STAT 505
Lesson 14: Cluster Analysis – Pennsylvania State University
How to do a cluster analysis of data in Excel – Updated 2017
Recurrent neural network External links:
How to build a Recurrent Neural Network in TensorFlow (1/7)
Applied science External links:
Applied science. Technology (eBook, 2013) [WorldCat.org]
Applied Science – AbeBooks
Applied Science Reports – PSCIPUB
http://www.pscipub.com/Journals/Default.aspx?Title=Applied Science Reports
PubMed Central External links:
MEDLINE, PubMed, and PMC (PubMed Central): How are …
PubMed Tutorial – Getting the Articles – PubMed Central
PubMed Central | NIH Library
Software engineer External links:
Title Software Engineer Jobs, Employment | Indeed.com
Software Engineer Title Ladder – ChangeLog.ca
K-nearest neighbors algorithm External links:
Using the k-Nearest Neighbors Algorithm in R « Web Age …
Support vector machine External links:
Support Vector Machine – Python Tutorial
Proximal Support Vector Machine Home Page
Introduction to Support Vector Machines¶ – OpenCV
Relevance vector machine External links:
python – Relevance Vector Machine – Stack Overflow
Machine Learning External links:
The Machine Learning Conference
Microsoft Azure Machine Learning Studio
DataRobot – Automated Machine Learning for Predictive …
General Assembly External links:
General Assembly – Official Site
Indiana General Assembly – Official Site
Find Your Legislator – PA General Assembly
Reinforcement learning External links:
Reinforcement Learning | The MIT Press
Reinforcement Learning Overview – DZone AI
Model-based Reinforcement Learning with Neural …
Decision tree learning External links:
Decision Tree Learning | Statistics | Applied Mathematics
Data set External links:
OpenFEMA Dataset: OpenFEMA Data Sets – V1 | FEMA.gov
Limited Data Set | HHS.gov
NYU Stern Center for Business and Human Rights External links:
Team – NYU Stern Center for Business and Human Rights
About – NYU Stern Center for Business and Human Rights
Business analyst External links:
Project Management and Business Analyst Conferences
Business Analyst – jobs.kumc.edu
Title Business Analyst Jobs, Employment | Indeed.com
Graduate school External links:
Harvard Graduate School of Education – Official Site
Samuel Curtis Johnson Graduate School of Management …
Jesse H. Jones Graduate School of Business – Official Site
Online machine learning External links:
What is online machine learning? | E-learning
[PDF]Online Machine Learning Algorithms For Currency …
Software Developer External links:
Title Software Developer Vs Engineer windows 8 1 enterprise activation keys free Download AutoCAD Full Version Crack Microsoft Word 2013 Free Install
Become a Software Developer In 12 Weeks | Coder Camps
[PDF]Job Description for Software Developer. Title: …
Artificial neural network External links:
[PDF]J3.4 USE OF AN ARTIFICIAL NEURAL NETWORK TO …
Artificial neural network – ScienceDaily
International Conference on Machine Learning External links:
International Conference on Machine Learning – 10times
International Conference on Machine Learning – 10times
Independent component analysis External links:
Group Independent Component Analysis (gICA) and …
[PDF]An Independent Component Analysis Mixture Model …
What is Independent Component Analysis?
American Statistical Association External links:
[PDF]American Statistical Association Style Guide
American Statistical Association | HuffPost
[PDF]American Statistical Association is collaborating …
Social science External links:
Social Science Research Lab at WSU – Wichita State University
Home | Institute for Social Science Research
Irrational Game | A fun Social Science game by Dan Ariely
Random forest External links:
Unsupervised Learning With Random Forest Predictors
GCD.5 – Random Forest | STAT 897D
python – Random Forest with GridSearchCV – Error on …
Bias-variance dilemma External links:
[PDF]The Bias-Variance Dilemma of the Monte Carlo Method
Difference between bias-variance dilemma and overfitting
Bias-Variance Dilemma – YouTube
Hierarchical clustering External links:
[PDF]Bayesian Hierarchical Clustering – Duke University
Hierarchical Clustering – Saed Sayad
14.4 – Agglomerative Hierarchical Clustering | STAT 505
The Data Incubator External links:
The Data Incubator – Official Site
The Data Incubator Team | The Data Incubator
The Data Incubator – Home | Facebook
K-means clustering External links:
[PPT]K-means Clustering – Computer Science & E
3-3 K-means Clustering – Mirlab
http://mirlab.org/jang/books/dcpr/dcKMeans.asp?title=3-3 K-means Clustering
[1512.07548] k-Means Clustering Is Matrix Factorization
Probably approximately correct learning External links:
[PDF]Probably Approximately Correct Learning – III
CiteSeerX — Probably Approximately Correct Learning
Empirical risk minimization External links:
10: Empirical Risk Minimization – Cornell University
[PDF]Differentially Private Empirical Risk Minimization
Journal of Machine Learning Research External links:
Journal of machine learning research | ROAD
Publication: The Journal of Machine Learning Research
Journal of Machine Learning Research Homepage
Conference on Neural Information Processing Systems External links:
Conference on Neural Information Processing Systems …
CURE data clustering algorithm External links:
CURE data clustering algorithm – Revolvy
https://update.revolvy.com/topic/CURE data clustering algorithm
Statistical learning theory External links:
[PDF]Statistical Learning Theory: A Tutorial – Princeton …
Syllabus for Statistical Learning Theory
Deep learning External links:
Focal Systems – Deep Learning and Computer Vision …
MIT 6.S094: Deep Learning for Self-Driving Cars
Structured prediction External links:
The Imitation Learning View of Structured Prediction – …
[1608.00612] Structured prediction models for RNN …
[PDF]End-to-End Learning for Structured Prediction …
Semi-supervised learning External links:
Semi-Supervised Learning Software
[PDF]Semi-Supervised Learning for Natural Language – …
[PDF]Title: A Semi-supervised learning approach to …
Linear regression External links:
Testing the assumptions of linear regression
Introduction to linear regression analysis – Duke University
Introduction to Linear Regression
Empirical research External links:
[PDF]Introduction to Empirical Research
Empirical Research Examples – LibGuides at CSU, Chico
Data Science External links:
Data Science Course | Udacity
Earn your Data Science Degree Online
What is Data Science?
Anomaly detection External links:
Anodot | Automated anomaly detection system and real …
Temporal difference learning External links:
Neural Network and Temporal Difference Learning
Feature learning External links:
Unsupervised Feature Learning and Deep Learning Tutorial
[PDF]PointNet++: Deep Hierarchical Feature Learning on …
Context Encoders: Feature Learning by Inpainting
Hidden Markov model External links:
Hidden Markov model regression – conservancy.umn.edu
[PPT]Hidden Markov Model Tutorial – Fei Hu – Welcome to …
Hidden Markov Model – Everything2.com
Regression analysis External links:
Regression Analysis Flashcards | Quizlet
Regression Analysis Made Easy with Excel – WorldatWork
How to Read Regression Analysis Summary in Excel: 4 …
Grammar induction External links:
Bayesian Grammar Induction for Language Modeling
CiteSeerX — Phylogenetic Grammar Induction
[PDF]Unsupervised Grammar Induction of Clinical Report …
Statistical classification External links:
[PDF]International Statistical Classification of Diseases …
What Is Statistical Classification? (with pictures) – wiseGEEK
Information science External links:
Computer & Information Science & Engineering …
Research Information Science & Computing
UHM Library and Information Science Program – hawaii.edu
Conditional random field External links:
[PDF]Tutorial on Conditional Random Fields for Sequence …
CRF – Conditional Random Fields | AcronymAttic
[PDF]Conditional Random Fields
Principal component analysis External links:
[PDF]203-30: Principal Component Analysis versus …
11.1 – Principal Component Analysis (PCA) Procedure | …
Local outlier factor External links:
Where can I get C code for Local Outlier Factor? – quora.com
Multilayer perceptron External links:
Patent US20160071003 – Multilayer Perceptron for Dual …
Big data External links:
ZestFinance.com: Machine Learning & Big Data …
Event Hubs – Cloud big data solutions | Microsoft Azure
Take 5 Media Group – Build an audience using big data
Pattern recognition External links:
Pattern Recognition – IMDb
Pattern recognition – Encyclopedia of Mathematics
Pattern recognition (Computer file, 2006) [WorldCat.org]
Canonical correlation analysis External links:
Canonical Correlation Analysis | R Data Analysis …
[PDF]Chapter 8: Canonical Correlation Analysis and …
The Redundancy Index in Canonical Correlation Analysis.
Supervised learning External links:
Supervised Learning in R: Regression – DataCamp
Computer science External links:
Purdue University – Department of Computer Science
College of Engineering and Computer Science | Wright …
TEALS – Computer Science in Every High School