Machine learning and data mining
Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression)
Clustering BIRCH CURE Hierarchical k-means Expectation–maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction Graphical models  Bayes net Conditional random field Hidden Markov
Anomaly detection k-NN Local outlier factor
Artificial neural network Autoencoder Deep learning DeepDream Multilayer perceptron RNN  LSTM GRU Restricted Boltzmann machine GAN SOM Convolutional neural network  U-Net
Reinforcement learning Q-learning SARSA Temporal difference (TD)
Theory[hide] Bias–variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues NeurIPS ICML ML JMLR ArXiv:cs.LG
Glossary of artificial intelligence Glossary of artificial intelligence
Related articles List of datasets for machine-learning research Outline of machine learning

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data.

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.

Contents

• History
• Relationship to statistics

History

The term "data science" has appeared in various contexts over the past thirty years but did not become an established term until recently. In an early usage, it was used as a substitute for computer science by Peter Naur in 1960. Naur later introduced the term "datalogy". In 1974, Naur published Concise Survey of Computer Methods, which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications.

The modern definition of "data science" was first sketched during the second Japanese-French statistics symposium organized at the University of Montpellier II (France) in 1992. The attendees acknowledged the emergence of a new discipline with a specific focus on data from various origins, dimensions, types and structures. They shaped the contour of this new science based on established concepts and principles of statistics and data analysis with the extensive use of the increasing power of computer tools.

In 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"), after the term was introduced in a roundtable discussion by Chikio Hayashi.

In November 1997, C.F. Jeff Wu gave the inaugural lecture entitled "Statistics = Data Science?" for his appointment to the H. C. Carver Professorship at the University of Michigan. In this lecture, he characterized statistical work as a trilogy of data collection, data modeling and analysis, and decision making. In his conclusion, he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists. Later, he presented his lecture entitled "Statistics = Data Science?" as the first of his 1998 P.C. Mahalanobis Memorial Lectures. These lectures honor Prasanta Chandra Mahalanobis, an Indian scientist and statistician and founder of the Indian Statistical Institute.

In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique. In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory.

In April 2002, the International Council for Science (ICSU): Committee on Data for Science and Technology (CODATA) started the Data Science Journal, a publication focused on issues such as the description of data systems, their publication on the internet, applications and legal issues. Shortly thereafter, in January 2003, Columbia University began publishing The Journal of Data Science, which provided a platform for all data workers to present their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published "Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century" defining data scientists as "the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection" whose primary activity is to "conduct creative inquiry and analysis."

Around 2007, Turing award winner Jim Gray envisioned "data-driven science" as a "fourth paradigm" of science that uses the computational analysis of large data as primary scientific method and "to have a world in which all of the science literature is online, all of the science data is online, and they interoperate with each other."

In the 2012 Harvard Business Review article "Data Scientist: The Sexiest Job of the 21st Century", DJ Patil claims to have coined this term in 2008 with Jeff Hammerbacher to define their jobs at LinkedIn and Facebook, respectively. He asserts that a data scientist is "a new breed", and that a "shortage of data scientists is becoming a serious constraint in some sectors", but describes a much more business-oriented role.

In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched. In 2013, the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg, establishing the European Association for Data Science (EuADS). The first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, General Assembly launched student-paid bootcamp and The Data Incubator launched a competitive free data science fellowship. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK.

Relationship to statistics

In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, "I think data-scientist is a sexed up term for a statistician....Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician."

On the other hand, responses to criticism are as numerous. In a 2014 Wall Street Journal article, Irving Wladawsky-Berger compares the data science enthusiasm with the dawn of computer science. He argues data science, like any other interdisciplinary field, employs methodologies and practices from across the academia and industry, but then it will morph them into a new discipline. He brings to attention the sharp criticisms of computer science, now a well respected academic discipline, had to once face. Likewise, NYU Stern's Vasant Dhar, as do many other academic proponents of data science, argues more specifically in December 2013 that data science is different from the existing practice of data analysis across all disciplines, which focuses only on explaining data sets. Data science seeks actionable and consistent pattern for predictive uses. This practical engineering goal takes data science beyond traditional analytics. Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models.

In an effort similar to Dhar's, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms. First, for Donoho, data science does not equate to big data, in that the size of the data set is not a criterion to distinguish data science and statistics. Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines. Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. As a statistician, Donoho, following many in his field, champions the broadening of learning scope in the form of data science, like John Chambers who urges statisticians to adopt an inclusive concept of learning from data. Together, these statisticians envision an increasingly inclusive applied field that grows out of traditional statistics and beyond.

For the future of data science, Donoho projects an ever-growing environment for open science where data sets used for academic publications are accessible to all researchers. US National Institute of Health has already announced plans to enhance reproducibility and transparency of research data. Other big journals are likewise following suit. This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms. As Donoho concludes, "the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available."

Algorithms:

• Network Routing: Algorithms, Protocols, and Architectures
• Algorithm-Aided Building Information Modeling: Connecting Algorithm-Aided Design and Object-Oriented Design
• The Nature of Code
• Concurrent Programming: Algorithms, Principles, and Foundations
• An Introduction to Distributed Algorithms
• Planning Algorithms
• Algorithms in a Nutshell
• Algorithms Unplugged
• Guide to Competitive Programming: Learning and Improving Algorithms Through Contests
• Algorithms of the Intelligent Web
• Introduction to Genetic Algorithms
• Algorithmics: The Spirit of Computing
• Code: The Hidden Language of Computer Hardware and Software
• Beginning Algorithms
• Algorithms and Theory of Computation Handbook
• Code Complete
• Algorithms for Data Science
• Algorithms For Dummies
• Structure and Interpretation of Computer Programs
• Think Like a Programmer – An Introduction to Creative Problem Solving
• grokking algorithms
• Thinking Recursively
• Essential Algorithms: A Practical Approach to Computer Algorithms
• How to Think About Algorithms
• The Algorithm Design Manual
• Fast Fourier Transform: Algorithms and Applications
• The art of computer programming - Volume 2, Seminumerical algorithms
• Introduction to Algorithms
• Learning Algorithms Through Programming and Puzzle Solving
• A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills
• Algorithms on Strings, Trees, and Sequences
• Algorithms for Sensor Systems: 10th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, ALGOSENSORS 2014 Wroclaw, Poland, September 12, 2014 Revised Selected Papers
• Problem Solving with Algorithms and Data Structures
• The Master Algorithm
• Python Algorithms: Mastering Basic Algorithms in the Python Language
• Clever Algorithms: Nature-Inspired Programming Recipes
• Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
• An Active Introduction to Discrete Mathematics and Algorithms
• Design and Analysis of Approximation Algorithms
• Algorithms Illuminated
• Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
• Algorithms Unlocked
• Algorithms to Live By - The Computer Science of Human Decisions
• Algorithms, Flowcharts & Program Design
• Introduction to Design and Analysis of Algorithms
• Practical Algorithms in Pediatric Hematology and Oncology
• Mathematical and Algorithmic Foundations of the Internet
• Problems on Algorithms
• Techniques of the Average Case Analysis of Algorithms
• Analysis of Algorithms: An Active Learning Approach
• The Power of Algorithms
• Automate This: How Algorithms Took Over Our Markets, Our Jobs, and the World
• Principles of Algorithmic Problem Solving
• Purely Functional Data Structures
• Algorithms For Interviews
• Hacker's Delight
• Algorithms
• Computer Algorithms
• Evolutionary Algorithms in Engineering and Computer Science
• How to Solve it By Computer
• Selected papers on analysis of algorithms
• Algorithms and Data Structures: The Basic Toolbox
• Algorithms in C
• Mastering Algorithms with C
• Problem Solving with Algorithms and Data Structures
• Introduction To Algorithms: A Creative Approach
• Algorithm Design

Natural Language Processing:

• Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow
• Handbook of Natural Language Processing and Machine Translation
• Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition
• Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
• Python Natural Language Processing
• Deep Learning in Natural Language Processing
• Natural Language Processing and Computational Linguistics
• Cognitive Approach to Natural Language Processing
• Multilingual Natural Language Processing Applications
• Mastering Natural Language Processing with Python
• Handbook of Natural Language Processing
• Natural Language Processing with TensorFlow
• Natural Language Processing of Semitic Languages
• Deep Learning for Natural Language Processing: Creating Neural Networks with Python
• Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
• Natural Language Processing in Action
• Foundations of Statistical Natural Language Processing
• Semantic Approaches in Natural Language Processing
• Aspects of Natural Language Processing
• Building Chatbots with Python: Using Natural Language Processing and Machine Learning
• Natural Language Processing with Python
• Ontology and the Lexicon: A Natural Language Processing Perspective
• Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python
• Natural Language Processing and Cognitive Science
• Natural Language Processing and Text Mining
• Challenges in natural language processing
• Bayesian Analysis in Natural Language Processing
• The Handbook of Computational Linguistics and Natural Language Processing
• Semantics-Oriented Natural Language Processing
• Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
• Natural Language Processing with Java
• Text Resources and Lexical Knowledge: Selected Papers from the 9th Conference on Natural Language Processing, KONVENS 2008
• Natural Language Processing for Online Applications
• Natural Language Processing Through Different Mathematical and Statistical Tools
• Python for Programmers
• Text Mining with R: A Tidy Approach
• Taming Text: How to Find, Organize, and Manipulate It
• Introduction to Computational Linguistics
• Natural Language Annotation for Machine Learning
• Building Applied Natural Language Generation Systems
• Deep Learning for Natural Language Processing: Develop Deep Learning Models for Natural Language in Python

Machine Learning:

• Machine Learning for Developers
• Machine Learning for Hackers
• Python Machine Learning Cookbook
• Python Machine Learning
• Real-World Machine Learning
• Python: Deeper Insights into Machine Learning
• Machine Learning for the Web
• Practical Machine Learning
• Large Scale Machine Learning with Spark
• Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
• Mastering Machine Learning with Python in Six Steps
• Practical Machine Learning with H2O
• Thoughtful Machine Learning with Python: A Test-Driven Approach
• Python Machine Learning Case Studies
• Machine Learning Hands-On for Developers and Technical Professionals
• Python for Probability, Statistics, and Machine Learning
• scikit-learn Cookbook
• Machine Learning Projects for .NET Developers
• Machine Learning in Action
• Building Machine Learning Systems with Python
• Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
• Neural Network Programming with Java
• Machine Learning in Java
• Machine Learning with Spark
• Microsoft Azure Machine Learning
• Large Scale Machine Learning with Python
• What You Need to Know about Machine Learning
• Learning NumPy Array: Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
• Building Intelligent Systems: A Guide to Machine Learning Engineering
• A Course in Machine Learning
• Advanced Machine Learning with Python
• Machine Learning Using C# Succinctly
• Designing Machine Learning Systems with Python
• Using Python to Develop Analytics, Control and Machine Learning Products
• Neural Networks Using C# Succinctly
• What you need to know about R: Kick-start your journey with R
• Learning scikit-learn: Machine Learning in Python
• Introduction to Machine Learning with Python: A Guide for Data Scientists
• Python Machine Learning Blueprints
• Foundations of Machine Learning
• Pattern Recognition and Machine Learning
• The Hundred-Page Machine Learning Book
• Introduction to Machine Learning
• Machine Learning For Dummies
• Mathematics for Machine Learning

Deep Learning:

• Deep Learning: A Practitioner's Approach
• Deep Learning
• Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras
• Deep Learning with Python: A Hands-on Introduction
• Deep Learning with Python
• Deep Learning with Theano
• Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
• Grokking Deep Learning
• Introduction to Deep Learning Business Applications for Developers
• Java Deep Learning Essentials
• Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
• Python: Deeper Insights into Machine Learning
• Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis
• R Deep Learning Essentials
• A Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, Scalable Computing
• Advanced Deep Learning with Keras
• Deep Learning for Natural Language Processing: Creating Neural Networks with Python
• MATLAB Deep Learning
• Reinforcement Learning: An Introduction
• Learning Generative Adversarial Networks
• Deep Learning for Computer Vision
• Neural Networks and Deep Learning
• Learning at the Speed of Light: Deep Learning and Accelerated Online Graduate Courses
• The Deep Learning Revolution

Artificial Intelligence:

• AI Techniques for Game Programming
• Artificial Intelligence: A Systems Approach
• Artificial Intelligence
• Artificial Intelligence: Mirrors for the Mind
• Artificial Intelligence Illuminated
• Artificial Intelligence in the 21st Century: a living introduction
• Biologically Inspired Artificial Intelligence for Computer Games
• Fundamentals of the New Artificial Intelligence
• Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases
• Practical Python AI Projects: Mathematical Models of Optimization Problems with Google OR-Tools
• Artificial Intelligence with Python
• Artificial Intelligence for Big Data
• Life 3.0: Being Human in the Age of Artificial Intelligence
• Artificial Intelligence: A Guide to Intelligent Systems
• Artificial Intelligence By Example
• Elements of Artificial Neural Networks
• Artificial Intelligence for Games
• Artificial Intelligence and Expert Systems for Engineers
• Artificial Intelligence and Machine Learning Fundamentals
• Prediction Machines: The Simple Economics of Artificial Intelligence
• The 4th Industrial Revolution: Responding to the Impact of Artificial Intelligence on Business
• Artificial Intelligence For Dummies

Data Warehousing:

• Building and Managing the Meta Data Repository
• Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
• Building a Data Warehouse: With Examples in SQL Server
• Azure SQL Data Warehouse
• Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources
• Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics
• Data Warehouse Design: Modern Principles and Methodologies
• Building and Maintaining a Data Warehouse
• Common Warehouse Metamodel
• Data Warehouse Requirements Engineering
• Data Warehouse Systems: Design and Implementation
• Data Warehouses and OLAP: Concepts, Architectures and Solutions
• Data Warehousing in the Age of Big Data
• Data Warehousing
• Data Warehousing Fundamentals: A Comprehensive Guide for IT Professionals
• Developing a Data Warehouse for the Healthcare Enterprise: Lessons from the Trenches
• Development of a Central Data Warehouse for Statewide ITS and Transportation Data in Florida
• Data Warehousing Tutorial
• Data Warehousing For Dummies
• Implementing a SQL Data Warehouse
• Mastering Data Warehouse Design: Relational and Dimensional Techniques
• Practical Hive: A Guide to Hadoop's Data Warehouse System
• The Data Warehouse Lifecycle Toolkit
• The Data Warehouse Toolkit
• The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science
• Web Data Management: A Warehouse Approach
• Building the Data Warehouse
• A Manager's Guide to Data Warehousing
• Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing
• Creating a Data-Driven Organization: Practical Advice from the Trenches
• Discovering Knowledge in Data: An Introduction to Data Mining
• The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence
• Transactions on Large-Scale Data- and Knowledge- Centered Systems VIII: Special Issue on Advances in DataWarehousing and Knowledge Discovery

Data Mining:

• Data Mining for Business Analytics
• R: Mining Spatial, Text, Web, and Social Media Data
• Mining the Social Web
• Mastering Blockchain
• Frontiers in Massive Data Analysis
• Data Mining: Concepts and Techniques
• Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
• Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
• Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
• Data Mining and Analysis: Fundamental Concepts and Algorithms
• Advances in Data Mining: Applications in Medicine, Web Mining, Marketing, Image and Signal Mining
• Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
• Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities
• Data Warehousing and Data Mining
• Agents and Data Mining Interaction
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• Data Mining: Practical Machine Learning Tools and Techniques
• Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
• Statistical and Machine-Learning Data Mining
• Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining
• Mining Massive Data Sets for Security: Advances in Data Mining, Search, Social Networks and Text Mining, and Their Applications to Security
• Business Intelligence and Data Mining
• Discovering Knowledge in Data: An Introduction to Data Mining
• Data Preprocessing in Data Mining
• Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
• Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications
• Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data
• Data Mining for Managers
• Predictive Analytics, Data Mining and Big Data
• Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
• Data Mining Techniques
• Transparent Data Mining for Big and Small Data
• Data Preparation for Data Mining Using SAS
• Modeling and Data Mining in Blogosphere
• Data Mining with R: learning by case studies
• Mathematical Analysis for Machine Learning and Data Mining
• Post, Mine, Repeat: Social Media Data Mining Becomes Ordinary
• Intelligent Data Warehousing: From Data Preparation to Data Mining
• Data Mining and Big Data
• Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
• Mining of Massive Datasets
• Think Bayes: Bayesian Statistics Made Simple
• Data Jujitsu
• Data Mining: A Heuristic Approach
• Data Mining Methods and Models
• Dark Web: Exploring and Data Mining the Dark Side of the Web
• Data Mining for Social Network Data
• Granular-Relational Data Mining
• Quantum Machine Learning: What Quantum Computing Means to Data Mining

Business Intelligence:

• Business Intelligence: Data Mining and Optimization for Decision Making
• Business Intelligence and Analytics
• Business Intelligence For Dummies
• Business Intelligence Success Factors
• Business Intelligence: A Managerial Perspective on Analytics
• Business Intelligence: The Savvy Manager's Guide
• Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
• Decision Trees for Business Intelligence and Data Mining
• Successful Business Intelligence: Secrets to Making BI a Killer App
• Financial Business Intelligence
• Practical Tableau
• Quick Emotional Intelligence Activities For Busy Managers
• The AI Advantage: How to Put the Artificial Intelligence Revolution to Work
• Early Warning: Using Competitive Intelligence to Anticipate Market Shifts, Control Risk, and Create Powerful Strategies
• Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
• Delivering Business Intelligence with Microsoft SQL Server 2008
• The Manager's Guide to Competitive Intelligence
• Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence

Mathematics for Data Science:

• Probability and Measure
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• Mathematics for Machine Learning
• Advanced Calculus
• OpenIntro Statistics
• Linear Algebra and Its Applications
• Linear Algebra For Dummies
• 1,001 Calculus Practice Problems For Dummies
• Introduction to Linear Algebra
• An Introduction to Statistical Learning: with Applications in R
• Calculus Made Easy
• Mathematical Problems in Data Science
• Calculus Demystified
• Elementary Linear Algebra
• Understanding Probability: Chance Rules in Everyday Life
• A First Course in Probability
• Fundamentals of Applied Probability and Random Processes
• naked statistics: Stripping the Dread from the Data
• Elementary Calculus. An Infinitesimal Approach
• Probability, Random Variables And Stochastic Processes

Data Analytics:

• Big Data: How Data Analytics Is Transforming the World
• Data analytics 2016: The Fifth International Conference on Data Analytics
• Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples
• Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
• Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports
• Analytics in a Big Data World
• Applied Microsoft Power BI: Bring your data to life!
• Applied Predictive Analytics
• Big Data Analytics Beyond Hadoop
• Big Data Analytics with R and Hadoop
• Big Data Analytics with Spark
• Big Data Analytics: Turning Big Data Into Big Money
• Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results
• Big Data, Mining, and Analytics: Components ofStrategic Decision Making
• Big Data Analytics with Java
• Big-Data Analytics for Cloud, IoT and Cognitive Computing
• Building a Digital Analytics Organization: Create Value by Integrating Analytical Processes, Technology, and People into Business Operations
• Data Analytics Made Accessible
• Data Analytics: Models and Algorithms for Intelligent Data Analysis
• Data Analytics and Linux Operating System 2 Bundle Manuscript
• Data Driven: How Performance Analytics Delivers Extraordinary Sales Results
• Data Just Right
• Data Science – Analytics and Applications
• Data Science and Analytics with Python
• Data Strategy: How to profit from a world of big data, analytics and the internet of things
• Derivatives Analytics with Python
• Digital Analytics: Data Driven Decision Making in Digital World
• Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World
• Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
• Fraud and Fraud Detection: A Data Analytics Approach
• From Big Data to Big Profits: Success with Data and Analytics
• Getting Started with Data Science: Making Sense of Data with Analytics
• Internet of Things and Big Data Analytics Toward Next-Generation Intelligence
• Lean Analytics: Use Data to Build a Better Startup Faster
• Learning Elastic Stack 6.0: A beginner's guide to distributed search, analytics, and visualization using Elasticsearch, Logstash, and Kibana
• Missing Data and Small-Area Estimation: Modern Analytical Equipment for the Survey Statistician
• Modern Big Data Processing with Hadoop
• Practical Text Analytics: Maximizing the Value of Text Data
• Practical Web Analytics for User Experience
• Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
• Pro Hadoop Data Analytics: Designing and Building Big Data Systems using the Hadoop Ecosystem
• Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language
• Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization
• Real-time Analytics with Storm and Cassandra
• SAS Essentials: Mastering SAS for Data Analytics
• Scalable Big Data Architecture: A Practitioner's Guide to Choosing Relevant Big Data Architecture
• Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery
• Taming the Big Data Tidal Wave
• The Analytics Revolution: How to Improve Your Business By Making Analytics Operational In The Big Data Era
• The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality
• Beginners' Guide to Analytics
• Introduction to Data Analysis Handbook
• Data Science: Theories, Models, Algorithms, and Analytics

Data Visualization:

• Beautiful Visualization: Looking at Data through the Eyes of Experts
• Data Visualization and Exploration with R
• Visual Data Mining: Techniques and Tools for Data Visualization and Mining
• Data Visualization: Representing Information on Modern Web
• Visual Six Sigma: Making Data Analysis Lean
• The Visual Imperative: Creating a Visual Culture of Data Discovery
• Expanding the Frontiers of Visual Analytics and Visualization
• Python Data Visualization Cookbook
• data visualization with python and javascript: Crafting a Data-visualisation Toolchain for the Web
• Practical Data Science Cookbook
• The Handbook of Statistics
• Data Visualization with Flash Builder: Designing RIA and AIR Applications with Remote Data Sources
• Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems
• D3 Tips and Tricks: Interactive Data Visualization in a Web Browser
• Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications
• Big Data and Visual Analytics
• Mastering Data Analysis with R: Gain clear insights into your data and solve real-world data science problems with R – from data munging to modeling and visualization
• Google Visualization API Essentials
• Mathematics and Visualization
• Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist
• Data Science with Julia
• Data Visualization: A Guide to Visual Storytelling for Libraries
• Data Visualization with D3.js Cookbook
• Mathematica Data Visualization
• Learning QlikView Data Visualization
• Bayes' Theorem: A Visual Introduction for Beginners
• Visual Research: A Concise Introduction to Thinking Visually
• Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
• Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations
• Effective Data Visualization: The Right Chart for the Right Data
• The Functional Art: An introduction to information graphics and visualization
• Help Your Kids with Computer Science

16 Best IoT Books For Beginners:

• Designing the Internet of Things
• Industry 4.0: Th e Industrial Internet of Things
• The Internet of Things: Industrie 4.0 Unleashed
• Architecting the Internet of Things
• Internet of Things: Principles and Paradigms
• The Internet of Things: Do-It-Yourself at Home Projects for Arduino, Raspberry Pi and BeagleBone Black
• Internet of Things (IoT) in 5 days
• From Internet of Things to Smart Cities: Enabling Technologies
• Value Creation and the Internet of Things
• Mastering Internet of Things
• Learning Internet of Things
• The Internet of Things: Applications to the Smart Grid and Building Automation
• Internet of Things for Architects
• Smarter Decisions – The Intersection of Internet of Things and Decision Science
• Internet of Things From Hype to Reality
• Internet of Things: Legal Perspectives

Databases:

• A First Course in Database Systems
• Advances in Databases: Concepts, Systems and Applications
• Beginning Databases with PostgreSQL: From Novice to Professional
• Database Design for Mere Mortals
• Database Design: Know It All
• Database Management System Tutorial
• Database System Concepts
• Database Systems: Design, Implementation, and Management
• Databases Demystified
• Databases for Small Business
• Databases Theory and Applications
• Databases: A Beginner's Guide
• Database Solutions: A step-by-step guide to building databases
• Distributed Database Management Systems: A Practical Approach
• Essential SQLAlchemy
• Expert Oracle Database Architecture
• Foundations of Databases
• Fundamentals of Database Systems
• Hands-On Database: An Introduction to Database Design and Development
• Modern Database Management
• New Trends in Databases and Information Systems
• Database Management Systems
• Refactoring Databases: Evolutionary Database Design
• Seven Databases in Seven Weeks
• SQL For Dummies
• The Database Hacker's Handbook: Defending Database Servers
• The Manga Guide to Databases
• Web, Web-Services, and Database Systems
• Graph Databases
• Learning Azure DocumentDB
• Learning Neo4j
• Learning PostgreSQL
• Mastering RethinkDB
• Microsoft SQL Azure: Enterprise Application Development
• Microsoft SQL Server 2012 Integration Services: An Expert Cookbook
• MongoDB Basics
• MySQL for Python
• Oracle Database 11gR2 Performance Tuning Cookbook
• PostgreSQL 9 Administration Cookbook
• PostgreSQL 9 High Availability Cookbook
• PostgreSQL Development Essentials
• PostgreSQL Server Programming
• Pro SQL Server Internals
• SQL Server 2014 Development Essentials
• SQL Server 2016 Reporting Services Cookbook
• The Definitive Guide to MongoDB
• Creating your MySQL Database: Practical Design Tips and Techniques
• Database Design and Relational Theory
• Joe Celko's Complete Guide to NoSQL
• Microsoft SQL Server 2012 TSQL Fundamentals
• The Art of SQL
• Data Modeling Essentials
• Database in Depth: The Relational Model for Practitioners

Cloud Computing:

• Cloud Computing For Dummies
• Cloud Computing: Principles and Paradigms
• Secure Cloud Computing
• Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS)
• A Complete Guide to Cloud Computing
• Cloud Computing Bible
• Handbook of Cloud Computing
• The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice
• Cloud Computing: Technologies and Strategies of the Ubiquitous Data Center
• Cloud Computing: Implementation, Management, and Security
• Big-Data Analytics and Cloud Computing: Theory, Algorithms and Applications
• Cloud Computing and Computer Clouds
• Cloud Computing and Services Sciences
• Cloud Computing for Geospatial Big Data Analytics
• Cloud Computing Patterns: Fundamentals to Design, Build, and Manage Cloud Applications
• Data Security in Cloud Computing
• Internet Infrastructure: Networking, Web Services, and Cloud Computing
• Moving to the Cloud: Developing Apps in the New World of Cloud Computing
• Business in the Cloud: What Every Business Needs to Know About Cloud Computing
• Distributed and Cloud Computing: From Parallel Processing to the Internet of Things

Computer Vision:

• Computer and Machine Vision: Theory, Algorithms, Practicalities
• Computer Vision and Action Recognition
• Computer Vision and Applications: A Guide for Students and Practitioners
• Computer Vision Metrics
• Machine Learning in Computer Vision
• Practical Computer Vision
• Computer Vision: Algorithms and Applications
• Computer Vision – ACCV 2010
• Natural Image Statistics: A Probabilistic Approach to Early Computational Vision
• Robust Computer Vision: Theory and Applications
• Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
• Algorithms for Image Processing and Computer Vision
• Computer Vision: Models, Learning, and Inference
• Foundations of Computer Vision

Pattern Recognition:

• Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
• Pattern Recognition and Image Analysis
• Neural Networks for Pattern Recognition
• Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
• Pattern Recognition
• Frontiers of Pattern Recognition
• A Probabilistic Theory of Pattern Recognition
• Image Processing and Pattern Recognition: Fundamentals and Techniques
• Pattern Recognition and Classification
• A First Course in Machine Learning
• Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition
• Computational Intelligence in Multi-Feature Visual Pattern Recognition
• Trade What You See: How to Profit from Pattern Recognition
• Improving Pattern Recognition Methods for Speaker Recognition
• Technical Analysis for Algorithmic Pattern Recognition
• Pattern Recognition and Trading Decisions
• Mathematical Methodologies in Pattern Recognition and Machine Learning
• Pattern Recognition: An Algorithmic Approach
• Introduction To Pattern Recognition And Machine Learning
• Fibonacci ratios with pattern recognition
• Support Vector Machines for Pattern Classification
• Error Estimation for Pattern Recognition
• Feature Selection for Data and Pattern Recognition
• Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
• Granular Neural Networks, Pattern Recognition and Bioinformatics
• Speech Pattern Recognition for Speech To Text Conversion
• Markov Models for Pattern Recognition: From Theory to Applications
• Matrix Methods in Data Mining and Pattern Recognition
• Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior
• Pattern Recognition in Computational Molecular Biology: Techniques and Approaches
• Statistical Pattern Recognition
• Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence
• Data Analysis and Pattern Recognition in Multiple Databases
• Correlation Pattern Recognition
• Data Complexity in Pattern Recognition
• Evolutionary Synthesis of Pattern Recognition Systems
• Face recognition using statistical adapted local binary patterns
• Fine-Needle Biopsy of Superficial and Deep Masses: Interventional Approach and Interpretation Methodology by Pattern Recognition
• Handbook of Geometric Computing: Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics
• Handbook of Pattern Recognition and Computer Vision
• Scalable Pattern Recognition Algorithms
• Neuromorphic computational models for machine learning and pattern recognition from multi-modal time-series data
• Similarity-Based Pattern Analysis and Recognition
• Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and Applications
• Artificial Neural Networks in Pattern Recognition
• Evolution of Spiking Neural Networks for Temporal Pattern Recognition and Animat Control
• On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
• Pattern Recognition on Oriented Matroids
• Syntactic Pattern Recognition, Applications
• Type-2 Fuzzy Graphical Models for Pattern Recognition

TensorFlow:

• Learning TensorFlow: A Guide to Building Deep Learning Systems
• Machine Learning For Dummies
• Machine Learning with TensorFlow
• Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
• Reinforcement Learning with TensorFlow
• Getting Started with TensorFlow
• TensorFlow For Dummies
• TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms
• TensorFlow in 1 Day: Make your own Neural Network
• TensorFlow Machine Learning Cookbook
• R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet

Neural Networks:

• Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
• Convolutional Neural Networks in Visual Computing
• Deep Learning and Convolutional Neural Networks for Medical Image Computing
• Deep Learning: Practical Neural Networks with Java
• Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization
• Neural Networks and Learning Machines
• Elements of Artificial Neural Networks
• FPGA Implementations of Neural Networks
• Neural Network Systems Techniques and Applications
• Guide to Convolutional Neural Networks
• Introduction to Artificial Intelligence
• Introduction to the Math of Neural Networks
• Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
• Neural Networks − A Visual Introduction For Beginners
• Neural Network Toolbox User's Guide
• Artificial Neural Networks: A Practical Course
• Neural Networks and Statistical Learning
• Neural Networks for Pattern Recognition
• Neural Networks with R
• Neural Networks: Methodology and Applications
• Neural Networks and Deep Learning
• New Directions in Neural Networks
• TensorFlow in 1 Day: Make your own Neural Network
• The Application of Neural Networks in the Earth System Sciences
• Artificial Intelligence for Humans, Volume 3: Neural Networks and Deep Learning

Impactful Papers and Literature on AI

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AI Hardware:

• An Analysis of Deep Neural Network Models for Practical Applications
• Benchmarking TPU, GPU, and CPU Platforms for Deep Learning
• Deep Learning Hardware: Past, Present, and Future
• Eyeriss v2: A Flexible and High-Performance Accelerator for Emerging Deep Neural Networks
• Faster Neural Network Training with Data Echoing
• Recent progress in analog memory-based accelerators for deep learning
• Rethinking floating point for deep learning

AI Business:

• Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models

AI Game Theory:

• A Polynomial-time Nash Equilibrium Algorithm for Repeated Games
• Building Machines that Learn and Think for Themselves: Commentary on Lake, Ullman, Tenenbaum, and Gershman, Behavioral and Brain Sciences, 2017
• Friend-or-Foe Q-learning in General-Sum Games
• An introduction to information theory and entropy
• Markov games as a framework for multi-agent reinforcement learning
• Monte-Carlo Tree Search: A New Framework for Game AI
• Non-zero-sum Game Theory, Auctions and Negotiation
• Solving Stochastic Games
• Introduction to Game Theory
• Toward an AI Physicist for Unsupervised Learning
• Games with Hidden Information

Application of AI:

• Discovering physical concepts with neural networks
• Exploring galaxy evolution with generative models
• Learning to Predict the Cosmological Structure Formation
• Newton vs the machine: solving the chaotic three-body problem using deep neural networks
• QuCumber: wavefunction reconstruction with neural networks
• Tackling Climate Change with Machine Learning
• TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
• Unsupervised word embeddings capture latent knowledge from materials science literature

Deep Learning:

• A Closer Look at Memorization in Deep Networks
• A disciplined approach to neural network hyper-parameters: Part 1 − learning rate, batch size, momentum, and weight decay
• A fast learning algorithm for deep belief nets
• A Neural Probabilistic Language Model
• Regularized Evolution for Image Classifier Architecture Search
• An exact mapping between the Variational Renormalization Group and Deep Learning
• Automatic Differentiation in Machine Learning: a Survey
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
• Dive into Deep Learning
• Data Synthesis based on Generative Adversarial Networks
• Deep Boltzmann Machines
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Deep Sparse Rectifier Neural Networks
• Deep, Skinny Neural Networks are not Universal Approximators
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• Do Neural Networks Show Gestalt Phenomena?: An Exploration of the Law of Closure
• Don't Decay the Learning Rate, Increase the Batch Size
• Dropout: A Simple Way to Prevent Neural Networks from Overfitting
• Dynamic Routing Between Capsules
• Efficient BackProp
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• End-To-End Memory Networks
• Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• FixingWeight Decay Regularization in Adam
• FractalNet: Ultra-Deep Neural Networks without Residuals
• Generative Adversarial Nets
• Deep Learning: A Critical Appraisal
• Geometric deep learning: going beyond Euclidean data
• Going deeper with convolutions
• Gradient-Based Learning Applied to Document Recognition
• Deep Residual Learning for Image Recognition
• Neural Computation of Decisions in Optimization Problems
• How transferable are features in deep neural networks?
• ImageNet Classification with Deep Convolutional Neural Networks
• Learning long-term dependencies with gradient descent is difficult
• Long short-term memory
• Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
• Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
• Mastering the game of Go with deep neural networks and tree search
• Mathematics of Deep Learning
• Maxout Networks
• Mixed Precision Training
• Mixture Density Networks
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• MorphNet: Fast and Simple Resource-Constrained Structure Learning of Deep Networks
• Network In Network
• Neural Architecture Search with Reinforcement Learning
• Neural Arithmetic Logic Units
• Neural Machine Translation by Jointly Learning to Align and Translate
• Neural Networks for Optimal Approximation of Smooth and Analytic Functions
• Neural Ordinary Differential Equations
• Neuro-Dynamic Programming: An Overview
• Number detectors spontaneously emerge in a deep neural network designed for visual object recognition
• On the difficulty of training recurrent neural networks
• On the Variance of the Adaptive Learning Rate and Beyond
• Opening the black box of Deep Neural Networks via Information
• Piecewise Linear Multilayer Perceptrons and Dropout
• Practical Recommendations for Gradient-Based Training of Deep Architectures
• Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)
• Rectified Linear Units Improve Restricted Boltzmann Machines
• Representation Learning: A Review and New Perspectives
• Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
• Searching for Activation Functions
• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
• Session-based Recommendations with Recurrent Neural Networks
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• SGDR: Stochastic Gradient Descent with Warm Restarts
• Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
• The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
• The Matrix Calculus You Need For Deep Learning
• The power of deeper networks for expressing natural functions
• Tutorial on Variational Autoencoders
• Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure
• Understanding Convolutional Neural Networks with A Mathematical Model
• Understanding deep learning requires rethinking generalization
• Understanding the difficulty of training deep feedforward neural networks
• Universal Approximation Bounds for Superpositions of a Sigmoidal Function
• Universal Approximation using Radial-Basis-Function Networks
• Unsupervised learning by competing hidden units
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Visualizing and Understanding Recurrent Networks
• Weight Agnostic Neural Networks
• Why does deep and cheap learning work so well?
• YOLO9000: Better, Faster, Stronger
• You Only Look Once: Unified, Real-Time Object Detection

Explainability in AI:

• Attentive Explanations: Justifying Decisions and Pointing to the Evidence
• Explainable Artificial Intelligence (XAI)
• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
• The Mythos of Model Interpretability
• Towards Robust Interpretability with Self-Explaining Neural Networks

Fairness, Bias, Ethics in AI and ML:

• Fair is Better than Sensational: Man is to Doctor as Woman is to Doctor
• The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning

General Machine Learning:

• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
• A Few Useful Things to Know about Machine Learning
• A Survey of Collaborative Filtering Techniques
• A survey of cross-validation procedures for model selection
• Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
• AdaBoost
• An Efficient Approach for Assessing Hyperparameter Importance
• An Impossibility Theorem for Clustering
• An Interactive Machine Learning Framework
• API design for machine learning software: experiences from the scikit-learn project
• Pattern Recognition and Machine Learning
• Boosting the margin: A new explanation for the effectiveness of voting methods
• For valid generalization, the size of the weights is more important than the size of the network
• Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
• Multi-Agent Reinforcement Learning: a critical survey
• Machine learning at the energy and intensity frontiers of particle physics
• How can machine learning solve my problem?
• Please Stop Explaining Black Box Models for High-Stakes Decisions
• Regression Error Characteristic Curves
• Restructuring Sparse High Dimensional Data for Effective Retrieval
• Support-vector networks
• The Optimality of Naive Bayes
• The Riemannian Geometry of Deep Generative Models
• Theoretical Impediments to Machine Learning: A position paper
• TherML: Thermodynamics of Machine Learning
• Top 10 algorithms in data mining
• CS260: Machine Learning Theory Lecture 13: Weak vs. Strong Learning and the Adaboost Algorithm

Learning Theory:

• Computational learning theory
• Efficiency and Computational Limitations of Learning Algorithms
• Gaussian Processes for Machine Learning

NLP, Language models:

• BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
• Language Models are Unsupervised Multitask Learners

Reinforcement Learning:

• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
• A View on Deep Reinforcement Learning in System Optimization
• Adaptive Critics and the Basal Ganglia
• Algorithms for Reinforcement Learning
• An Analysis of Temporal-Difference Learning with Function Approximation
• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
• Curiosity-driven Exploration by Self-supervised Prediction
• Curious model-building control systems
• Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games
• Deep Recurrent Q-Learning for Partially Observable MDPs
• Deep reinforcement learning
• Deep Reinforcement Learning that Matters
• DeepMDP: Learning Continuous Latent Space Models for Representation Learning
• DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
• Episodic Curiosity through Reachability
• 10703 Deep Reinforcement Learning and Control
• Making RL practical
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Mastering the Game of Go without Human Knowledge
• Near-Optimal Reinforcement Learning in Polynomial Time
• PEGASUS: A policy search method for large MDPs and POMDPs
• Playing Atari with Deep Reinforcement Learning
• Policy Gradient Methods for Reinforcement Learning with Function Approximation
• Prefrontal cortex as a meta-reinforcement learning system
• Proximal Policy Optimization Algorithms
• Reinforcement Learning: A Tutorial Survey and Recent Advances
• Reinforcement Learning: An Introduction
• Reinforcement Learning: A Survey
• Reinforcement Learning
• Reinforcement Learning for Long-Run Average Cost
• Lecture: Introduction to Reinforcement Learning

Statistics and Statistical Learning:

• A survey of dimension reduction techniques
• Independent Component Analysis: Algorithms and Applications
• Nonlinear Principal Component Analysis Using Autoassociative Neural Networks
• On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
• Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics
• Regularization Paths for Generalized Linear Models via Coordinate Descent
• Statistical Modeling: The Two Cultures
• Survey on Independent Component Analysis

3D Machine Learning Papers:

• OctNet: Learning Deep 3D Representations at High Resolutions
• Deep Convolutional Priors for Indoor Scene Synthesis
• Synthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC
• Automatic 3D Indoor Scene Modeling from Single Panorama
• Unsupervised Texture Transfer from Images to Model Collections
• Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
• Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
• Learning 3D Mesh Segmentation and Labeling
• Functionality Preserving Shape Style Transfer
• Shape Completion from a Single RGBD Image
• GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction
• 3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
• Learning Part-based Templates from Large Collections of 3D Shapes
• Learning a Predictable and Generative Vector Representation for Objects
• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
• Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering
• Model Composition from Interchangeable Components
• Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
• Supplementary Material for Human-centric Indoor Scene Synthesis Using Stochastic Grammar
• Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs
• Deep Generative Modeling for Scene Synthesis via Hybrid Representations
• Style-Content Separation by Anisotropic Part Scales
• Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images
• ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
• 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
• Deep Marching Cubes: Learning Explicit Surface Representations
• Object Detection in 3D Scenes Using CNNs in Multi-view Images
• PU-Net: Point Cloud Upsampling Network
• Category-Specific Object Reconstruction from a Single Image
• Dynamic Graph CNN for Learning on Point Clouds
• 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation
• Learning Representations and Generative Models for 3D Point Clouds
• SPLATNet: Sparse Lattice Networks for Point Cloud Processing
• Weakly supervised 3D Reconstruction with Adversarial Constraint
• GRAINS: Generative Recursive Autoencoders for INdoor Scenes
• Im2Struct: Recovering 3D Shape Structure from a Single RGB Image
• Joint Material and Illumination Estimation from Photo Sets in the Wild
• 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
• LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
• Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
• Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• What Is Around The Camera?
• Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
• Frustum PointNets for 3D Object Detection from RGB-D Data
• Learning to Generate Chairs, Tables and Cars with Convolutional Networks
• 3D Sketching using Multi-View Deep Volumetric Prediction
• Geometric deep learning on graphs and manifolds using mixture model CNNs
• FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans
• 3D Bounding Box Estimation Using Deep Learning and Geometry
• A Point Set Generation Network for 3D Object Reconstruction from a Single Image
• SEGCloud: Semantic Segmentation of 3D Point Clouds
• ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
• The space of human body shapes: reconstruction and parameterization from range scans
• Complete 3D Scene Parsing from an RGBD Image
• ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
• Deep Hough Voting for 3D Object Detection in Point Clouds
• 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans
• Learning Informative Edge Maps for Indoor Scene Layout Prediction
• Pano2CAD: Room Layout From A Single Panorama Image
• Modelling Uncertainty in Deep Learning for Camera Relocalization
• PCPNET Learning Local Shape Properties from Raw Point Clouds
• View Synthesis by Appearance Flow
• Interactive 3D Modeling with a Generative Adversarial Network
• 3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare
• Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
• Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
• Volumetric and Multi-View CNNs for Object Classification on 3D Data
• OctNetFusion: Learning Depth Fusion from Data
• AttribIt: Content Creation with Semantic Attributes
• Co-Locating Style-Defining Elements on 3D Shapes
• Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
• FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
• PointGrid: A Deep Network for 3D Shape Understanding
• Learning Material-Aware Local Descriptors for 3D Shapes
• Geometric loss functions for camera pose regression with deep learning
• A Probabilistic Model for Component-Based Shape Synthesis
• Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image
• Modeling by Example
• TextureGAN: Controlling Deep Image Synthesis with Texture Patches
• CSGNet: Neural Shape Parser for Constructive Solid Geometry
• Shape Generation using Spatially Partitioned Point Clouds
• Mesh-based Autoencoders for Localized Deformation Component Analysis
• Visual Object Networks: Image Generation with Disentangled 3D Representation
• ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
• Relative Camera Pose Estimation Using Convolutional Neural Networks
• Unsupervised Learning of 3D Structure from Images
• A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition
• Im2Avatar: Colorful 3D Reconstruction from a Single Image
• Deformable Shape Completion with Graph Convolutional Autoencoders
• Hierarchical Surface Prediction for 3D Object Reconstruction
• Multi-view 3D Models from Single Images with a Convolutional Network
• Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
• Automated Interior Design Using a Genetic Algorithm
• Human-centric Indoor Scene Synthesis Using Stochastic Grammar
• Scan2Mesh: From Unstructured Range Scans to 3D Meshes
• PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
• 3D Shape Induction from 2D Views of Multiple Objects
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• RoomNet: End-to-End Room Layout Estimation
• MeshNet: Mesh Neural Network for 3D Shape Representation
• SceneSuggest: Context-driven 3D Scene Design
• Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
• Predicting Complete 3D Models of Indoor Scenes
• PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
• Probabilistic Reasoning for Assembly-Based 3D Modeling
• RGBD Datasets: Past, Present and Future
• A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation
• Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks
• 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds
• Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
• A Morphable Model For The Synthesis Of 3D Faces
• Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
• 3D Object Detection with Latent Support Surfaces
• Recovering the Spatial Layout of Cluttered Rooms
• VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition
• Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network
• Texture Synthesis Using Convolutional Neural Networks
• Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients
• Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
• SurfNet: Generating 3D shape surfaces using deep residual networks
• Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
• FusionNet: 3D Object Classification Using Multiple Data Representations
• Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
• SeeThrough: Finding Objects in Heavily Occluded Indoor Scene Images
• Design Preserving Garment Transfer
• Single-View Reconstruction via Joint Analysis of Image and Shape Collections
• Reflectance and Natural Illumination from Single-Material Specular Objects Using Deep Learning
• Three-Dimensional Object Detection and Layout Prediction using Clouds of Oriented Gradients
• Elements of Style: Learning Perceptual Shape Style Similarity

Awesome Papers on Deep Vision:

• Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5)
• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• Learning to Deblur
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Going deeper with convolutions
• Explain Images with Multimodal Recurrent Neural Networks
• Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
• Fully Convolutional Networks for Semantic Segmentation
• Long-term Recurrent Convolutional Networks for Visual Recognition and Description
• Show and Tell: A Neural Image Caption Generator
• From Captions to Visual Concepts and Back
• Learning a Recurrent Visual Representation for Image Caption Generation
• DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection
• Translating Videos to Natural Language Using Deep Recurrent Neural Networks
• Multiple Object Recognition with Visual Attention
• Image Super-Resolution Using Deep Convolutional Networks
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Conditional Random Fields as Recurrent Neural Networks
• Phrase-based Image Captioning
• DRAW: A Recurrent Neural Network For Image Generation
• Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
• Describing Videos by Exploiting Temporal Structure
• Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
• Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
• BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
• Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
• Holistically-Nested Edge Detection
• Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
• FlowNet: Learning Optical Flow with Convolutional Networks
• Compression Artifacts Reduction by a Deep Convolutional Network
• Fast R-CNN
• VQA: Visual Question Answering
• Sequence to Sequence – Video to Text
• Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
• Language Models for Image Captioning: The Quirks and WhatWorks
• Jointly Modeling Embedding and Translation to Bridge Video and Language
• Exploring Models and Data for Image Question Answering
• Learning Deconvolution Network for Semantic Segmentation
• Exploring Nearest Neighbor Approaches for Image Captioning
• Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
• What Value Do Explicit High Level Concepts Have in Vision to Language Problems?
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• The Long-Short Story of Movie Description
• Deeply Learning the Messages in Message Passing Inference
• Learning language through pictures
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
• Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
• R-CNN minus R
• Distributional Smoothing with Virtual Adversarial Training
• Describing Multimedia Content using Attention-based Encoder–Decoder Networks
• Joint Calibration for Semantic Segmentation
• Image Representations and New Domains in Neural Image Captioning
• Semantic Image Segmentation via Deep Parsing Network
• Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
• Stacked Attention Networks for Image Question Answering
• Generating Images from Captions with Attention
• Deep multi-scale video prediction beyond mean square error
• Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
• Censoring Representations with an Adversary
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Delving Deeper into Convolutional Networks for Learning Video Representations
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Variational Auto-encoded Deep Gaussian Processes
• Auxiliary Image Regularization for Deep CNNs with Noisy Labels
• Multi-Scale Context Aggregation by Dilated Convolutions
• Pushing the Boundaries of Boundary Detection using Deep Learning
• Spherical CNNs
• SSD: Single Shot MultiBox Detector
• Deep Residual Learning for Image Recognition
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
• Dynamic Memory Networks for Visual and Textual Question Answering
• Colorful Image Colorization
• Context Encoders: Feature Learning by Inpainting
• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
• Training Recurrent Answering Units with Joint Loss Minimization for VQA
• Conditional Image Generation with PixelCNN Decoders
• PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
• Generative Visual Manipulation on the Natural Image Manifold
• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
• Speed/accuracy trade-offs for modern convolutional object detectors
• Temporal Tessellation: A Unified Approach for Video Analysis
• Recurrent Models of Visual Attention
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Deep Filter Banks for Texture Recognition and Segmentation
• Deep Visual-Semantic Alignments for Generating Image Descriptions
• Deep Convolutional Neural Network for Image Deconvolution
• Deep Learning Tutorial
• Learning a Deep Compact Image Representation for Visual Tracking
• Learning to Generate Chairs with Convolutional Networks
• Learning a Deep Convolutional Network for Image Super-Resolution
• Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
• Learning Hierarchical Features for Scene Labeling
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution: Supplementary Mate
• Rich feature hierarchies for accurate object detection and semantic segmentation
• Finding Action Tubes
• Hypercolumns for Object Segmentation and Fine-grained Localization
• Deep Networks for Image Super-Resolution with Sparse Prior
• Learning Iterative Image Reconstruction
• Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid
• Deep Residual Learning
• Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
• Understanding image representations by measuring their equivariance and equivalence
• Predicting Eye Fixations using Convolutional Neural Networks
• Fully Convolutional Networks for Semantic Segmentation
• Hierarchical Convolutional Features for Visual Tracking
• Understanding Deep Image Representations by Inverting Them
• Feedforward semantic segmentation with zoom-out features
• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
• Deep Visual Analogy-Making
• Is object localization for free? – Weakly-supervised learning with convolutional neural networks
• DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking
• Weakly supervised graph based semantic segmentation by learning communities of image-parts
• Deep Hierarchical Parsing for Semantic Segmentation
• Learning a Sequential Search for Landmarks
• DeepFace: Closing the Gap to Human-Level Performance in Face Verification
• Designing Deep Networks for Surface Normal Estimation
• Sketch-based 3D Shape Retrieval using Convolutional Neural Networks
• Deep Edge-Aware Filters
• Learning Query and Image Similarities with Ranking Canonical Correlation Analysis
• Computing the Stereo Matching Cost with a Convolutional Neural Network
• Visualizing and Understanding Convolutional Networks
• Appearance-Based Gaze Estimation in the Wild
• Cross-scene Crowd Counting via Deep Convolutional Neural Networks

Paper List On Deep Generative Models:

• Statistical Inference of Intractable Generative Models via Classification
• Nice: Non-linear Independent Components Estimation
• Deep Exponential Families
• DRAW: A Recurrent Neural Network For Image Generation
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• The Variational Gaussian Process
• Pixel Recurrent Neural Networks
• Generating images with recurrent adversarial networks
• Towards Conceptual Compression
• Adversarial Feature Learning
• Adversarially Learned Inference
• Improved Techniques for Training GANs
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Deep Boltzmann Machines
• Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
• The Helmholtz Machine
• The Neural Autoregressive Distribution Estimator
• Autoencoding beyond pixels using a learned similarity metric
• Auxiliary Deep Generative Models
• An Efficient Learning Procedure for Deep Boltzmann Machines
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Relationship between Pretraining and Maximum Likelihood Estimation in Deep Boltzmann Machines

The Best Artificial Intelligence Books:

• AI Techniques for Game Programming
• Artificial Intelligence: A Systems Approach
• Artificial Intelligence
• Artificial Intelligence: Mirrors for the Mind
• Artificial Intelligence Illuminated
• Artificial Intelligence in the 21st Century: A Living Introduction
• Artificial Intelligence with Python
• Biologically Inspired Artificial Intelligence for Computer Games
• Fundamentals of the New Artificial Intelligence
• Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases
• Introduction to Genetic Algorithms
• Practical Genetic Algorithms
• Practical Python AI Projects: Mathematical Models of Optimization Problems with Google OR-Tools

Best Keras Books of All Time:

• Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
• Beginning Application Development with TensorFlow and Keras
• Deep Learning with Keras: Implement neural networks with Keras on Theano and TensorFlow
• Advanced Deep Learning with Keras
• Keras Reinforcement Learning Projects
• Deep Learning with Keras: Beginner's Guide To Deep Learning With Keras
• Keras to Kubernetes: The Journey of a Machine Learning Model to Production
• Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
• Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
• Keras Succinctly

Biology ML Papers:

• Highway Networks
• DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins
• DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation
• Fast animal pose estimation using deep neural networks
• ATP7B Variant c.1934T>G p.Met645Arg Causes Wilson Disease by Promoting Exon 6 Skipping
• Deep learning at base-resolution reveals motif syntax of the cis-regulatory code
• Transcriptomic learning for digital pathology
• Creating Artificial Human Genomes Using Generative Models
• Structure-Based Function Prediction using Graph Convolutional Networks
• Expanding functional protein sequence space using generative adversarial networks
• CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets
• Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
• Diet Networks: Thin Parameters for Fat Genomics
• Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data

Papers On Database Management Systems:

• Parallel Database Systems: The Future of High Performance Database Systems
• ARIES: A Transaction Recovery Method Supporting Fine-Granularity Locking and Partial Rollbacks Using Write-Ahead Logging
• Relational Model of Data for Large Shared Data Banks
• An Evaluation of Buffer Management Strategies for Relational Database Systems
• Encapsulation of parallelism in the Volcano query processing system
• The design of the POSTGRES storage system
• Architecture of a Database System
• Query Evaluation Techniques for Large Databases
• Granularity of Locks and Degrees of Consistency in a Shared Data Base
• The Gamma Database Machine Project
• Join Processing in Database Systems with Large Main Memories
• On Optimistic Methods for Concurrency Control
• Main Memory Database Systems: An Overview
• Improved Query Performance with Variant Indexes
• Rethinking Database System Architecture: Towards a Self-tuning RISC-style Database System
• Database Architecture Optimized for the new. Bottleneck: Memory Access
• DBMSs On A Modern Processor: Where Does Time Go?
• Transaction Management in the R* Distributed Database Management System
• Efficient Locking for Concurrent Operations on B-Trees
• A Case for Redundant Arrays of Inexpensive Disks (RAID)
• R* Optimizer Validation and Performance Evaluation for Distributed Queries
• A Case for Fractured Mirrors
• An Overview of Data Warehousing and OLAP Technology
• Operating System Support for Database Management
• A History and Evaluation of System R
• Why Do Computers Stop and What Can Be Done About It?

Face Recognition:

• 3D Face Modeling, Analysis and Recognition
• Dynamic Vision: From Images to Face Recognition
• Face Detection and Recognition: Theory and Practice
• Face Recognition Technique: A Literature Survey on Face Recognition and Insight on Machine Recognition Using Software
• Face recognition: Methods, applications and technology
• Kernel Learning Algorithms for Face Recognition
• Methods for face detection and adaptive face recognition
• Machine-based Intelligent Face Recognition
• Video Analytics: Face and Facial Expression Recognition and Audience Measurement
• Face and Facial Expression Recognition from Real World Videos

Text Mining:

• An Introduction to Text Mining: Research Design, Data Collection, and Analysis
• Mastering Text Mining with R
• Mining Text Data
• Survey of Text Mining II: Clustering, Classification, and Retrieval
• Text Mining in Practice with R
• Text Mining with R: A Tidy Approach
• Text Mining: Applications and Theory
• Text Mining: From Ontology Learning to Automated Text Processing Applications
• The Text Mining Handbook
• Fundamentals of Predictive Text Mining
• Natural Language Processing and Text Mining
• Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS
• Text Mining for Qualitative Data Analysis in the Social Sciences
• Text Mining with Information Extraction
• Statistical Issues in Quantifying Text Mining Performance
• A Text Mining Framework for Discovering Technological Intelligence to Support Science and TechnologyManagement
• Text Mining Using Data Compression Models
• Graphical Models for Text Mining: Knowledge Extraction and Performance Estimation
• Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data
• Text Mining and Internet Content Filtering
• Scalable Text Mining with Sparse Generative Models
• Text Mining for Drug Discovery
• A Tool for Text Mining in Molecular Biology Domains
• Practical Text Mining With Per1
• Text mining for cross-domain knowledge discovery
• Text Mining in Financial Industry: Implementing Text Mining Techniques on Bank Policies
• Text Mining: Classification, Clustering, and Applications
• Unsupervised Algorithms for Cross-Lingual Text Analysis, Translation Mining, and Information Retrieval
• Survey of Text Mining: Clustering, Classification, and Retrieval Scanned by Velocity
• Text Mining for Biology and Biomedicine
• Text Mining with MATLAB

Network Architecture Search (NAS) Papers:

• Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves
• Learning curve prediction with Bayesian Neural Networks
• Neural Architecture Search with Reinforcement Learning
• Designing Neural Network Architectures using Reinforcement Learning
• Large-Scale Evolution of Image Classifiers
• Efficient Architecture Search by Network Transformation
• Learning Transferable Architectures for Scalable Image Recognition
• Hierarchical Representations for Efficient Architecture Search
• Progressive Neural Architecture Search
• Regularized Evolution for Image Classifier Architecture Search
• Efficient Neural Architecture Search via Parameter Sharing
• Path-Level Network Transformation for Efficient Architecture Search
• DARTS: Differentiable Architecture Search
• MnasNet: Platform-Aware Neural Architecture Search for Mobile
• Neural Architecture Optimization
• You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
• MorphNet: Fast and Simple Resource-Constrained Structure Learning of Deep Networks
• AMC: AutoML for Model Compression and Acceleration on Mobile Devices
• Learning Transferable Architectures for Scalable Image Recognition