Data Science 101

DECEMBER 11, 2019

Every year they host an excellent and influential conference focusing on many areas of data science. Topics of interest include artificial intelligence, big data, data analytics, data science, data mining, deep learning, knowledge graphs, machine learning, relational databases and statistical methods.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

O'Reilly on Data

SEPTEMBER 12, 2019

The O’Reilly Data Show Podcast: Michael Mahoney on developing a practical theory for deep learning. In this episode of the Data Show , I speak with Michael Mahoney , a member of RISELab , the International Computer Science Institute , and the Department of Statistics at UC Berkeley.

Deep Learning 28

Deep Learning Statistics Data Processing Optimization 28

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. The majority (72%) of enterprises that use APIs for model access use models hosted on their cloud service providers.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

O'Reilly on Data

MARCH 31, 2020

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Machine learning adds uncertainty.

Management 333

Management Machine Learning Experimentation Metrics 333

Domino Data Lab

MAY 8, 2019

Machine learning model interpretability. At CMU I joined a panel hosted by Zachary Lipton where someone in the audience asked a question about machine learning model interpretation. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

JANUARY 25, 2021

AI, Machine Learning, and Data. Healthy growth in artificial intelligence has continued: machine learning is up 14%, while AI is up 64%; data science is up 16%, and statistics is up 47%. While AI and machine learning are distinct concepts, there’s enough confusion about definitions that they’re frequently used interchangeably.

Machine Learning 363

Machine Learning Software Testing Technology 363

Domino Data Lab

JULY 9, 2019

He advocated that an impactful ML solution does not end with Google Slides but becomes “a working API that is hosted or a GUI or some piece of working code that people can put to work” Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

datapine

NOVEMBER 19, 2019

AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI.

Technology 279

Technology Internet of Things IT IoT 279