# Tag: Bayesian (37)

**How I Learned to Stop Worrying and Love Uncertainty**- Oct 24, 2018.

This is a written version of Data Scientist Adolfo Martínez’s talk at Software Guru’s DataDay 2017. There is a link to the original slides (in Spanish) at the top of this post.**The Intuitions Behind Bayesian Optimization with Gaussian Processes**- Oct 19, 2018.

Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.**Unfolding Naive Bayes From Scratch**- Sep 25, 2018.

Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!**Frequentists Fight Back**- May 24, 2018.

Frequentist methods are sometimes described as “classical”, though most have only appeared in recent decades and new ones are under development as you read this. Whatever we call it, this branch of statistics is very much alive.**5 Machine Learning Projects You Should Not Overlook**- Feb 8, 2018.

It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!**What is a Bayesian Neural Network?**- Dec 5, 2017.

BNNs are important in specific settings, especially when we care about uncertainty very much.**The amazing predictive power of conditional probability in Bayes Nets**- Nov 13, 2017.

This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.**How Bayesian Networks Are Superior in Understanding Effects of Variables**- Nov 9, 2017.

Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.**Vital Statistics You Never Learned… Because They’re Never Taught**- Aug 29, 2017.

Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.**The Truth About Bayesian Priors and Overfitting**- Jul 25, 2017.

Many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain.**When not to use deep learning**- Jul 24, 2017.

Despite DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining models and features to general public is required.

**Optimizing Web sites: Advances thanks to Machine Learning**- Jul 17, 2017.

Machine learning has revitalized a nearly dormant method, leading to a powerful approach for optimizing Web pages, finding the best of thousands of alternatives.**Top KDnuggets tweets, Feb 22-28: 50 Companies Leading the #AI Revolution; #AI Nanodegree Program Syllabus**- Mar 1, 2017.

50 Companies Leading the #AI Revolution; #AI Nanodegree Program Syllabus: Term 1, In Depth; What is a Support Vector Machine, and Why Would I Use it?; 6 Easy Steps to Learn Naive #Bayes Algorithm (with code in #Python).**Surprising Popularity: A Solution to the Crowd Wisdom Problem**- Feb 15, 2017.

This is an overview of a recent proposed method for solving the crowd wisdom problem: select the answer that is more popular than people predict. Research shows that this principle yields the best answer under reasonable assumptions about voter behavior.**KDnuggets™ News 16:n45, Dec 21: 50+ Data Science, ML Cheat Sheets; Ethics of Self-Driving Cars? Top experts on Machine Learning Main Events, Key Trends**- Dec 21, 2016.

Also New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? Machine Learning, AI Main Events and Key Trends; 5 Basic Types of Data Science Interview Questions.**Introduction to Bayesian Inference**- Dec 16, 2016.

Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.**KDnuggets™ News 16:n44, Dec 14: Key Data Science 2016 Events, 2017 Trends; Where Data Science was applied; Bayesian Basics**- Dec 14, 2016.

Data Science, Predictive Analytics Main Developments in 2016, Key Trends in 2017; Where Analytics, Data Mining, Data Science were applied in 2016; Bayesian Basics, Explained; Data Science Trends To Look Out For In 2017; Artificial Neural Networks (ANN) Introduction**Bayesian Basics, Explained**- Dec 9, 2016.

This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics and how it differs from the ordinary statistics most of us learned in college.**KDnuggets Top Blogs and Bloggers in November 2016**- Dec 8, 2016.

We recognize the best KDnuggets Bloggers who had the most popular blogs by views or social media shares in November 2016.**How Bayesian Inference Works**- Nov 15, 2016.

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.**5 EBooks to Read Before Getting into A Machine Learning Career**- Oct 21, 2016.

A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.**Learning from Imbalanced Classes**- Aug 31, 2016.

Imbalanced classes can cause trouble for classification. Not all hope is lost, however. Check out this article for methods in which to deal with such a situation.**What Statistics Topics are Needed for Excelling at Data Science?**- Aug 2, 2016.

Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.**The Core of Data Science**- Aug 1, 2016.

This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.**Barley, Hops, and Bayes: Predicting The World Beer Cup**- Jul 26, 2016.

This post covers predicting award counts by the United States in an international beer competition. Exploratory data analysis and Bayes methods are also supported.**Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab**- Jul 20, 2016.

Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence**KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble**- Jul 20, 2016.

Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu**Bayesian Machine Learning, Explained**- Jul 13, 2016.

Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.

**Top KDnuggets tweets, Jun 22-28: #Bayesian #Statistics explained in Simple English; Brexit**- Jun 29, 2016.

#Bayesian #Statistics explained to Beginners in Simple English; Amazing analysis of #Brexit with #MachineLearning - it is sad; 18 Useful Mobile Apps for #DataScientist; Sharp divisions between England, #Scotland in #Brexit vote suggest future UK split.**When Good Advice Goes Bad**- Mar 14, 2016.

Consider these 4 examples of good statistical advice which, when misused, can go bad.**Top KDnuggets tweets, Feb 22-28: Quantifying Similarity in Structured Data; #Oscar #DataScience: 4-5 nominations no guarantee of winning**- Feb 29, 2016.

A Statistical View of #DeepLearning; Impressive tutorial - Tree Kernels: Quantifying Similarity in Structures; Conversation with Data Scientist Sebastian Raschka - new podcast; How to become a #Bayesian in eight easy steps.**Top KDnuggets tweets, Jan 11-24: Why R Users will inevitably become #Bayesians; Is #Quran really more violent that #Bible?**- Jan 25, 2016.

TextAnalytics examines: Is #Quran really more violent that #Bible? Why R Users will inevitably become #Bayesians; Next #MachineLearning problem: what to do with 80% accurate algorithm? ;Learning to Code #NeuralNetworks #MachineLearning Tutorial;**Nando de Freitas AMA: Bayesian Deep Learning, Justice, and the Future of AI**- Jan 6, 2016.

During his recent AMA, machine learning star Nando de Freitas answers a host of questions on a number of topics, including Bayesian methods in deep learning, harnessing AI for the good of humanity, and what the future holds for machine learning.**Top 10 Data Mining Algorithms, Explained**- May 21, 2015.

Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.**All Machine Learning Models Have Flaws**- Mar 3, 2015.

This classic post examines what is right and wrong with different models of machine learning, including Bayesian learning, Graphical Models, Convex Loss Optimization, Statistical Learning, and more.**Top /r/MachineLearning posts, Jan 11-17**- Jan 18, 2015.

SVMs, open source datasets, Bayesian decision theory, game AI, and deep learning visualizations are all featured in the past week's top /r/MachineLearning posts.**Machine Learning in 7 Pictures**- Mar 18, 2014.

Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures.