Search results for "Artificial Intelligence"

A Gentle Introduction to Computational Learning Theory

A Gentle Introduction to Computational Learning Theory

Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having […]

Continue Reading 4
Difference Between Algorithm and Model in Machine Learning

Difference Between Algorithm and Model in Machine Learning

Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often “machine learning algorithm” is used interchangeably with “machine learning model.” Are they the same thing or something different? As a developer, your intuition with “algorithms” like sort algorithms and search algorithms will help to clear up […]

Continue Reading 29

Do you have tutorials on AI or AGI?

Sorry, I do not have tutorials on AI or AGI. I focus on predictive modeling with supervised learning, and maybe a little unsupervised learning. These are the areas of machine learning that the average developer may need to use “at work“. For a good layman introduction to AI, I recommend: Artificial Intelligence: A Guide for […]

Continue Reading 0
A Gentle Introduction to Stochastic in Machine Learning

What Does Stochastic Mean in Machine Learning?

The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.” The stochastic nature […]

Continue Reading 10
A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode. Maximum a Posteriori or MAP for short is a Bayesian-based […]

Continue Reading 5
A Gentle Introduction to Markov Chain Monte Carlo for Probability

A Gentle Introduction to Markov Chain Monte Carlo for Probability

Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable with probabilistic models, and instead, approximation methods must be used. Markov Chain Monte Carlo sampling provides a class of algorithms for systematic random sampling from high-dimensional probability distributions. Unlike Monte Carlo sampling methods that are […]

Continue Reading 8
Histogram Plots of Differently Sized Monte Carlo Samples From the Target Function

A Gentle Introduction to Monte Carlo Sampling for Probability

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity is intractable. This may be due to many reasons, such as the stochastic nature of the domain or an exponential number […]

Continue Reading 17
Histogram of Dataset Constructed From Two Different Gaussian Processes

A Gentle Introduction to Expectation-Maximization (EM Algorithm)

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires that the training dataset is complete, e.g. all relevant interacting random variables are present. Maximum likelihood becomes intractable if […]

Continue Reading 23