We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We highlight the challenges of modelling Plotting with PDDL and of solving it with a grounding-based state-of-the-art planner.

This is my first post and this post is for an absolute beginner. If you are stuck somewhere in this tutorial then don't worry about that. This post is just for you to make you familiar with the machine learning process, In the upcoming series of posts, we will discuss in-depth about the concepts. In this post, you will make your first machine learning project (step-by-step) in Python. This post is 1 day of the "10 days of machine learning project" post series.

Learn about PyTorch from zero to hero for Deep Learning. This tutorial is the most beginner-friendly place on the internet to learn PyTorch for deep learning.

Data Analytics Communication is the last step where the analyzed data is formally presented to stakeholders. Use of several data visualization techniques makes communication effective. Data visualization techniques are used for graphical representation of data and are powerful tools for effective communication of data. Examples of data visualization: - If you want to present information about new and existing customers on the website and their behavior when they access the website. Plotting is a data visualization technique used to represent underlying data through graphics.

Plotting is a tile-matching puzzle video game published by Taito in 1989. Its objective is to reduce a given grid of coloured blocks down to a goal number or fewer. This is achieved by the avatar character repeatedly shooting the block it holds into the grid. Plotting is an example of a planning problem: given a model of the environment, a planning problem asks us to find a sequence of actions that can lead from an initial state of the environment to a given goal state while respecting some constraints. The key difficulty in modelling Plotting is in capturing the way the puzzle state changes after each shot. A single shot can affect multiple tiles directly, and the grid is affected by gravity so numerous other tiles can be affected indirectly. We present and evaluate a constraint model of the Plotting problem that captures this complexity. We also discuss the difficulties and inefficiencies of modelling Plotting in PDDL, the standard language used for input to specialised AI planners. We conclude by arguing that AI planning could benefit from a richer modelling language.

Wikipedia: "In statistics, Bayesian linear regression is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference. When the regression model has errors that have a normal distribution, and if a particular form of the prior distribution is assumed, explicit results are available for the posterior probability distributions of the model's parameters." The most common interpretation of Bayes' formula in finance is the diachronic interpretation. This mainly states that over time we learn new information about certain variables or parameters of interest, like the mean return of a time series. Here, H stands for an event, the hypothesis, and D represents the data an experiment or the real world might present.

Linear Regression is without a doubt one of the most widely used machine algorithms because of the simple mathematics behind it and the ease with which it can be implemented. I have gone through in detail in some of my previous articles on how to make sure these assumptions are met and taken care of. In this article, I will be going over How Outliers can pose a serious problem for a Linear Regression model and how to detect them accordingly. Outliers are data points that fall far away from the major"cluster" of points. They can be legit data points carrying valuable information or can be erroneous values altogether.

In this blog post, I will be explaining how to plot confusion matrices in Python. This is my second blog post on the Confusion Matrix. If you want to understand what a confusion matrix is and how to get insights from the confusion matrix, check out my first blog post. I have attached the link below. Now, without further due, let's dive into how to plot a confusion matrix.

Lets start with this simple number series question. Find the next few numbers in this series 2, 5, 8, 11, 14... From the numbers it is trivial to guess that each number is 3 more than the previous number. So the numbers would be 17, 20, 23. This would be the end of this post if there were no machine learning. Can we predict the next number using some machine learning techniques.

Moore's law drove an information revolution that changed the world. But computers will need more than raw performance in the future. Because computers now act as agents for humans, new issues arise that can only be addressed if engineers coordinate with lawyers and businesspeople.