Learning From Data-Kaggle Group - Comunitate - Google+
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Artificial Intelligence - foundations of computational agents
https://bitbucket.org/les2/aiclass/src There is the source code I used throughout the course. There are the following files: PixelAlignment.py - pixel correspondence from scan linesKMeans.py - k-means algorithm as well as guassian and multivariate guassian regressionsSched.py - task network schedulingSmooth.py - Laplacian smoother that also does Markov chains (e.g., 'ABBBAAABA', what's P(A followed by A)ValIter.py - value iteration algorithm for MDP / grid world; you can customize the actions allowed (e.g., N/S/E/W or NE/NW/SE/SW) and combine stochastic and deterministic actions; the state space is only two dimensions so it will not handle a headingLinearFilter.py - convolve and image with a kernelLinearRegression.py - linear regression (find the w0 and w1) I used these programs for the homeworks / exams, so they work for the examples in class. Source code used for AI class
Weka (machine learning) The Weka logo. The weka is a bird endemic to New Zealand. free availability under the GNU General Public Licenseportability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platforma comprehensive collection of data preprocessing and modeling techniquesease of use due to its graphical user interfaces Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.
CS 188: Lectures
Introduction Q-Learning (section 21.2.3 of AIMA) is a type of temporal difference Reinforcement Learning that uses a value function to pick the best action. In this demo, a Q-Learning agent leans to navigate the maze from experience: It learns how likely each possible move (north, south, west, east) at each location of the maze is to lead to the goal. The video below shows how it works; you can also run OpenNERO yourself to test it interactively. Running the Demo To run the demo, QLearning - opennero - Q-Learning demo - game platform for Artificial Intelligence research and education
Easy AI with Python
Toy problem In scientific disciplines, a toy problem is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate a trait that may be shared by other, more complicated, instances of the problem, or as a way to explain a particular, more general, problem solving technique. For instance, while engineering a large system, the large problem is often broken down into many smaller toy problems which have been understood in good detail. Often these problems distill a few important aspects of complicated problems so that they can be studied in isolation. Toy problems are thus often very useful in providing intuition about specific phenomena in more complicated problems.
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CS 188: Lectures
Lesson / Unit 5 - Machine Learning
Amit’s A* Pages
Python Artificial Intelligence Posted by at 12:04 on 26 Apr 2005. There are 11 Comments Articles - Python Artificial Intelligence
Amit’s A* Pages
Bayes' Theorem Consider Goldie Lockes' predicament. She already has a probability distribution describing her (and Cactus's) beliefs about market demand for ACME's next-generation roadrunner traps. Bayes' Theorem