Subscriber Reviews Average Rating: Based on 8 Ratings Programming Collective Intelligence
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 lines KMeans.py - k-means algorithm as well as guassian and multivariate guassian regressions Sched.py - task network scheduling Smooth.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 heading LinearFilter.py - convolve and image with a kernel LinearRegression.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) Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java , developed at the University of Waikato , New Zealand . Weka is free software available under the GNU General Public License . [ edit ] Description The Weka logo.
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.
CS 188: Lectures
by Ivan De Marino - http://blog.ivandemarino.me The world has become very DATA-RICH: all sort of industries and scopes now produce an enormous amount of information, sometimes in realtime, and Machine Learning is THE discipline to make sense this "ocean" of information . Machine Learning is a KEY subfield of Artificial Intelligence, and it's main aim is to find ways to use this amount of data to produce knowledge, understanding out of it. Prof. 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
Google AI Challenge Ants is a multi-player strategy game set on a plot of dirt with water for obstacles and food that randomly drops. Each player has one or more hills where ants will spawn. The objective is for players to seek and destroy the most enemy ant hills while defending their own hills.
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