The Rubik's Cube Solution How to Solve the Rubik's Cube in Seven Steps The world's most famous puzzle, simultaneously beloved and despised for it's beautiful simple complexity, the Rubiks Cube has been frustrating gamers since Erno Rubik invented it back in 1974. Over the years many brave gamers have whole-heartedly taken up the challenge to restore a mixed Rubik's cube to it's colorful and perfect original configuration, only to find the solution lingering just out of their grasp time and time again. After spending hours and days twisting and turning the vaunted cube in vain, many resorted to removing and replacing the multi-colored facelets of the cube in a dastardly attempt to cheat the seemingly infallible logic of the cube, while others simply tossed it to the side and dubbed it impossible. Humanity required a solution, so intelligent gamers went to work to take down the so-called "frustration cube". Rubiks Cube Terminology and Move Notation The Seven-Step Guide to Solving a Rubiks cube Left Right Several times
Computational Neuroscience About the Course This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information. Course Syllabus Topics covered include: 1. Recommended Background Familiarity with basic concepts in linear algebra, calculus, and probability theory. In-course Textbooks
Best Machine Learning Resources for Getting Started This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource. If you are a true beginner and excited to get started in the field of machine learning, I hope you find something useful. Programming Libraries I am an advocate of “learn just enough to be dangerous and start trying things”. This is how I learned to program and I’m sure many other people learned that way too. Find a library and read the documentation, follow the tutorials and start trying things out. Video Courses Overview Papers Beginner Machine Learning Books
Neural Network Tutorial Introduction I have been interested in artificial intelligence and artificial life for years and I read most of the popular books printed on the subject. I developed a grasp of most of the topics yet neural networks always seemed to elude me. Sure, I could explain their architecture but as to how they actually worked and how they were implemented… well that was a complete mystery to me, as much magic as science. I bought several books on the subject but every single one attacked the subject from a very mathematical and academic viewpoint and very few even gave any practical uses or examples. So for a long long time I scratched my head and hoped that one day I would be able to understand enough to experiment with them myself. That day arrived some time later when - sat in a tent in the highlands of Scotland reading a book - I had a sudden blast of insight. The C++ source code for the tutorial and a pre-compiled executable can be found here. 2 3 4 5 6 7 8 Next Home
Logic 101 Logic 101 These lectures cover introductory sentential logic, a method used to draw inferences based off of an argument's premises. Logic is ubiquitous--individuals thinking of pursuing a career in law, computer science, mathematics, or social science must have a firm understanding of basic logic to succeed. Even someone who occasionally programs in Microsoft Excel would benefit greatly. Lectures Prerequisites Logic 101 is the ground floor--there are no prerequisites other than being willing to think through problems. Syllabus This class will cover eight topics: Simple Sentences and OperationsTruth TablesReplacement RulesRules of InferenceProofsConditional ProofsProof by ContradictionFormal Fallacies Additional information Teacher qualifications I am a PhD Candidate at the University of Rochester.
Une introduction aux arbres de décision Les arbres de décision sont l’une des structures de données majeures de l’apprentissage statistique. Leur fonctionnement repose sur des heuristiques qui, tout en satisfaisant l’intuition, donnent des résultats remarquables en pratique (notamment lorsqu’ils sont utilisés en « forêts aléatoires »). Leur structure arborescente les rend également lisibles par un être humain, contrairement à d’autres approches où le prédicteur construit est une « boîte noire ». L’introduction que nous proposons ici décrit les bases de leur fonctionnement tout en apportant quelques justifications théoriques. Suivez le lien pour la version PDF. Table des matières Un arbre de décision modélise une hiérarchie de tests sur les valeurs d’un ensemble de variables appelées attributs. Un ensemble de valeurs pour les différents attributs est appelé une « instance », que l’on note généralement (x, y) où y est la valeur de l’attribut que l’on souhaite prédire et x = x1, …, xm désignent les valeurs des m autres attributs.
Voting Methods 1. The Problem: Who Should be Elected? The central question of this article is: Given a group of people faced with some decision, how should a central authority combine the individual opinions so as to best reflect the “will of the group”? A complete analysis of this question would incorporate a number of different issues ranging from central topics in political philosophy (e.g., how should we define the “will” of the people? I start with a concrete example to illustrate the type of analysis surveyed in this article. For this example, assume that each of the voters has one of four possible rankings of the candidates. Read the table as follows: Each column represents a ranking in which candidates in lower rows are ranked lower. One candidate who, at first sight, seems to be a good choice to win the election is candidate A. Of course, 13 people rank A last, so a much larger group of voters will be unsatisfied with the election of A. Candidate C should win. Candidate B should win. 2. 3. 4.
Learning How to Learn This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. We’ll learn about how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. In-depth Introduction To Machine Learning In 15 Hours Of Expert Videos In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website. Chapter 1: Introduction (slides, playlist) Chapter 2: Statistical Learning (slides, playlist) Interviews (playlist)
Species Counterpoint An Introduction to Species Counterpoint COUNTERPOINT may be briefly defined as the art of combining independent melodies. In figure 1 the lower melody is harmonized by one a third higher - such an arrangement of the voices could be regarded as counterpoint, but there is little or no independence between the parts; for example there is no dissonance between the voices, and both rise and fall together in parallel. However, the arrangement in figure 1a demonstrates much greater independence between its two voices, with movement in one part while the other halts, divergence in the melodic direction of the parts, and with at least some occurrence of dissonance. J.J.Fux's Gradus Ad Parnassum is the classic text dealing with species counterpoint and fugue; it presents a set of rules for writing in the style of 16th century vocal composition (i.e. There are several rules governing melodic movement in the counterpoint; these are common to all five species. First Species An example from Gradus
Introduction to Psychology Syllabus Professor Paul Bloom, Brooks and Suzanne Ragen Professor of Psychology Description What do your dreams mean? Texts Gray, Peter. Requirements Exams: There is a mid-term and a final. Reading Responses: Starting on the third week of class, you will submit a short reading response every week. Book Review: You will write one book review. Experimental participation: All Introductory Psychology students serve as subjects in experiments. Grading Reading responses: 15%Book review: 20%Midterm examination: 30%Final examination: 35% Join a Study Group Through a pilot arrangement with Open Yale Courses, OpenStudy offers tools to participate in online study groups for a selection of Open Yale Courses, including PSYC 110. View study group OpenStudy is not affiliated with Yale University.