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AI: Neural Networks

AI: Neural Networks
About the Course Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples. Recommended Background Programming proficiency in Matlab, Octave or Python. Course Format The class will consist of lecture videos, which are between 5 and 15 minutes in length. Related:  Conceptual & Higher level Math topics

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

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.

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.

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)

Applied Cryptography and Encryption When does the course begin? This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course. How long will the course be available? This class will always be available! How do I know if this course is for me? Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. Can I skip individual videos? Yes! How much does this cost? It’s completely free! What are the rules on collaboration? Collaboration is a great way to learn. Why are there so many questions? Udacity classes are a little different from traditional courses. What should I do while I’m watching the videos? Learn actively!

How to perform a Logistic Regression in R Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values. Logistic regression implementation in R R makes it very easy to fit a logistic regression model. The dataset We’ll be working on the Titanic dataset. The data cleaning process When working with a real dataset we need to take into account the fact that some data might be missing or corrupted, therefore we need to prepare the dataset for our analysis. <- read.csv('train.csv',header=T,na.strings=c("")) Now we need to check for missing values and look how many unique values there are for each variable using the sapply() function which applies the function passed as argument to each column of the dataframe. data <- subset(,select=c(2,3,5,6,7,8,10,12))

Creative Problem Solving About the Course This course will help you understand the role of creativity, innovation, and problem solving in your own life and across disciplines. It will challenge you to move outside of your existing comfort zone and to recognize the value of that exploration. What makes an idea creative, anyway? This course will help you understand the importance of diverse ideas, and to convey that understanding to others. The principal learning activity in the course is a series of "differents" where you will be challenged to identify and change your own cultural, habitual, and normal patterns of behavior. Course Syllabus Introduction: including creativity as an area of study, course methods, and doing something different. Recommended Background No background required, all learners are welcome. In-course Textbooks As a student enrolled in this course, you will have free access to selected chapters and content for the duration of the course. Suggested Readings Johnson, Steven. Lehrer, Jonah.

Introduction to Mathematical Thinking About the Course NOTE: For the Fall 2015 session, the course website will go live at 10:00 AM US-PST on Saturday September 19, two days before the course begins, so you have time to familiarize yourself with the website structure, watch some short introductory videos, and look at some preliminary material. The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. The course is offered in two versions. Course Syllabus Instructor’s welcome and introduction 1. 2. 3. 4. 5. 6. 7. 8. 9. Recommended Background High school mathematics. Suggested Readings There is one reading assignment at the start, providing some motivational background. There is a supplemental reading unit describing elementary set theory for students who are not familiar with the material.

Logic: Language and Information 1 About the Course Information is everywhere: in our words and our world, our thoughts and our theories, our devices and our databases. Logic is the study of that information: the features it has, how it’s represented, and how we can manipulate it. Learning logic helps you formulate and answer many different questions about information: Does this hypothesis clash with the evidence we have or is it consistent with the evidence? If you take this subject, you will learn how to use the core tools in logic: the idea of a formal language, which gives us a way to talk about logical structure; and we'll introduce and explain the central logical concepts such as consistency and validity; models; and proofs. Course Syllabus Week 1. Week 2. Weeks 3–5. Electronic Engineering — simplifying digital circuitsPhilosophy — vagueness and borderline casesComputer Science — databases, resolution and propositional PrologLinguistics — meaning: implication vs implicature Recommended Background Suggested Readings