Classroom - Udacity. Bayesian statistics: a comprehensive course. This playlist provides a complete introduction to the field of Bayesian statistics.
Big Data University: Login to the site. Learning Site - Couchbase. Nuclear Science and Engineering. Learn how to use Illustrator CC. Optimization Algorithms in Machine Learning. Optimization provides a valuable framework for thinking about, formulating, and solving many problems in machine learning.
Since specialized techniques for the quadratic programming problem arising in support vector classification were developed in the 1990s, there has been more and more cross-fertilization between optimization and machine learning, with the large size and computational demands of machine learning applications driving much recent algorithmic research in optimization. This tutorial reviews the major computational paradigms in machine learning that are amenable to optimization algorithms, then discusses the algorithmic tools that are being brought to bear on such applications.
We focus particularly on such algorithmic tools of recent interest as stochastic and incremental gradient methods, online optimization, augmented Lagrangian methods, and the various tools that have been applied recently in sparse and regularized optimization. Lecture Videos and Slides. Learn how to use Audition CC. Paul Nissenson. Learn Photoshop, get help and support. Introducing Bundler Ruby on Rails 4. HyperSTV Creative Cloud. Adobe Creative Cloud is a software as a service offering from Adobe Systems that gives users access to a collection of software developed by Adobe for graphic design, video editing, web development, photography, and cloud services.
In Creative Cloud, a monthly or annual subscription service is delivered over the Internet. Software from Creative Cloud is downloaded from the Internet, installed directly on a local PC and used as long as the subscription remains valid. Online updates and multiple languages are included in the CC subscription. Creative Cloud gives you the world’s best creative tools, always up to date. SQL Lite Database: Android Programming. Data Binding - an easier way to connect android UI. Android: Populating a ListView from the SQLite Database. Make a Custom ListView through Database in android. Android SQLite Database Tutorial 2 # Introduction + Creating Database and Tables (Part 2) How to use Github. Android SQLite Tutorial - 1 - Create database and tables. Udacity/Sunshine-Version-2.
UX Design. Android Apps. Learn Android Programming From Scratch - Beta. The course provide an introduction to Android Programming and allows someone with a basic knowledge of programming to start creating Android Applications.
It is a light course to cover fundamentals of Android. It will teach you the Android programming Paradigm and how to think while creating an Android program. We will cover topics such as Installation, Activities, Layouts, List Views, SQLite, Services Multimedia and Google Play. The course is divided into 6 units covering each of the above topics. You will start with basic installation process and will move on to the first Android example which will outline the structure of Android Programs. It will be a fun learning course that is sure to help you get going with Android programming.
Logic. Android for beginners. Android Studio App Development Tutorials. Undergraduate Course on Design and Analysis of Algorithms - UC Davis, Computer Science - Dan Gusfield. ECS 222A - Graduate Level Design and Analysis of Efficient Computer Algorithms - Mostly from Fall 2007 - Gusfield. 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. School math typically focuses on learning procedures to solve highly stereotyped problems.
Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The course is offered in two versions. AWS re:Invent 2015. Getting Started with Amazon Web Services. How to learn math. Coursera - Algorithms.
Coursera - Machine Learning. About this course: Machine learning is the science of getting computers to act without being explicitly programmed.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. CS 229: Machine Learning. Undergraduate machine learning 1: Introduction to machine learning. Bayesian statistics made (as) simple (as possible)
Classroom - Udacity. Stanford NLP. Statistical Learning. About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods.
The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013).