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Learning From Data - Online Course (MOOC)

Learning From Data - Online Course (MOOC)
A real Caltech course, not a watered-down version Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible?

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Professor TL McCluskey - Profile Vallati, M., Hutter, F., Chrpa, L. and McCluskey, T. (2015) ‘On the Effective Configuration of Planning Domain Models’. In: International Joint Conference on Artificial Intelligence, 25th - 31st July, 2015, Argentina Chrpa, L., Vallati, M. and McCluskey, T. (2015) ‘On the Online Generation of Effective Macro-operators’. In: International Joint Conference on Artificial Intelligence (IJCAI) 2015, 25th - 31st July, 2015, Buenos Aires, Argentina Mohammad, R., Thabtah, F. and McCluskey, T. (2015) Phishing Websites Dataset [Dataset] Chrpa, L., McCluskey, T. and Osborne, H. (2015) ‘On the Completeness of Replacing Primitive Actions with Macro-actions and its Generalization to Planning Operators and Macro-operators’ AI Communications .

Deep Learning Tutorial - In the spring quarter of 2015, I gave an entire class at Stanford on deep learning for natural language processing. If you're interested in all the details of these methods and applications, see Slides Updated Version of Tutorial at NAACL 2013 See Videos Apple's Start Developing iOS Apps Today Guide Is a Roadmap for Creating Your First App I'm pretty decent at programming iOS apps now. I came from a background in VB and JAVA. I had a nasty learning curve with getting used to Obj C. A couple resources I used when starting up were: Querying with SPARQL Home > User Guide > Querying with SPARQL SPARQL is the standard query language for the Semantic Web and can be used to query over large volumes of RDF data. dotNetRDF provides support for querying both over local in-memory data using it's own SPARQL implementation and for querying remote data using SPARQL endpoints or through other stores SPARQL implementations. If you want to learn about SPARQL you should take a look at the SPARQL Query Language Specification which provides examples of all the various query forms as well as the full formal specifcation. Advanced Users may want to take a look at the Advanced SPARQL and SPARQL Optimization pages for more details about how our in-memory SPARQL engine functions.

Introducing PredictionIO PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. Building a production-grade engine to predict users’ preferences and personalize content for them used to be time-consuming. Not anymore with PredictionIO’s latest v0.7 release. We are going to show you how PredictionIO streamlines the data process and make it friendly for developers and production deployment. A movie recommendation case will be used for illustration purpose. login required By logging in to this service, you are agreeing to the following Regulations and Policies. If you do not understand any of the Regulations or Policies, or if you do not agree to fully abide by them, then you should not use this service. Specifically, you must ensure that you keep your login details confidential and you may not allow any third party to use your account. All login attempts are monitored and all suspicious account activity is investigated. In extreme circumstances, your network account may be disabled. If you are using the University of Huddersfield's network to use this service, then you are also agreeing to abide by the JANET Acceptable Use Policy.

UFLDL Tutorial - Ufldl From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first. Sparse Autoencoder

Code Hero raises over $100,000 for shooter that teaches computer programming Over the years, there have been a lot of efforts to create games that make learning how to program a computer simple and fun, with widely variable results. But indie developer Primer Labs seems to have hit on something special with Code Hero, a first-person shooter that teaches JavaScript and UnityScript programming by letting players fire bits of code that actually affect the environment. The group recently reached its Kickstarter funding goal of $100,000 for the project, and is still looking for last-minute donations to help fund a multiplayer mode.

A Graph-Based Movie Recommender Engine « Marko A. Rodriguez The MovieRatings Dataset The GroupLens research group has made available a corpus of movie ratings. There are 3 versions of this dataset: 100 thousand, 1 million, and 10 million ratings.

Related:  Machine LearningMachine Learningmachine learning