1. Knowledge-Based AI: Cognitive Systems | Udacity


    The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition.

  2. CS 541: Artificial Intelligence Planning


    CS 541: Artificial Intelligence Planning. Instructors: Jim Blythe, Jose-Luis Ambite, and Yolanda Gil. Time: Tuesday and Thursday, 3:30 - 4:50 PM

  3. Java: Learning to Program with Robots


    Java: Learning to Program with Robots. What is Robots? Itís a textbook. Itís software. Itís a gentle way to teach object-oriented programming that really is ...

  4. R Programming A-Zô: Download Practice Datasets ...


    Greetings Welcome to the data repository for the R Programming Course by Kirill Eremenko. The datasets and other supplementary materials are below. Enjoy! Section 1.

  5. Wolfram Alpha Is Making It Extremely Easy for Students to ...


    Teachers are being forced to adapt to Wolfram Alpha, which executes homework perfectly and whose use almost impossible to detect.

  6. JMAP HOME - Math Regents Exams Algebra I, Geometry ...


    JMAP offers teachers and other users of the Common Core State Standards free resources that simplify the integration of Regents exam questions into their curriculum.

  7. Dietary Reference Intakes (DRI)_041516 - Course Hero

    www.coursehero.com › Rasmussen College, Minneapolis MN

    What are the three components of the DRI? RDA (Recommended Dietary Allowance), AI (Adequate Intake), UL (Tolerable Upper Intake Level) Describe how RDA, AI, and UL ...

  8. Create a Canvas Account


    Watch a Video. Sign up now, it's free! I'm a Teacher I'm a Student Parents sign up here. © 2017 Instructure

  9. CS229 Machine Learning | Stanford Center for Ö


    Overview. Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking.

  10. CS 229: Machine Learning (Course handouts)


    Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.