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Thread: Miscellaneous

  1. #21
    Article "Teaching machines to predict the future"
    Deep-learning vision system from the Computer Science and Artificial Intelligence Lab anticipates human interactions using videos of TV shows.

    by Adam Conner-Simons, Rachel Gordon
    June 21, 2016

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  5. #25
    Article "To supervise or not to supervise in AI?"
    If you look carefully at how humans learn, you see surprisingly little unsupervised learning.

    by Mike Loukides
    July 11, 2016

  6. #26

  7. #27


    ODSC East 2016 | Rahul Dave - "Machine Learning for Suits"

    Published on Jul 14, 2016

    Abstract: You will learn the basic concepts of machine learning – such as Modeling, Model Selection, Loss or Profit, overfitting, and validation – in a non-mathematical way, so that you can ask for data analysis and interpret the results of a model in the context of making business decisions. The concepts behind machine learning are actually quite simple, so expect to take away not just words and acronyms, but rather, a deep understanding. We will work in the context of concrete examples from different domains, including finance and medicine.

    1. What is probability? What is a model? Supervised vs unsupervised learning. Regression and Classification. Minimizing Cost and Maximizing likelihood.

    2. Models and Data: Bias, Variance, Noise, Overfitting, and how to solve Overfitting with Regularization and Validation

    3. Different kinds of models, including ensembles and deep learning.

    4. How good is a model? Profit Curves, ROC curves, and the expected value formalism.

    Bio: Rahul Dave is a lecturer at Harvard University and partner at LxPrior, a small Data Science consultancy. LxPrior offers its clients data analysis services as well as data science training. Rahul trained as an astrophysicist, doing research on dark energy, and worked at the University of Pennsylvania, NASA’s Astrophysics Data System, as well as at Harvard University. As a computational scientist, he has developed time series databases, semantic search engines, and techniques for classifying astronomical objects. He was one of the people behind Harvard’s Data Science course CS109, and Harvard Library’s Data Science Training For Librarians course. This year he is teaching courses in computer science and stochastic methods to scientists and engineers.

  8. #28


    AI, Deep Learning, and Machine Learning: A Primer

    "One person, in a literal garage, building a self-driving car." That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
    Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple "A.I. winters"? What's the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?
    From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.

  9. #29
    Article "How to Steal an AI"

    by Andy Greenberg
    September 30, 2016

  10. #30

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