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Data Science 202 - Ensemble Methods

Background

Ensemble methods are generally agreed to be the closest thing to a cure-all in machine learning.  Google uses ensemble methods for determining what ads to place on pages and eBay uses them for determining what search results to return.  Ensemble methods combine a large number of mediocre machine learning algorithms into a single answer.  When done properly this results in a algorithm that resists over-training and that delivers top performance on a wide variety of problems - winning a majority of Kaggle competitions for example. 

This class will start with the basics of trees and will cover the background, usage, strengths and weaknesses of the major ensemble algorithms.  By the end of class attendees will understand when these algorithms are applicable and how to get the best performance from them.  The class will meet for 4 hours on 4  Saturdays.  Here are the topics we'll cover. 

Week   Topic
1.        Decision Trees, Boosting
2.        Gradient Boosting
3.        Random Forests
4.        Topics of Interest in Machine Learning (e.g. active learning, low-rank matrix approx for recommender, expectation maximization algo)

If attendees are interested we can schedule a session for projects or machine learning competition.

The class is intended for computer programmers.  No prior knowledge of machine learning is assumed.  The course will primarily use R statistical language.  There will be a separate review of R language for those requiring it.  The class will include derivations that require undergrad level math - calculus and linear algebra.

Class Schedule
Sept 14, Sept 21, Sept 28, Oct 5 - One payment covers all 4 sessions.

Early Bird Registration

There's a $100 discount if you register and pay 5 days before class starts.  You can pay through Eventbrite datascience202.eventbrite.com or through paypal (mike at mbowles dot com).  

Attend by web conference

Sessions will be webcast.  If you'd like to attend by webcast, be sure to sign up at least 12 hours before the start of class in order to receive instructions and passwords.


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  • Bruno

    Is the price correct for the single session?

    September 20, 2013

  • Joey B.

    Will a recording of the webcast be available for viewing anytime? Or do I have to watch it live? Thanks.

    September 11, 2013

    • Joey B.

      Thanks, I signed up and intend to view the recording.

      September 12, 2013

    • Mike B.

      Okay. If you have any problem viewing or questions about the material, send me an email or we can set up a phone call to make sure they get answered.

      September 12, 2013

  • Mike B.

    Even though we're past the limit of 20, there will be plenty of room. Don't be discouraged if you're on the waiting list. Just come on by. There will be room for you.

    September 5, 2013

  • Bharathram L.

    So confused..What is the actual cost?

    August 12, 2013

    • Mike B.

      i fixed the pricing details. $300 early bird, $400 general admission

      August 12, 2013

  • Gowtham N.

    What is the cost to attend? $10? But it also says we will get $100 discount at the end?

    August 12, 2013

Your organizer's refund policy for Data Science 202 - Ensemble Methods

Refunds offered if:

  • the Meetup is cancelled
  • you can cancel at least 5 day(s) before the Meetup

Payments you make go to the organizer, not to Meetup. You must make refund requests to the organizer.

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