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Machine Learning: Hands-On Introduction, Oct 18-19


Requiring no previous background in machine learning, this two-day seminar is a hands-on introduction to the field as well as to the R statistical environment that will be used to illustrate the machine learning concepts.



Statistical HorizonsMachine Learning:
A Hands-On Introduction

A 2-Day Seminar Taught by Adi Tarca, Ph.D.

Oct 18-19, Philadelphia, PA

Classification of objects into predefined groups is a common task that humans do every day, and machines are also getting better at it, with applications ranging from character and speech recognition to distinguishing between spam and non-spam messages, medical diagnosis, stock market prediction, etc. Output prediction or supervised machine learning refers to the task of using the information embedded in a set of input variables (X1, X2, …, Xp) to make predictions about an outcome variable Y (e.g. binary, multinomial, continuous) by learning from a training dataset consisting of observed input-output pairs (samples). The learning is to be achieved by a computer/machine algorithm that uses the training dataset to estimate/tune its internal parameters with minimal input from the user.

An example of a machine learning application is fitting a logistic regression model in which the binary outcome represents the voting preference (Republican vs. Democrat) of a set of individuals (samples) based on a set of features (e.g. age, gender and income), with the goal of predicting the voting preference of a new set of individuals. Although the methods of machine learning are often similar to those of statistical hypothesis testing, the goals and the pitfalls are quite different.

Requiring no previous background in machine learning, this two-day seminar is a hands-on introduction to the field as well as to the R statistical environment that will be used to illustrate the machine learning concepts.

The course is organized as a sequence of lectures and practical sessions on both machine learning and the R statistical language. The machine learning issues we will focus on include:

  • When, why and how to use the different types of prediction models such as logistic regression, quadratic and linear discriminants, neural networks, support vector machines, decision trees and random forests, k-nearest neighbor.
  • How to select the best set of features for a particular type of model.
  • How to assess the model performance in an unbiased way and avoid overfitting.

The emphasis in the course will be on concepts and their practical use rather than on the mathematical theory behind them. At the end of the course the participants will be able to use R to (1) load a data set from a file, (2) prepare the data for analysis, (3) build and tune an appropriate classifier, (4) assess the future performance of the classifier on new data, and (5) apply the classifier on a new dataset to predict the outcome.

To register or learn more, visit

www.statisticalhorizons.com/seminars/public-seminars/machine-learning-2013


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