Tuesday, 6 November 2012

Introduction to Support Vector Machines In R

Support vector machines (SVM's) have become popular among data miners, being especially suited for extreme data intensive tasks like image classification, biosequence processing, handwriting recognition, etc., but less prone to over fitting than other machine learning methods.  Dr. Lutz Hamel, author of "Knowledge Discovery with Support Vector Machines",  presents his online course "Introduction to Support Vector Machines In R" at Statistics.com. For more details please visit at http://www.statistics.com/SVM/.

Support vector machines (SVMs) have established themselves as one of the preeminent machine learning models for classification and regression over the past decade or so, frequently outperforming artificial neural networks in task such as text mining and bioinformatics.

"Support Vector Machines in R" teaches you what is going on "under the hood" when you use SVM's.  After completing this course, you will be able to interpret the performance of SVM models, choose model parameters well during the model evaluation and selection cycle, know how linear, polynomial, and Gaussian kernels differ, and know how to tune their parameters. In addition, you will gain a deep understanding of how the cost constant "C" affects the quality of your models.

The course is based on the R statistical computing environment.  However, the knowledge gained here is easily transferred to other knowledge discovery environments.

Who Should Take This Course:
Statisticians and data miners who need to know a variety of methods for classification.

Course Program:

Course outline: The course is structured as follows

SESSION 1: The Foundations
  • What is Knowledge Discovery?
  • Describing Data Mathematically
  • Linear Decision Surfaces and Functions
  • Perceptron Learning
    • Duality
  • Maximum Margin Classifiers
    • Quadratic Programming

SESSION 2: Support Vector Machines
  • The Lagrangian Dual
  • Dual Maximum Margin Optimization
  • Linear/Non-Linear SVMs
    • "The Kernel Trick"
  • Soft-margin Classifiers

SESSION 3: Model Evaluation and Selection
  • Performance metrics
    • the Confusion Matrix
  • Model Evaluation
    • Hold-out
    • Leave-one-out
    • N-fold Cross-validation
  • Confidence Intervals
  • Elements of Statistical Learning Theory
    • the VC-dimension
    • Empirical Risk Minimization
    • VC-confidence
    • Structural Risk Minimization

SESSION 4: Extensions to the Basic Model
  • Multi-class Classification
    • One-versus-the-rest Classification
    • Pairwise Classification
  • Regression with SVMs
    • Regression with Maximum Margin Machines
    • Regression with Support Vector Machines
    • Model Evaluation

Dr. Lutz Hamel teaches at the University of Rhode Island and founded the machine learning and data mining group there. Prior to his academic post, Dr. Hamel was Director of Software Development at Thinking Machine Corporation, and Vice President of R&D for Bluestreak, where he oversaw the development of advanced technologies for online ad delivery and optimization, and directed the building of a next generation data warehouse-driven system for campaign analysis and design tools.  Participants can ask questions and exchange comments with Dr. Hamel via a private discussion board throughout the course.

You will be able to ask questions and exchange comments with Dr. Lutz Hamel via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at www.india.statistics.com.

For More details contact at
Call: 020 66009116


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