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.
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 More details
contact at
Call: 020
66009116
Websites:
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