Introduction to Support Vector Machines in R

taught by Lutz Hamel

Brief Description:

This course begins by developing basic concepts such as hyperplanes, features spaces and kernels; then it covers the development of support vector machines (SVM's).

Instructor(s):
Level: Advanced

Who Should Take This Course:

Statisticians and data miners who need to know a variety of methods for classification.

Dates:
November 16, 2012 to December 14, 2012
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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Introduction to Support Vector Machines in R

taught by Lutz Hamel

Aim of Course:

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.

The aim of this course is to give you an understanding on what is going on "under the hood" when using SVMs. After completing this course, you will be able to interpret the performance of SVM models and make appropriate choices for model parameters during the model evaluation and selection cycle. You will understand the difference between linear, polynomial, and gaussian kernels and know how to tune their parameters. In addition, you will have a deep understanding on 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.

Prerequisite(s):

The material covered in these courses is a prerequisite:

• Calculus
• Matrix Algebra
• Introduction to R - Data Handling

Some familiarity with the predictive modeling paradigm will also be helpful, (such as presented in Introduction to Predictive Modeling.)
Course Program:

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

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

Organization of the Course:

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The required text for this course is Knowledge Discovery with Support Vector Machines, by Lutz Hamel, and it can be ordered from Wiley by clicking here. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

You must have a copy of R for the course. Click here for information on obtaining a free copy.

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Introduction to Support Vector Machines in R

taught by Lutz Hamel

Instructor(s):
Dates:
November 16, 2012 to December 14, 2012
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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