Weka is open-source machine learning software issued under the GNU General Public License.

WEKA’s third interface, the Experimenter, is designed to help you answer a basic practical question when applying classification and regression techniques: Which methods and parameter values work best for the given problem?

Scripting Weka in Python: the Jython package and the Python Weka wrapper Applications: analyzing soil samples, neuroimaging with functional MRI data, classifying tweets and images, signal peptide prediction

Class 1 Exploring Weka’s interfaces; working with big data Class 2 Discretization and text classification Class 3 Classification rules, association rules, and clustering Class 4 Selecting attributes and counting the cost Class 5 Neural networks, learning curves, and performance optimization Lesson 3.1 Decision trees and rules

Getting started with Weka Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Class 5 Putting it all together Lesson 4.1 Classification boundaries Lesson 4.2 Linear regression Lesson 4.3 Classification by regression Lesson 4.4 Logistic regression Lesson 4.5 Support vector machines Lesson 4.6 Ensemble learning

We present a brief account of the WEKA 3 software, which is distributed under the GNU General Public License, followed by some lessons learned over the period spanning its development and maintenance. We also include a brief historical mention of its predecessors.

More Data Mining with Weka This course assumes that you know about – What data mining is and why it’s useful – The “simplicity-first” paradigm – Installing Weka and using the Explorer interface – Some popular classifier algorithms and filter methods – Using classifiers and filters in Weka … and how to find out more about them

Recall Support Vector Machines (Data Mining with Weka, lesson 4.5) – also restricted to linear decision boundaries – but can get more complex boundaries with the “Kernel trick” (not explained)

Newton method using BFGS updates implemented in WEKA’s Optimization class, and a non-linear conjugate gradient descent method, implemented as a new ConjugateGradientDescentOptimization subclass of the Optimization class. This new class implements the hybrid conjugate gradient descent method speci ed by Equation 1.25 in …

• the open-source Weka data mining platform • access to chapters from Data Mining (3rd Edition) • online assessment leading to a completion certificate