Here we train different machine learning algorithms with R for extreme imbalanced classification.
ITI43210 Machine Learning, Østfold University College
Machine learning is about computers learning through training and experience instead of being explicitely programmed for a given task.The students will get acquainted with several methods and algorithms for machine learning. Based on this, the students should be able to select the methods best suited for the problem in question.
The course should give the students knowledge about the basic properties common to all machine learning methods. Examples include ability to generalise and heuristic search.
The course contains three projects, one about decision trees, rules and regression analysis, one about neural networks, and one about evolutionary computation.
Induction of decision trees and some applications such as medical diagnosis and credit evaluation.
Artificial neural nets and optimization algorithms such as steepest descent and trust region Newton methods. Applications of neural nets to sound and image analysis.
Basic theory for machine learning, for example Bayes' formula, maximum likelihood and the minimum description length principle.
Instance based learning such as nearest neighbour, locally weighted regression, and radial basis functions.
Evolutionary computation, especially genetic algorithms and genetic programming. General principles for evolution. Selection methods and genetic operators such as mutation and crossover. The Baldwin effect.
Some of the topics above require basic knowledge of statistics and information theory which will be taught as needed.