The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive course on the subject, covering a broad array of topics not usually included in introductory machine learning books. In order to present a unified treatment of machine learning problems and solutions, including pattern recognition and data mining.
The course will introduce the basics of fuzzy logic for data analysis. Fuzzy Logic can be used to model and deal with imprecise information, such as inexact measurements or available expert knowledge in the form of verbal descriptions. We will first introduce the concepts of fuzzy sets, degrees of membership and fuzzy set operators. After discussions on fuzzy numbers and arithmetic operations using them, the focus will shift to fuzzy rules and how such systems of rules can be derived from available data. I will lecture on three major topics: Reasoning under uncertainity and fuzzy logic; fuzzy control; and learning in fuzzy systems.
Our goal is to introduce students to a powerful class of model, the Neural Network. In fact, this is a broad term which includes many diverse models and approaches. We will first motivate networks by analogy to the brain. The analogy is loose, but serves to introduce the idea of parallel and distributed computation. We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. We discuss model architectures, training methods and data representation issues. We use GMDH Forecasting Software
Support vector machines (SVM) and kernel methods are important machine learning techniques. In this course, we will introduce their basic concepts. We then focus on the training and optimization procedures of SVM. Examples demonstrating the practical use of SVM will also be discussed. Basically we focus on binary and multiclass classification. We will study in particular support vector machines (SVM) and kernels, as well as feature selection techniques including SVRegression (epsilon-SVR & nu-SVR).