Machine Learning
Professor Haralick has made a series of contributions in the field of computer vision. In the highlevel vision area, he has worked on inferring 3D geometry from one or more perspective projection views.] He has also identified a variety of vision problems which are special cases of the consistent labeling problem. His papers on consistent labeling, arrangements, relation homomorphism, matching, and tree search translate some specific computer vision problems to the more general combinatorial consistent labeling problem and then discuss the theory of the lookahead operators that speed up the tree search. The most basic of these is called Forward Checking. This gives a framework for the control structure required in highlevel vision problems. He has also extended the forwardchecking tree search technique to propositional logic.
In the lowand midlevel areas, Professor Haralick has worked in image texture analysis using spatial gray tone cooccurrence texture features. These features have been used with success on biological cell images, xray images, satellite images, aerial images and many other kinds of images taken at small and large scales. In the feature detection area, Professor Haralick has developed the facet model for image processing. The facet model states that many lowlevel image processing operations can be interpreted relative to what the processing does to the estimated underlying gray tone intensity surface of which the given image is a sampled noisy version. The facet papers develop techniques for edge detection, line detection, noise removal, peak and pit detection, as well as a variety of other topographic gray tone surface features. For shape analysis and extraction he developed the techniques of mathematical morphology, including the mathematical morphology sampling theorem and recursive morphological operations.
His most recent work is in the machine learning area, particularly in the manifold clustering of high dimensional data sets, the application of pattern recognition to mathematical combinatorial problems. He is current work is in the learning of knowledge and structure through relation decomposition.
Description
Machine learning is a branch of artificial intelligence, concerned with the construction and study of systems that can learn from data. Data may be numeric or symbolic and typically has the form of an Ntuple. The anthropomorphic term learning in the machine learning context means being able to predict some unobserved components of an Ntuple given some observed components of the Ntuple. This course provides a detailed explanation of many of the techniques used in machine learning and statistical pattern recognition.
PreRequisites
Students are assumed to have learned the concepts taught in basic courses in probability, statistics, linear algebra, and articial intelligence.
Course Objectives
The course objectives are to enable the student to take a real world machine learning problem and

Identify A Suitable Algorithm for Making the Required Prediction

Design an Experimental Protocol for Making an Unbiased Estimate of the Performance of the Algorithm

Program the Solution

Validate that the Program Works

Carry out the Experiment
Learning Objectives
To achieve these objectives the student must be able to demonstrate a working knowledge of the theoretical foundations of machine learning represented by the topics of
Course Topics

Bayesian Classication

Minimax Classication

NonParametric Probability Models

Parametric Probability Models

Making Decisions in Context

Graphical Models

Decision Trees

Nearest Neighbor

Linear Regression

Principal Component Analysis

Logistic Regression

Neural Networks

Linear Decision Rules

Clustering

Experimental Protocols
Assessment
Grades will be based on