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CS 790Q
- Biometrics
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The main objective of the course is to introduce you to the area of Biometrics. Biometrics refers to the identification of an individual based on his/her physiological characteristics, like a fingerprint, face, voice or behavior like handwriting or keystroke patterns. Because biometric characteristics are unique to each individual, they can be used to prevent theft of fraud. In addition, unlike a password or a PIN, a biometric cannot be lost, stolen, or recreated. This course is primarily intended for **highly motivated** students who are interested in working on the area of Biometrics. There are many problems in this area suitable for investigation by graduate students leading to a master thesis or dissertation.
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EE
484/684 - Digital Signal Processing
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Discrete signals and systems, the Z transform, digital filter design
techniques, the Fast Fourier Transform, modeling, analysis, and simulation
of discrete random signals and systems.
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CS
474/674 - Image Processing and Interpretation
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Image files, thresholding, histogram transformation, spectra, connectedness, edges, filtering, detection and recognition of objects, optical character recognition.
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CS
485/685 - Computer Vision
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Principles, design and implementation of vision-based systems. Camera models and
image formation, feature detection, segmentation. Camera calibration, 3-D
reconstruction, stereo vision. Introduction to advanced topics.
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CS
486/686 - Advanced Computer Vision
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Projective geometry, 3-D reconstruction from multiple views. Motion analysis and
tracking. Object, face and gesture recognition, biometrics, human-computer
interaction. Image and video understanding.
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CS
491Y/791Y - Mathematical Methods for Computer Vision
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Computer Vision is a broad-based field of computer science that requires
students to understand and integrate knowledge from numerous disciplines.
Students are expected to strong background in calculus, linear algebra,
probabilities, statistics, geometry, and algorithms. Scientists engaged in
this field inevitably have to learn many things about a discipline in
which they did not receive formal training. The purpose of this course is
to help bridge the gap for more students to do research in computer
vision by teaching them some of the most important
and essential mathematical tools used in computer vision research. Our
goal is to emphasize those mathematical techniques which have been
extensively evaluated and demonstrated to be useful in practical
applications. Our emphasis will be on the application aspect of these
mathematical techniques in computer vision.
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CS
773 - Machine Intelligence
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(a) Intelligent systems, (b) neural computing, (c) advanced applications. Self-organizing, self-adapting systems; cybernetics; neural networks; automated decision making and control; learning automata; expert systems application; knowledge and data engineering; pattern recognition, image processing.
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CS
790Q - Machine Learning
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Supervised and self-organizing learning systems, feature processing, clustering as self-organization, agglomerative versus decisive processes, components of a pattern recognition (PR) system, feature vectors, competitive learning, fuzzy competitive learning, Bayesian classification and linear discrimination, weighting features, non-Euclidean distances between feature vectors, k-means clustering (Forgy versus MacQueen), improved k-means clustering, fuzzy c-means clustering, other fuzzy clustering, fuzzy merging of clusters, clustering validity measures, graphical methods for forming clusters, radial basis function neural networks for supervised learning, automata and recognizers, data mining via clustering: discovering rules in data.
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CS
491S/791S - Neural Networks
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Neural networks provide a model of computation drastically different from
traditional computers. Typically, neural networks are not explicitly
programmed to perform a given task; rather, they learn to do the task from
examples of desired input/output behavior. The networks automatically
generalize their processing knowledge into previously unseen situations,
and they perform well even when the input is noisy, incomplete or
inaccurate. These properties are well-suited for modeling tasks in
ill-structured domains such as face recognition, speech recognition and
motor control.
This course covers basic neural network architectures and learning
algorithms, for applications in pattern recognition, image processing, and
computer vision. Three forms of learning will be introduced (i.e.,
supervised, unsupervised and reinforcement learning) and applications of
these are discussed. Students have a chance to try out
several of these models on practical problems.
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CS
480/680 - Computer Graphics
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Software, hardware and mathematical tools for the representation, manipulation and display of two- and threedimensional objects: applications of these tools to specific problems.
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CS
479/679 - Pattern Recognition
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Pattern recognition systems, statistical methods, discrimination functions, clustering analysis, unsupervised learning, feature extraction and feature processing.
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CS
476/676 - Artificial Intelligence
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Problem solving, search, and game trees. Knowledge representation, inference, and rule-based systems. Semantic networks, frames, and planning. Introduction to machine learning, neural-nets, and genetic algorithms.
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CS/Math
483/683 - Numerical Methods I
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Numerical solution of linear systems, including linear programming; iterative solutions of non-linear equations; computation of eigenvectors, matrix diagonalization.
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CS/Math
484/684 - Numerical Methods II
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Numerical differentiation and integration; numerical solution of ordinary differential equations, two point boundary value problems; difference methods for partial differential equations.
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