Intelligent Informatics

Intelligent Informatics

EECS Associate Professor James Miller and EECS student David Wolfe develop interactive educational tools. Prof. Miller helped lead interdisciplinary research to create a virtual environment that taught social skills to students with Autism Spectrum Disorders.

EECS researchers are developing new tools and methods in data collection, integration, cleansing, and representation. In addition to innovative data gathering and management, EECS researchers advance all aspects of modeling, including model construction, selection, averaging, evaluation, and interpretation. The multidisciplinary research identifies and deploys intelligent informatics components for efficient solutions to real-world problems in medicine, science, industry, and other numerous other fields.

Associated Disciplines

Associated Programs

Professor
785-864-8821
1C Eaton Hall

Primary Research Interests

  • Intelligent Systems
  • Robotics
  • Medical Applications of Artificial Intelligence
  • Software Engineering
785-864-4488
3014 Eaton Hall

Primary Research Interests

  • Knowledge Discovery
  • Data Mining
  • Machine Learning
  • Expert Systems
  • Reasoning Under Uncertainty
Associate Professor
785-864-7389
2038 Eaton Hall

Primary Research Interests

  • Algorithm Design and Analysis
  • Combinatorial Optimizations
  • Graph Algorithms
Associate Professor
785-864-7393
2044 Eaton Hall

Primary Research Interests

  • Information security and privacy, database security
  • Information retrieval, Web and online social networks
  • Security and privacy issues in smart grid systems
  • XML and conventional database systems, data management
Associate Professor
785-864-7384
2036 Eaton Hall

Primary Research Interests

  • Scientific and Information Visualization
  • Visual Analytics
  • Geometric Modeling
  • Technology in Education
Associate Professor
785-864-8816
3016 Eaton Hall

Primary Research Interests

  • High Performance Scientific Computing Algorithms
  • Parallel Unstructured Mesh and Optimization Algorithms
  • Model Order Reduction
  • Computational Medicine
  • Image Processing
Assistant Professor
785-864-8800
3012 Eaton Hall

Primary Research Interests

  • Computer vision
  • Image processing
  • Pattern recognition
  • Artificial intelligence
  • Robotics

Associated Facilities

Multiagent development tools:

  • ACCS, C++, CORBA, Java

Information retrieval and Web tools:

  • KUIR Information Retrieval Library, Php, XMLSpy, MySQL, Perl

Data Mining Tools:

  • SNOB, Cobweb, ID3, C4.5, statistical analysis packages

Artificial intelligence development tools and languages:

  • Lisp, CLOS, CLIPS, Prolog, GBB, OPS, MEM-1

Image processing and computer vision tools:

  • KUIM Image Processing Library, high-speed video, and data cable/fiber link

Human-intelligent system interaction tools:

  • Mobile robots, VR user interface, head-mounted display, force feedback joysticks
  • PoepleBot, two Nomad Scouts, three Kheperas, and one Pioneer robot

Software packages for virtual prototyping and kinematics and dynamics modeling, such as the visualNastran 4D and Working Model.

Program Objectives

  • Understand fundamental principles and algorithms of intelligent informatics.
  • Understand the data collection, data integration, data cleansing, data representation, model construction, model selection, model averaging, model evaluation, and model interpretation components in intelligent informatics.
  • Understand how to identify intelligent informatics components and provide efficient solutions in solving real-world problems.
  • Have the ability to effectively communicate to impact technological decisions.

Core Coursework (MS)

EECS 738 Machine Learning
"Machine learning is the study of computer algorithms that improve automatically through experience" (Tom Mitchell). This course introduces basic concepts and algorithms in machine learning. A variety of topics such as Bayesian decision theory, dimensionality reduction, clustering, neural networks, hidden Markov models, combining multiple learners, reinforcement learning, Bayesian learning etc. will be covered. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Kuehnhausen, Martin
M 05:15-07:45 PM LEA 2112 - LAWRENCE
3 75477
EECS 741 Computer Vision
This course gives a hands-on introduction to the fundamentals of computer vision. Topics include: image formation, edge detection, image segmentation, line-drawing interpretation, shape from shading, texture analysis, stereo imaging, motion analysis, shape representation, object recognition. Prerequisite: EECS 672 or EECS 744. LEC.

The class is not offered for the Spring 2019 semester.

EECS 767 Information Retrieval
This class introduces algorithms and applications for retrieving information from large document repositories, including the Web. Topics span from classic information retrieval methods for text documents and databases, to recent developments in Web search, including: text algorithms, indexing, probabilistic modeling, performance evaluation, web structures, link analysis, multimedia information retrieval, social network analysis. Prerequisite: EECS 647 or permission of instructor. LEC.

The class is not offered for the Spring 2019 semester.

EECS 837 Data Mining
Extracting data from data bases to data warehouses. Preprocessing of data: handling incomplete, uncertain, and vague data sets. Discretization methods. Methodology of learning from examples: rules of generalization, control strategies. Typical learning systems: ID3, AQ, C4.5, and LERS. Validation of knowledge. Visualization of knowledge bases. Data mining under uncertainty, using approaches based on probability theory, fuzzy set theory, and rough set theory. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.

The class is not offered for the Spring 2019 semester.

Elective Coursework (MS)

EECS 638 Fundamentals of Expert Systems
Basic information about expert systems: architecture of an expert system, building expert systems, uncertainty in expert systems, taxonomy of expert systems. Knowledge representation: first order logic, production systems, semantic nets, frames. Uncertainty in expert systems, one-valued approaches: probability theory, systems using Bayes' rule, and systems using certainty theory; two-valued approaches: systems using Dempster-Shafer theory and system INFERNO; set-valued approaches: systems using fuzzy set theory and systems using rough set theory. Prerequisite: EECS 560 or consent of instructor. LEC.

The class is not offered for the Spring 2019 semester.

EECS 649 Introduction to Artificial Intelligence
General concepts, search procedures, two-person games, predicate calculus and automated theorem proving, nonmonotonic logic, probabilistic reasoning, rule based systems, semantic networks, frames, dynamic memory, planning, machine learning, natural language understanding, neural networks. Prerequisite: Corequisite: EECS 368. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Williams, Andrew
W 06:10-09:00 PM LEEP2 2415 - LAWRENCE
3 75465
EECS 662 Programming Languages
Formal definition of programming languages including specification of syntax and semantics. Simple statements including precedence, infix, prefix, and postfix notation. Global properties of algorithmic languages including scope of declaration, storage allocation, grouping of statements, binding time of constituents, subroutines, coroutines, and tasks. Run-time representation of program and data structures. Prerequisite: EECS 368 and EECS 388 and EECS 560. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Morris, John
TuTh 01:00-02:15 PM LEA 1136 - LAWRENCE
3 61234
EECS 672 Introduction to Computer Graphics
Foundations of 2D and 3D computer graphics. Structured graphics application programming. Basic 2D and 3D graphics algorithms (modeling and viewing transformations, clipping, projects, visible line/surface determination, basic empirical lighting, and shading models), and aliasing. Prerequisite: EECS 448. LEC.

The class is not offered for the Spring 2019 semester.

EECS 718 Graph Algorithms
This course introduces students to computational graph theory and various graph algorithms and their complexities. Algorithms and applications covered will include those related to graph searching, connectivity and distance in graphs, graph isomorphism, spanning trees, shortest paths, matching, flows in network, independent and dominating sets, coloring and covering, and Traveling Salesman and Postman problems. Prerequisite: EECS 560 or graduate standing with consent of instructor. LEC.

The class is not offered for the Spring 2019 semester.

EECS 741 Computer Vision
This course gives a hands-on introduction to the fundamentals of computer vision. Topics include: image formation, edge detection, image segmentation, line-drawing interpretation, shape from shading, texture analysis, stereo imaging, motion analysis, shape representation, object recognition. Prerequisite: EECS 672 or EECS 744. LEC.

The class is not offered for the Spring 2019 semester.

EECS 755 Software Modeling and Analysis
Modern techniques for modeling and analyzing software systems. Course coverage concentrates on pragmatic, formal modeling techniques that support predictive analysis. Topics include formal modeling, static analysis, and formal analysis using model checking and theorem proving systems. Prerequisite: EECS 368 or equivalent. LEC.

The class is not offered for the Spring 2019 semester.

EECS 764 Analysis of Algorithms
Models of computations and performance measures; asymptotic analysis of algorithms; basic design paradigms including divide-and-conquer, dynamic programming, backtracking, branch-and-bound, greedy method and heuristics; design and analysis of approximation algorithms; lower bound theory; polynomial transformation and the theory of NP-Completeness; additional topics may be selected from arithmetic complexity, graph algorithms, string matching, and other combinatorial problems. Prerequisite: EECS 660 or equivalent. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Zhong, Cuncong
TuTh 02:30-03:45 PM LEA 2111 - LAWRENCE
3 75478
EECS 773 Advanced Graphics
Advanced topics in graphics and graphics systems. Topics at the state of the art are typically selected from: photorealistic rendering; physically-based lighting models; ray tracing; radiosity; physically-based modeling and rendering; animation; general texture mapping techniques; point-based graphics; collaborative techniques; and others. Prerequisite: EECS 672 or permission of instructor. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Miller, James
MWF 01:00-01:50 PM LEA 2133 - LAWRENCE
3 75476
EECS 774 Geometric Modeling
Introduction to the representation, manipulation, and analysis of geometric models of objects. Implicit and parametric representations of curves and surfaces with an emphasis on parametric freeform curves and surfaces such as Bezier and Nonuniform Rational B-Splines (NURBS). Curve and surface design and rendering techniques. Introduction to solid modeling: representations and base algorithms. Projects in C/C++ using OpenGL. Prerequisite: EECS 672 or permission of instructor. LEC.

The class is not offered for the Spring 2019 semester.

EECS 775 Visualization
Data representations, algorithms, and rendering techniques typically used in Visualization applications. The emphasis is on Scientific Visualization and generally includes topics such as contouring and volumetric rendering for scalar fields, glyph and stream (integral methods) for vector fields, and time animations. Multidimensional, multivariate (MDMV) visualization techniques; scattered data interpolation; perceptual issues. Prerequisite: General knowledge of 3D graphics programming or instructor's permission. LEC.

The class is not offered for the Spring 2019 semester.

EECS 830 Advanced Artificial Intelligence
A detailed examination of computer programs and techniques that manifest intelligent behavior, with examples drawn from current literature. The nature of intelligence and intelligent behavior. Development of, improvement to, extension of, and generalization from artificially intelligent systems, such as theorem-provers, pattern recognizers, language analyzers, problem-solvers, question answerers, decision-makers, planners, and learners. Prerequisite: Graduate standing in the EECS department or Cognitive Science or permission of the instructor. LEC.

The class is not offered for the Spring 2019 semester.

EECS 839 Mining Special Data
Problems associated with mining incomplete and numerical data. The MLEM2 algorithm for rule induction directly from incomplete and numerical data. Association analysis and the Apriori algorithm. KNN and other statistical methods. Mining financial data sets. Problems associated with imbalanced data sets and temporal data. Mining medical and biological data sets. Induction of rule generations. Validation of data mining: sensitivity, specificity, and ROC analysis. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Grzymala-Busse, Jerzy
TuTh 11:00-12:15 PM LEA 2133 - LAWRENCE
3 73453
EECS 940 Theoretic Foundation of Data Science
A review of statistical and mathematical principles that are utilized in data mining and machine learning research. Covered topics include asymptotic analysis of parameter estimation, sufficient statistics, model selection, information geometry, function approximation and Hilbert spaces. Prerequisite: EECS 738, EECS 837, EECS 844 or equivalent. LEC.

The class is not offered for the Spring 2019 semester.

MATH 727 Probability Theory
A mathematical introduction to premeasure-theoretic probability. Topics include probability spaces, conditional probabilities and independent events, random variables and probability distributions, special discrete and continuous distributions with emphasis on parametric families used in applications, the distribution problem for functions of random variables, sequences of independent random variables, laws of large numbers, and the central limit theorem. Prerequisite: MATH 223 and MATH 290, or equivalent. LEC.

The class is not offered for the Spring 2019 semester.

MATH 728 Statistical Theory
Theory of point estimation and hypothesis testing with applications. Confidence region methodologies and relations to estimation and testing. Prerequisite: MATH 727 or equivalent. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Soo, Terry
TuTh 02:30-03:45 PM SNOW 156 - LAWRENCE
3 65246

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