Artificial intelligence school training courses in Austin, TX (public, in-house or online)

This course focuses on design, development and implementation of effective systems for meeting information needs of management decision-makers. The course explains both model-based and data-based decision support systems and their use by managers in functional areas. Spreadsheets and applied artificial intelligence models, such as artificial neural network, and/or rule-based expert systems software may be used to introduce the decision-support process

This course focuses on concepts needed for analyzing, designing and applying effective systems for meeting management’s information needs for tactical short-term, and strategic long-term, planning and decision-making. The course covers various models of business intelligence and decision support systems, based largely on database design in support of data warehousing and data mining concepts. Topics include data modeling for the data warehouse and data mart, and application of OLAP and arti...

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The student will learn the terminology and methods used in a variety of artificial intelligence (AI) areas. These topics will be covered: history of artificial intelligence, search techniques, knowledge representation. In addition, one or more of these topics will be covered: expert systems, uncertainty, case-based reasoning, neural networks, vision, robotics. The student may use various AI tools, Lisp, and/or Prolog for AI projects.

An introduction to the large and diverse field of artificial intelligence. Topics include: problem-solving by search and constraint satisfaction; alpha-beta search for two-player games; and logic and knowledge representation, planning, learning, decision theory, statistical learning, and computer vision.

Introduction and general pattern recognition concerns and statistical pattern recognition: introduction to statistical pattern recognition, supervised learning (training) using parametric and nonparametric approaches, parametric estimation and supervised learning, maximum likelihood (ML) estimation, the Bayesian parameter estimation approach, supervised learning using nonparametric approaches, Parzen windows, nonparametric estimation, unsupervised learning and clustering, and formulation of unsu...

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