Artificial intelligence school training courses in Boston, MA (public, in-house or online)

Artificial intelligence (AI) is the study of computer systems that appear to behave with some degree of human intelligence. This survey course covers major background and state-of-the-art topics in AI: game playing, natural language understanding, theorem proving, computer vision, genetic algorithms, and neural networks. Other topics in machine learning, planning, and expert systems are covered if time permits. The LISP programming language is introduced and used for some programming assignments...

Full archival course description>>

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...

Full archival course description>>

An introduction to the field of Computer Vision, focusing on the underlying algorithmic, geometric and optic issues. The course starts with a brief overview of basic image processing topics (convolution, smoothing, edge detection). It then proceeds on various image analysis topics: binary images, moments-based shape analysis, Hough transform, image formation, depth and shape recovery, photometry, motion, classification, special topics.