Simply put, machine learning is the part of artificial intelligence that actually works. Machine learning deals with studying and building systems that can learn from data or past experiences to solve problems. It consists of a set of methods that can automatically detect patterns in data, and then use these detected patterns to predict future data, or to perform decision making under uncertainty.
Its applications include robots learning to better navigate based on experience gained by roaming their environments; medical systems that learn to predict therapies that work best for specific diseases based on existing health records; natural language processing systems that learn your speaking patterns while listening to you; and a variety of applications in which systems learn how to represent and recognize objects. It is also heavily applied in sketch recognition systems for engineering communication, outlier detection in measurements, pattern detection in engineering data, damage detection and classification, predictive modeling, recommender systems, and so on.
This graduate course deals with the mathematical modeling, computer representations and algorithms for manipulating one, two and three-dimensional solid objects on a computer; It focuses on the basic concepts of solid and geometric modeling from geometry and topology, and uses these concepts to develop computational techniques for creating, editing, rendering, analyzing and computing with models of physical objects, mechanical parts, assembly and processes.
Typical Topics include geometric and topological representation of three dimensional objects; computer representations for curves, surface and solids; geometric algorithms and operations on curves, surfaces, and solids; the study and practical use of various representation schemes in solid modeling and commercial systems, as well as examples of engineering problems that are formulated and solved using geometric modeling methods; and advanced topics.
This undergraduate course provides an introduction to geometric modeling and Computer-Aided Design for engineering applications. It consists of both lectures (discussing fundamental concepts in geometric modeling and their applications to Computer Aided Design and Manufacturing), and computer lab work with Unigraphics NX.
Typical topics include geometric modeling concepts and systems; construction, analysis, and interrogation of models; points, vectors, coordinate systems, views, and transformations; parametric and implicit curves and surfaces; sketches, dimensions, and constraints; solids, features, and parametric design; assemblies and fits; drawings and annotations geometric dimensioning and tolerancing (GD&T), topology optimization.
The course covers the fundamentals of kinematics of machines and mechanisms as well as some of their important applications in the analysis and design of machines.