Fall 2017
This is an introductory graduate level course in critical thinking about formal methods for design in mechanical engineering. Course participants gain background in these methods and the creative potential each offers to designers. Participants ... will formulate, present, and discuss their own opinions on the value and appropriate use of design materials for mechanical engineering.
Prerequisites: Graduate standing or permission of instructor.

In the course of engineering design, project management, and other functions, engineers have to make decisions, almost always under time and budget constraints. Managing risk requires making decisions in the presence of uncertainty. This course ... will cover material on individual decision making, group decision making, and organizations of decision-makers. The course will present techniques for making better decisions, for understanding how decisions are related to each other, and for managing risk.
Also offered as: ENRE 671. Credit only granted for: ENME 808X, ENRE 671 or ENME 607. Formerly: ENME 808X.

Fall 2017 Tu 4:00pm - 6:40pm Shapour Azarm
Overview of applied single- and multi-objective optimization and decision making concepts and techniques with applications in engineering design and/or manufacturing problems. Topics include formulation examples, concepts, optimality conditions, ... unconstrained/constrained methods, and post-optimality sensitivity analysis. Students are expected to work on a semester-long real-world multi-objective engineering project.
Prerequisite: Graduate standing or permission of instructor.

Machine learning is a rapidly growing field at the intersection of computer science and statistics that is concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations ... to speech recognition in cell phones. The goal of this course is to provide a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important. Through a variety of lecture examples and programming projects, students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.
Credit only granted for: ENME 743 OR ENME 808E. Formerly: ENME 808E.

Fall 2017 Class time/details on ELMS Ryan Sochol
Develop a comprehensive understanding of fundamental additive manufacturing-alternatively, "three-dimensional (3D) printing-approaches, including extrusion-based deposition, stereolithography, powder bed-based melting, and inkjet-based deposition. ... Cultivate a "design-for-additive manufacturing" skill set for combining computer-aided design (CAD) and computer-aided manufacturing (CAM) methodologies to produce successful 3D prints. Fabricate 3D mechanical objects using a variety of 3D printing technologies on campus. Execute a design project that demonstrates how additive manufacturing technologies can overcome critical limitations of traditional manufacturing processes.
Prerequisite: ENME272 and ENME331; or students who have taken courses with comparable content may contact the department. Also offered as: ENME416. Credit only granted for: ENME 416 OR ENME 744.

Fall 2017
Machine learning is a rapidly growing field at the intersection of computer science and statistics that is concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations ... to speech recognition in cell phones. The goal of this course is to provide a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important. Through a variety of lecture examples and programming projects, students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

Formulate and quantitatively state the mechanical/physical responses of structural components and configurations subjected to loads, temperature, pre-strains etc. The two methods of anlysis employed are the mechanics of materials approach ... and the theory of elasticity approach. Analysis and design of components of structural/machine systems as experienced in aeronautical, civil, mechanical and nuclear engineering.