Regli, William C. (Drexel University) | Kopena, Joseph B. (Drexel University) | Grauer, Michael (Drexel University) | Simpson, Timothy W. (Penn State University) | Stone, Robert B. (Oregon State University) | Lewis, Kemper (University at Buffalo - SUNY) | Bohm, Matt R. (Oregon State University) | Wilkie, David (Drexel University) | Piecyk, Martin (Drexel University) | Osecki, Jordan (Drexel University)
This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop Digital Engineering Archives. This work is part of an ongoing, multiuniversity, effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, workflows and processes. With these formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability.
Referring to an intelligent system, artificial intelligence seeks to recreate the human brain and provide a complex but efficient technology that will innovate the way technological industries work. In recent years, artificial intelligence has seen a big development, with more self-aware robots that are increasingly more capable of performing difficult tasks. With the evolution of the Internet of Things and the rise of automation, artificial intelligence will play a growing part in all processes of design and manufacturing involved in a wide range of engineering industries. The Internet of Things is allowing for an interconnected world, where devices connect everyone from everywhere. This connection allows for engineers from all over the world to collaborate and minimise errors in projects.
Entering college soon and since the past year and a half I've been studying ML pretty rigorously. So far I've loved every day of it and as of right now I like the idea of being able to develop my own machine learning algorithms once I graduate for a living. My advisor wants me to pick a degree to focus on but I have no idea which of the 4 I listed above will give me the amount of value for developing ML algorithms. I assume all 4 will pretty helpful and I originally planned on perusing either CS or Statistics, however, I've heard the math covered in Electrical Engineering is very applicable for ML and heard Jeremy Howard (creator of AI) say people with a classical Statistics background are actually the one's who have the hardest time of all his students picking up deep learning... i have no idea which one to choose really:\ If anyone here who's already graduated could give me some advice, I'd really appreciate it.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for? So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up.