3: Supervised vs Unsupervised Learning

When discussing machine learning development approaches, data scientists often need to ask themselves does this use case apply best for supervised or unsupervised learning? In this episode, we break down the strengths and weaknesses of each approach and discuss various use cases to which each one best applies. Melody explores the notion that supervised learning works much like our education system: there's a teacher "supervising" the learning process. Unsupervised learning, on the other hand, has no correct answers and no teacher. Algorithms are simply fed unlabeled data and left to structure the data in some new, interesting way. Melody, Nikhil, and Saurabh dive into each approach and cite exciting business use cases including autonomous vehicles, Speech2Face, and accelerating ecological research in Serengeti National Park. https://content.alegion.com/podcast (https://alegion.com/)

Om Podcasten

No BiAS is a podcast about the emerging and ever-shifting terrain of artificial intelligence and machine learning. Each episode your host, Melody Travers, University of Potsdam, gets to pick the very big brains of machine learning researchers Nikhil Kumar, Carnegie Mellon University, and Saurabh Bagalkar, Arizona State University, and hear their different perspectives on the frontier of AI technology.