Deep Learning for AI and Computer Vision
Deep Learning for AI and Computer Vision
July 19-23, 2021 | Live virtual short course

Explore the latest developments in vision AI, with a focus on advanced deep learning methods. In this dynamic, five-day course, you will learn to leverage the next generation of machine learning tools to build innovative applications — and integrate these advancements into your existing products and services.

Registration deadline: July 8, 2021

Member DiscountAlliances members are eligible for a discount for this program. Please log in to view discount instructions.
Polina Golland
CSAIL article
Golland named 2021 AIMBE Fellow

MIT professor Polina Golland has been named a fellow of the American Institute for Medical and Biological Engineering (AIMBE) for her outstanding contributions to the development of novel techniques for biomedical image analysis and understanding. Golland is joining a group of the top two percent of medical and biological engineers in the country. 

Torralba AAAI
MIT news article
Professor Antonio Torralba Elected 2021 AAAI Fellow

Antonio Torralba, faculty head of Artificial Intelligence and Decision Making within the Department of Electrical Engineering and Computer Science (EECS) and the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science, has been selected as a 2021 Fellow by the Association for the Advancement of Artificial Intelligence (AAAI).

CSAIL MATch software
CSAIL article
Automating Material-matching for Movies

A team led by researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed an approach that they say can make texturing even less tedious, to the point where you can snap a pic of something you see in a store, and then go recreate the material on your home laptop

neutral networks mindstate
CSAIL article
Neural Networks that Imagine Others’ States of Mind

In a new paper, a team led by MIT computer scientists trained a neural network to learn NASCAR-style driving maneuvers purely from looking at a sequence of images taken from a two-person racing game. The network begins without knowing anything about cars, roads, or driving - and yet ultimately becomes able to do complex moves like overtaking an opponent on a turn and even forcing other cars off the road. 

ML heart failure
MIT news article
Anticipating Heart Failure with Machine Learning

A group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

Subscribe to Graphic & Vision