Prerequisite: 6.034 or 6.036, or permission of instructor
Instructor: Prof. Hal Abelson (email@example.com)
Schedule: MW2-3:30, room 36-112
This subject counts as a department laboratory subject.
This course prepares students to carry out projects that use deep learning. In the first part of the course, we’ll survey basic techniques, including convolutional neural networks, recurrent neural networks, generative adversarial networks, and embedding. For each technique, we’ll begin by studying the fundamental concepts, explore open-ended demo applications that use the tool and carry out programming assignments to modify and extend the demo. For the second part of the course, students will work in teams to design and implement original projects that use these techniques. Each project team will have a mentor who is a machine learning expert from industry. We’ll also discuss some policy and social related issues in deep learning, such as algorithmic fairness and interpretability.
6.S198 counts as a course 6 advanced departmental lab (DLAB2).
Enrollment is limited, permission of instructor required. To apply for permission to enroll in 6.S198 for the spring, you fill out the web form at: https://mit.edu/6.s198.