General Information

Objective: Study of the main deep learning methods and their applications.

Syllabus: Machine learning basics; Deep Feedforward Networks; Convolutional Neural Networks (CNN), Recurrent Neural Networks; Segmentation and Object Recognition; Frameworks for deep learning and Practical Aspects; Applications of deep learning models for real-world problems.

Duration: 60 hours (17 weeks).

Time: Tuesdays (18:40 - 21:10) - synchronous (SYN) and asynchronous (ASYN) classes.

Grade: Assignments (50%) and Final Project (50%).

Lecturer: André Eugenio Lazzaretti and Heitor Silvério Lopes.

Collaborators (PhD students): Andrei Inácio, Matheus Gutoski, Anderson Brilhador, and Clayton Kossoski.